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Putting facts and observations together to arrive at conclusions is a", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 485, + 505, + 497 + ], + "spans": [ + { + "bbox": [ + 105, + 485, + 505, + 497 + ], + "score": 1.0, + "content": "central necessary ability as we work to move neural networks beyond their current great success with", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 495, + 505, + 508 + ], + "spans": [ + { + "bbox": [ + 105, + 495, + 505, + 508 + ], + "score": 1.0, + "content": "sensory perception tasks (LeCun et al., 1998; Krizhevsky et al., 2012) towards displaying Artificial", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 506, + 192, + 520 + ], + "spans": [ + { + "bbox": [ + 105, + 506, + 192, + 520 + ], + "score": 1.0, + "content": "General Intelligence.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30, + "bbox_fs": [ + 105, + 462, + 506, + 520 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 523, + 375, + 698 + ], + "lines": [ + { + "bbox": [ + 106, + 523, + 376, + 535 + ], + "spans": [ + { + "bbox": [ + 106, + 523, + 376, + 535 + ], + "score": 1.0, + "content": "Concretely, we develop a novel model that we apply to the CLEVR", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 533, + 376, + 547 + ], + "spans": [ + { + "bbox": [ + 106, + 533, + 376, + 547 + ], + "score": 1.0, + "content": "dataset (Johnson et al., 2016) for visual question answering (VQA).", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 545, + 376, + 558 + ], + "spans": [ + { + "bbox": [ + 106, + 545, + 376, + 558 + ], + "score": 1.0, + "content": "VQA (Antol et al., 2015; Gupta, 2017) is a challenging multimodal", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 556, + 376, + 569 + ], + "spans": [ + { + "bbox": [ + 106, + 556, + 376, + 569 + ], + "score": 1.0, + "content": "task that requires responding to natural language questions about", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 567, + 376, + 580 + ], + "spans": [ + { + "bbox": [ + 106, + 567, + 376, + 580 + ], + "score": 1.0, + "content": "images. 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Notably, each in-", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "stance in CLEVR is also accompanied by a tree-structured functional program that was both used to", + "type": "text" + } + ], + "index": 57 + }, + { + "bbox": [ + 106, + 710, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 106, + 710, + 505, + 722 + ], + "score": 1.0, + "content": "construct the question and reflects its reasoning procedure – a series of predefined operations – that", + "type": "text" + } + ], + "index": 58 + }, + { + "bbox": [ + 106, + 721, + 264, + 733 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 264, + 733 + ], + "score": 1.0, + "content": "can be composed together to answer it.", + "type": "text" + } + ], + "index": 59 + } + ], + "index": 40.5, + "bbox_fs": [ + 105, + 523, + 377, + 700 + ] + }, + { + "type": "image", + "bbox": [ + 387, + 537, + 501, + 613 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 387, + 537, + 501, + 613 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 387, + 537, + 501, + 613 + ], + "spans": [ + { + "bbox": [ + 387, + 537, + 501, + 613 + ], + "score": 0.962, + "type": "image", + "image_path": "674dcd95919d901cb7279f9b0552761c7620c6d163165031e5b1cd19087e50a7.jpg" + } + ] + } + ], + "index": 49.5, + "virtual_lines": [ + { + "bbox": [ + 387, + 537, + 501, + 575.0 + ], + "spans": [], + "index": 49 + }, + { + "bbox": [ + 387, + 575.0, + 501, + 613.0 + ], + "spans": [], + "index": 50 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 383, + 622, + 504, + 687 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 383, + 621, + 504, + 633 + ], + "spans": [ + { + "bbox": [ + 383, + 621, + 504, + 633 + ], + "score": 1.0, + "content": "Figure 1: A sample im-", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 382, + 632, + 506, + 645 + ], + "spans": [ + { + "bbox": [ + 382, + 632, + 506, + 645 + ], + "score": 1.0, + "content": "age from the CLEVR dataset,", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 383, + 643, + 506, + 655 + ], + "spans": [ + { + "bbox": [ + 383, + 643, + 506, + 655 + ], + "score": 1.0, + "content": "with a question: “There is a", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 382, + 655, + 505, + 666 + ], + "spans": [ + { + "bbox": [ + 382, + 655, + 505, + 666 + ], + "score": 1.0, + "content": "purple cube behind a metal", + "type": "text" + } + ], + "index": 54 + }, + { + "bbox": [ + 383, + 666, + 506, + 677 + ], + "spans": [ + { + "bbox": [ + 383, + 666, + 506, + 677 + ], + "score": 1.0, + "content": "object left to a large ball; what", + "type": "text" + } + ], + "index": 55 + }, + { + "bbox": [ + 383, + 676, + 444, + 687 + ], + "spans": [ + { + "bbox": [ + 383, + 676, + 444, + 687 + ], + "score": 1.0, + "content": "material is it?”", + "type": "text" + } + ], + "index": 56 + } + ], + "index": 53.5 + } + ], + "index": 51.5 + }, + { + "type": "text", + "bbox": [ + 108, + 700, + 504, + 731 + ], + "lines": [], + "index": 58, + "bbox_fs": [ + 106, + 699, + 505, + 733 + ], + "lines_deleted": true + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 192 + ], + "lines": [ + { + "bbox": [ + 105, + 82, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 82, + 505, + 95 + ], + "score": 1.0, + "content": "Most neural networks are essentially very large correlation engines that will hone in on any statis-", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "score": 1.0, + "content": "tical, potentially spurious pattern that allows them to model the observed data more accurately. In", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 105, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 105, + 505, + 117 + ], + "score": 1.0, + "content": "contrast, we seek to create a model structure that requires combining sound inference steps to solve", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "score": 1.0, + "content": "a problem instance. At the other extreme, some approaches adopt symbolic structures that resemble", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "score": 1.0, + "content": "the expression trees of programming languages to perform reasoning (Andreas et al., 2016b; Hu", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "spans": [ + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "score": 1.0, + "content": "et al., 2017). In particular, some approaches to CLEVR use the supplied functional programs for", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "spans": [ + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "score": 1.0, + "content": "supervised or semi-supervised training (Andreas et al., 2016a; Johnson et al., 2017). Not only do we", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 158, + 505, + 172 + ], + "spans": [ + { + "bbox": [ + 105, + 158, + 505, + 172 + ], + "score": 1.0, + "content": "wish to avoid using such supervision in our work, but we in general suspect that the rigidity of these", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 170, + 506, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 170, + 506, + 183 + ], + "score": 1.0, + "content": "structures and the use of an inventory of operation-specific neural modules undermines robustness", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 181, + 471, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 181, + 471, + 194 + ], + "score": 1.0, + "content": "and generalization, and at any rate requires more complex reinforcement learning methods.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 4.5 + }, + { + "type": "text", + "bbox": [ + 107, + 198, + 505, + 396 + ], + "lines": [ + { + "bbox": [ + 106, + 197, + 505, + 211 + ], + "spans": [ + { + "bbox": [ + 106, + 197, + 505, + 211 + ], + "score": 1.0, + "content": "To address these weaknesses, while still seeking to use a sound and transparent underlying reasoning", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 209, + 506, + 222 + ], + "spans": [ + { + "bbox": [ + 105, + 209, + 506, + 222 + ], + "score": 1.0, + "content": "process, we propose Compositional Attention Networks, a novel, fully differentiable, non-modular", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 220, + 506, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 506, + 233 + ], + "score": 1.0, + "content": "architecture for reasoning tasks. Our model is a straightforward recurrent neural network with at-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 231, + 504, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 231, + 504, + 243 + ], + "score": 1.0, + "content": "tention; the novelty lies in the use of a new Memory, Attention and Composition (MAC) cell. The", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 241, + 506, + 255 + ], + "spans": [ + { + "bbox": [ + 105, + 241, + 506, + 255 + ], + "score": 1.0, + "content": "constrained and deliberate design of the MAC cell was developed as a kind of strong structural", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 254, + 505, + 265 + ], + "spans": [ + { + "bbox": [ + 106, + 254, + 505, + 265 + ], + "score": 1.0, + "content": "prior that encourages the network to solve problems by stringing together a sequence of transparent", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "score": 1.0, + "content": "reasoning steps. MAC cells are versatile but constrained neural units. They explicitly separate out", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 275, + 505, + 287 + ], + "spans": [ + { + "bbox": [ + 105, + 275, + 505, + 287 + ], + "score": 1.0, + "content": "memory from control, both represented recurrently. The unit contains three sub-units: The control", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 286, + 506, + 298 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 506, + 298 + ], + "score": 1.0, + "content": "unit updates the control representation based on outside instructions (for VQA, the question), learn-", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 297, + 506, + 309 + ], + "spans": [ + { + "bbox": [ + 105, + 297, + 506, + 309 + ], + "score": 1.0, + "content": "ing to successively attend to different parts of the instructions; the read unit gets information out of", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 307, + 506, + 321 + ], + "spans": [ + { + "bbox": [ + 105, + 307, + 506, + 321 + ], + "score": 1.0, + "content": "a knowledge base (for VQA, the image) based on the control signal and the previous memory; the", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 319, + 506, + 331 + ], + "spans": [ + { + "bbox": [ + 105, + 319, + 506, + 331 + ], + "score": 1.0, + "content": "write unit updates the memory based on soft self-attention to previous memories, controlled by the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 329, + 505, + 343 + ], + "spans": [ + { + "bbox": [ + 105, + 329, + 505, + 343 + ], + "score": 1.0, + "content": "retrieved information and the control signal. A universal MAC unit with a single set of parameters", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 340, + 505, + 353 + ], + "spans": [ + { + "bbox": [ + 105, + 340, + 505, + 353 + ], + "score": 1.0, + "content": "is used throughout the reasoning process, but its behavior can vary widely based on the context in", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 351, + 506, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 506, + 365 + ], + "score": 1.0, + "content": "which it is applied – the input to the control unit and the contents of the knowledge base. With atten-", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 362, + 505, + 375 + ], + "spans": [ + { + "bbox": [ + 105, + 362, + 505, + 375 + ], + "score": 1.0, + "content": "tion, our MAC network has the capacity to represent arbitrarily complex acyclic reasoning graphs in", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 373, + 506, + 387 + ], + "spans": [ + { + "bbox": [ + 105, + 373, + 506, + 387 + ], + "score": 1.0, + "content": "a soft manner, while having physically sequential structure. The result is a continuous counterpart", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 384, + 423, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 423, + 397 + ], + "score": 1.0, + "content": "to module networks that can be trained end-to-end simply by backpropagation.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 18.5 + }, + { + "type": "text", + "bbox": [ + 107, + 402, + 505, + 566 + ], + "lines": [ + { + "bbox": [ + 106, + 400, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 400, + 505, + 415 + ], + "score": 1.0, + "content": "We test the behavior of our new network on CLEVR and its associated datasets. On the primary", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 316, + 425 + ], + "score": 1.0, + "content": "CLEVR reasoning task, we achieve an accuracy of", + "type": "text" + }, + { + "bbox": [ + 316, + 412, + 343, + 423 + ], + "score": 0.87, + "content": "9 8 . 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 412, + 505, + 425 + ], + "score": 1.0, + "content": ", halving the error rate compared to the", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 423, + 505, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 505, + 436 + ], + "score": 1.0, + "content": "previous state-of-the-art FiLM model (Perez et al., 2017). In particular, we show that our architec-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 434, + 505, + 448 + ], + "spans": [ + { + "bbox": [ + 105, + 434, + 505, + 448 + ], + "score": 1.0, + "content": "ture yields better performance on questions involving counting and aggregation. In supplementary", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 104, + 443, + 506, + 460 + ], + "spans": [ + { + "bbox": [ + 104, + 443, + 506, + 460 + ], + "score": 1.0, + "content": "studies, we show that the MAC network learns more quickly (both in terms of number of training", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 456, + 506, + 469 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 506, + 469 + ], + "score": 1.0, + "content": "epochs and training time) and more effectively from limited amounts of training data. Moreover, it", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 466, + 505, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 466, + 314, + 480 + ], + "score": 1.0, + "content": "also achieves a new state-of-the-art performance of", + "type": "text" + }, + { + "bbox": [ + 315, + 467, + 342, + 478 + ], + "score": 0.88, + "content": "8 2 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 342, + 466, + 505, + 480 + ], + "score": 1.0, + "content": "on the more varied and difficult human-", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "score": 1.0, + "content": "authored questions of the CLEVR-Humans dataset. The careful design of our cell encourages com-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 489, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 506, + 502 + ], + "score": 1.0, + "content": "positionality, versatility and transparency. We achieve these properties by defining attention-based", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 500, + 505, + 512 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 505, + 512 + ], + "score": 1.0, + "content": "interfaces that constrict the cell’s input and output spaces, and so constrain the interactions both", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 511, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 505, + 523 + ], + "score": 1.0, + "content": "between and inside cells in order to guide them towards simple reasoning behaviors. Although", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 522, + 505, + 534 + ], + "spans": [ + { + "bbox": [ + 105, + 522, + 505, + 534 + ], + "score": 1.0, + "content": "each cell’s functionality has only a limited range of possible continuous reasoning behaviors, when", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 533, + 505, + 545 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 505, + 545 + ], + "score": 1.0, + "content": "chained together in a MAC network, the whole system becomes expressive and powerful. In the", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 544, + 506, + 556 + ], + "spans": [ + { + "bbox": [ + 105, + 544, + 506, + 556 + ], + "score": 1.0, + "content": "future, we believe that the architecture will also prove beneficial for other multi-step reasoning and", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 554, + 458, + 568 + ], + "spans": [ + { + "bbox": [ + 105, + 554, + 458, + 568 + ], + "score": 1.0, + "content": "inference tasks, for instance in machine comprehension and textual question answering.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 35 + }, + { + "type": "title", + "bbox": [ + 108, + 591, + 211, + 604 + ], + "lines": [ + { + "bbox": [ + 105, + 590, + 213, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 590, + 213, + 606 + ], + "score": 1.0, + "content": "2 RELATED WORK", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 43 + }, + { + "type": "text", + "bbox": [ + 107, + 621, + 505, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 622, + 505, + 634 + ], + "spans": [ + { + "bbox": [ + 106, + 622, + 505, + 634 + ], + "score": 1.0, + "content": "There have been several prominent models that address the CLEVR task. By and large they can be", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 633, + 506, + 646 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 646 + ], + "score": 1.0, + "content": "partitioned into two groups: module networks, which in practice have all used the strong supervision", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "score": 1.0, + "content": "provided in the form of tree-structured functional programs that accompany each data instance, and", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 655, + 505, + 668 + ], + "spans": [ + { + "bbox": [ + 105, + 655, + 505, + 668 + ], + "score": 1.0, + "content": "large, relatively unstructured end-to-end differentiable networks that complement a fairly standard", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 665, + 506, + 678 + ], + "spans": [ + { + "bbox": [ + 105, + 665, + 506, + 678 + ], + "score": 1.0, + "content": "stack of CNNs with components that aid in performing reasoning tasks. In contrast to modular", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 677, + 506, + 689 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 689 + ], + "score": 1.0, + "content": "approaches (Andreas et al., 2016a;b; Hu et al., 2017; Johnson et al., 2017), our model does not", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 687, + 506, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 506, + 700 + ], + "score": 1.0, + "content": "require additional supervision and makes use of a single computational cell chained in sequence", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "(like an LSTM) rather than a collection of custom modules deployed in a rigid tree structure. In", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "score": 1.0, + "content": "contrast to augmented CNN approaches (Santoro et al., 2017; Perez et al., 2017), we suggest that our", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "score": 1.0, + "content": "approach provides an ability for relational reasoning with better generalization capacity and higher", + "type": "text" + } + ], + "index": 53 + } + ], + "index": 48.5 + } + ], + "page_idx": 1, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2018", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 751, + 309, + 760 + ], + "lines": [ + { + "bbox": [ + 301, + 750, + 310, + 763 + ], + "spans": [ + { + "bbox": [ + 301, + 750, + 310, + 763 + ], + "score": 1.0, + "content": "2", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 192 + ], + "lines": [ + { + "bbox": [ + 105, + 82, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 82, + 505, + 95 + ], + "score": 1.0, + "content": "Most neural networks are essentially very large correlation engines that will hone in on any statis-", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "score": 1.0, + "content": "tical, potentially spurious pattern that allows them to model the observed data more accurately. In", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 105, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 105, + 505, + 117 + ], + "score": 1.0, + "content": "contrast, we seek to create a model structure that requires combining sound inference steps to solve", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "score": 1.0, + "content": "a problem instance. At the other extreme, some approaches adopt symbolic structures that resemble", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "score": 1.0, + "content": "the expression trees of programming languages to perform reasoning (Andreas et al., 2016b; Hu", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "spans": [ + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "score": 1.0, + "content": "et al., 2017). In particular, some approaches to CLEVR use the supplied functional programs for", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "spans": [ + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "score": 1.0, + "content": "supervised or semi-supervised training (Andreas et al., 2016a; Johnson et al., 2017). Not only do we", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 158, + 505, + 172 + ], + "spans": [ + { + "bbox": [ + 105, + 158, + 505, + 172 + ], + "score": 1.0, + "content": "wish to avoid using such supervision in our work, but we in general suspect that the rigidity of these", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 170, + 506, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 170, + 506, + 183 + ], + "score": 1.0, + "content": "structures and the use of an inventory of operation-specific neural modules undermines robustness", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 181, + 471, + 194 + ], + "spans": [ + { + "bbox": [ + 106, + 181, + 471, + 194 + ], + "score": 1.0, + "content": "and generalization, and at any rate requires more complex reinforcement learning methods.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 4.5, + "bbox_fs": [ + 105, + 82, + 506, + 194 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 198, + 505, + 396 + ], + "lines": [ + { + "bbox": [ + 106, + 197, + 505, + 211 + ], + "spans": [ + { + "bbox": [ + 106, + 197, + 505, + 211 + ], + "score": 1.0, + "content": "To address these weaknesses, while still seeking to use a sound and transparent underlying reasoning", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 209, + 506, + 222 + ], + "spans": [ + { + "bbox": [ + 105, + 209, + 506, + 222 + ], + "score": 1.0, + "content": "process, we propose Compositional Attention Networks, a novel, fully differentiable, non-modular", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 220, + 506, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 506, + 233 + ], + "score": 1.0, + "content": "architecture for reasoning tasks. Our model is a straightforward recurrent neural network with at-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 231, + 504, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 231, + 504, + 243 + ], + "score": 1.0, + "content": "tention; the novelty lies in the use of a new Memory, Attention and Composition (MAC) cell. The", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 241, + 506, + 255 + ], + "spans": [ + { + "bbox": [ + 105, + 241, + 506, + 255 + ], + "score": 1.0, + "content": "constrained and deliberate design of the MAC cell was developed as a kind of strong structural", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 254, + 505, + 265 + ], + "spans": [ + { + "bbox": [ + 106, + 254, + 505, + 265 + ], + "score": 1.0, + "content": "prior that encourages the network to solve problems by stringing together a sequence of transparent", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "score": 1.0, + "content": "reasoning steps. MAC cells are versatile but constrained neural units. They explicitly separate out", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 275, + 505, + 287 + ], + "spans": [ + { + "bbox": [ + 105, + 275, + 505, + 287 + ], + "score": 1.0, + "content": "memory from control, both represented recurrently. The unit contains three sub-units: The control", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 286, + 506, + 298 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 506, + 298 + ], + "score": 1.0, + "content": "unit updates the control representation based on outside instructions (for VQA, the question), learn-", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 297, + 506, + 309 + ], + "spans": [ + { + "bbox": [ + 105, + 297, + 506, + 309 + ], + "score": 1.0, + "content": "ing to successively attend to different parts of the instructions; the read unit gets information out of", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 307, + 506, + 321 + ], + "spans": [ + { + "bbox": [ + 105, + 307, + 506, + 321 + ], + "score": 1.0, + "content": "a knowledge base (for VQA, the image) based on the control signal and the previous memory; the", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 319, + 506, + 331 + ], + "spans": [ + { + "bbox": [ + 105, + 319, + 506, + 331 + ], + "score": 1.0, + "content": "write unit updates the memory based on soft self-attention to previous memories, controlled by the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 329, + 505, + 343 + ], + "spans": [ + { + "bbox": [ + 105, + 329, + 505, + 343 + ], + "score": 1.0, + "content": "retrieved information and the control signal. A universal MAC unit with a single set of parameters", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 340, + 505, + 353 + ], + "spans": [ + { + "bbox": [ + 105, + 340, + 505, + 353 + ], + "score": 1.0, + "content": "is used throughout the reasoning process, but its behavior can vary widely based on the context in", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 351, + 506, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 506, + 365 + ], + "score": 1.0, + "content": "which it is applied – the input to the control unit and the contents of the knowledge base. With atten-", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 362, + 505, + 375 + ], + "spans": [ + { + "bbox": [ + 105, + 362, + 505, + 375 + ], + "score": 1.0, + "content": "tion, our MAC network has the capacity to represent arbitrarily complex acyclic reasoning graphs in", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 373, + 506, + 387 + ], + "spans": [ + { + "bbox": [ + 105, + 373, + 506, + 387 + ], + "score": 1.0, + "content": "a soft manner, while having physically sequential structure. The result is a continuous counterpart", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 384, + 423, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 423, + 397 + ], + "score": 1.0, + "content": "to module networks that can be trained end-to-end simply by backpropagation.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 18.5, + "bbox_fs": [ + 105, + 197, + 506, + 397 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 402, + 505, + 566 + ], + "lines": [ + { + "bbox": [ + 106, + 400, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 400, + 505, + 415 + ], + "score": 1.0, + "content": "We test the behavior of our new network on CLEVR and its associated datasets. On the primary", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 316, + 425 + ], + "score": 1.0, + "content": "CLEVR reasoning task, we achieve an accuracy of", + "type": "text" + }, + { + "bbox": [ + 316, + 412, + 343, + 423 + ], + "score": 0.87, + "content": "9 8 . 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 412, + 505, + 425 + ], + "score": 1.0, + "content": ", halving the error rate compared to the", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 423, + 505, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 505, + 436 + ], + "score": 1.0, + "content": "previous state-of-the-art FiLM model (Perez et al., 2017). In particular, we show that our architec-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 434, + 505, + 448 + ], + "spans": [ + { + "bbox": [ + 105, + 434, + 505, + 448 + ], + "score": 1.0, + "content": "ture yields better performance on questions involving counting and aggregation. In supplementary", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 104, + 443, + 506, + 460 + ], + "spans": [ + { + "bbox": [ + 104, + 443, + 506, + 460 + ], + "score": 1.0, + "content": "studies, we show that the MAC network learns more quickly (both in terms of number of training", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 456, + 506, + 469 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 506, + 469 + ], + "score": 1.0, + "content": "epochs and training time) and more effectively from limited amounts of training data. Moreover, it", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 466, + 505, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 466, + 314, + 480 + ], + "score": 1.0, + "content": "also achieves a new state-of-the-art performance of", + "type": "text" + }, + { + "bbox": [ + 315, + 467, + 342, + 478 + ], + "score": 0.88, + "content": "8 2 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 342, + 466, + 505, + 480 + ], + "score": 1.0, + "content": "on the more varied and difficult human-", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "score": 1.0, + "content": "authored questions of the CLEVR-Humans dataset. The careful design of our cell encourages com-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 489, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 506, + 502 + ], + "score": 1.0, + "content": "positionality, versatility and transparency. We achieve these properties by defining attention-based", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 500, + 505, + 512 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 505, + 512 + ], + "score": 1.0, + "content": "interfaces that constrict the cell’s input and output spaces, and so constrain the interactions both", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 511, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 505, + 523 + ], + "score": 1.0, + "content": "between and inside cells in order to guide them towards simple reasoning behaviors. Although", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 522, + 505, + 534 + ], + "spans": [ + { + "bbox": [ + 105, + 522, + 505, + 534 + ], + "score": 1.0, + "content": "each cell’s functionality has only a limited range of possible continuous reasoning behaviors, when", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 533, + 505, + 545 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 505, + 545 + ], + "score": 1.0, + "content": "chained together in a MAC network, the whole system becomes expressive and powerful. In the", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 544, + 506, + 556 + ], + "spans": [ + { + "bbox": [ + 105, + 544, + 506, + 556 + ], + "score": 1.0, + "content": "future, we believe that the architecture will also prove beneficial for other multi-step reasoning and", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 554, + 458, + 568 + ], + "spans": [ + { + "bbox": [ + 105, + 554, + 458, + 568 + ], + "score": 1.0, + "content": "inference tasks, for instance in machine comprehension and textual question answering.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 35, + "bbox_fs": [ + 104, + 400, + 506, + 568 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 591, + 211, + 604 + ], + "lines": [ + { + "bbox": [ + 105, + 590, + 213, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 590, + 213, + 606 + ], + "score": 1.0, + "content": "2 RELATED WORK", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 43 + }, + { + "type": "text", + "bbox": [ + 107, + 621, + 505, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 622, + 505, + 634 + ], + "spans": [ + { + "bbox": [ + 106, + 622, + 505, + 634 + ], + "score": 1.0, + "content": "There have been several prominent models that address the CLEVR task. By and large they can be", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 633, + 506, + 646 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 646 + ], + "score": 1.0, + "content": "partitioned into two groups: module networks, which in practice have all used the strong supervision", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 506, + 657 + ], + "score": 1.0, + "content": "provided in the form of tree-structured functional programs that accompany each data instance, and", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 655, + 505, + 668 + ], + "spans": [ + { + "bbox": [ + 105, + 655, + 505, + 668 + ], + "score": 1.0, + "content": "large, relatively unstructured end-to-end differentiable networks that complement a fairly standard", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 665, + 506, + 678 + ], + "spans": [ + { + "bbox": [ + 105, + 665, + 506, + 678 + ], + "score": 1.0, + "content": "stack of CNNs with components that aid in performing reasoning tasks. In contrast to modular", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 677, + 506, + 689 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 689 + ], + "score": 1.0, + "content": "approaches (Andreas et al., 2016a;b; Hu et al., 2017; Johnson et al., 2017), our model does not", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 687, + 506, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 506, + 700 + ], + "score": 1.0, + "content": "require additional supervision and makes use of a single computational cell chained in sequence", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "(like an LSTM) rather than a collection of custom modules deployed in a rigid tree structure. In", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 506, + 723 + ], + "score": 1.0, + "content": "contrast to augmented CNN approaches (Santoro et al., 2017; Perez et al., 2017), we suggest that our", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "score": 1.0, + "content": "approach provides an ability for relational reasoning with better generalization capacity and higher", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "score": 1.0, + "content": "computational efficiency. These approaches and other related work are discussed and contrasted in", + "type": "text", + "cross_page": true + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 93, + 327, + 106 + ], + "spans": [ + { + "bbox": [ + 106, + 93, + 327, + 106 + ], + "score": 1.0, + "content": "more detail in the supplementary material in section C.", + "type": "text", + "cross_page": true + } + ], + "index": 1 + } + ], + "index": 48.5, + "bbox_fs": [ + 105, + 622, + 506, + 734 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 82, + 504, + 105 + ], + "lines": [ + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "score": 1.0, + "content": "computational efficiency. These approaches and other related work are discussed and contrasted in", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 93, + 327, + 106 + ], + "spans": [ + { + "bbox": [ + 106, + 93, + 327, + 106 + ], + "score": 1.0, + "content": "more detail in the supplementary material in section C.", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "title", + "bbox": [ + 107, + 120, + 339, + 133 + ], + "lines": [ + { + "bbox": [ + 105, + 119, + 340, + 135 + ], + "spans": [ + { + "bbox": [ + 105, + 119, + 340, + 135 + ], + "score": 1.0, + "content": "3 COMPOSITIONAL ATTENTION NETWORKS", + "type": "text" + } + ], + "index": 2 + } + ], + "index": 2 + }, + { + "type": "text", + "bbox": [ + 107, + 145, + 505, + 178 + ], + "lines": [ + { + "bbox": [ + 106, + 145, + 505, + 157 + ], + "spans": [ + { + "bbox": [ + 106, + 145, + 505, + 157 + ], + "score": 1.0, + "content": "Compositional Attention Networks is an end-to-end architecture for question-answering tasks that", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 156, + 505, + 169 + ], + "spans": [ + { + "bbox": [ + 105, + 156, + 505, + 169 + ], + "score": 1.0, + "content": "sequentially performs an explicit reasoning process by stringing together small building blocks,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 165, + 399, + 181 + ], + "spans": [ + { + "bbox": [ + 105, + 165, + 399, + 181 + ], + "score": 1.0, + "content": "called MAC cells, each is responsible for performing one reasoning step.", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 4 + }, + { + "type": "text", + "bbox": [ + 107, + 183, + 504, + 228 + ], + "lines": [ + { + "bbox": [ + 106, + 184, + 505, + 196 + ], + "spans": [ + { + "bbox": [ + 106, + 184, + 505, + 196 + ], + "score": 1.0, + "content": "We now provide an overview of the model, and a detailed discussion of the MAC cell. The model", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 195, + 506, + 207 + ], + "spans": [ + { + "bbox": [ + 105, + 195, + 506, + 207 + ], + "score": 1.0, + "content": "is composed of three components: an Input unit, the core MAC network, and an output unit. 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However, it should be noted that while the", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 244, + 505, + 258 + ], + "spans": [ + { + "bbox": [ + 105, + 244, + 505, + 258 + ], + "score": 1.0, + "content": "input and output units are naturally domain-specific and should be designed to fit the task at hand,", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 255, + 505, + 269 + ], + "spans": [ + { + "bbox": [ + 105, + 255, + 505, + 269 + ], + "score": 1.0, + "content": "the MAC network has been designed to be generic and more broadly applicable, and may prove", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 267, + 505, + 280 + ], + "spans": [ + { + "bbox": [ + 106, + 267, + 505, + 280 + ], + "score": 1.0, + "content": "useful in contexts beyond those explored in the paper, such as machine comprehension or question", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 278, + 475, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 278, + 475, + 290 + ], + "score": 1.0, + "content": "answering over knowledge bases, which in our belief is a promising avenue for future work.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 12 + }, + { + "type": "title", + "bbox": [ + 108, + 302, + 204, + 313 + ], + "lines": [ + { + "bbox": [ + 105, + 300, + 205, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 205, + 315 + ], + "score": 1.0, + "content": "3.1 THE INPUT UNIT", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 15 + }, + { + "type": "text", + "bbox": [ + 107, + 322, + 505, + 367 + ], + "lines": [ + { + "bbox": [ + 105, + 322, + 506, + 335 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 506, + 335 + ], + "score": 1.0, + "content": "The input unit processes the raw inputs given to the system into distributed vector representations. It", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "score": 1.0, + "content": "receives a text question (or in general, a query), and an image (or in general, a Knowledge Base (KB))", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "score": 1.0, + "content": "and processes each of them with a matching sub-unit, for the query and the KB, here a biLSTM and", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 356, + 412, + 367 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 412, + 367 + ], + "score": 1.0, + "content": "a CNN. More details can be found in the supplementary material, section A.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 17.5 + }, + { + "type": "text", + "bbox": [ + 107, + 372, + 505, + 450 + ], + "lines": [ + { + "bbox": [ + 105, + 372, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 105, + 372, + 505, + 385 + ], + "score": 1.0, + "content": "At the end of this stage, we get from the query sub-unit a series of biLSTM output states, which we", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 224, + 396 + ], + "score": 1.0, + "content": "refer to as contextual words,", + "type": "text" + }, + { + "bbox": [ + 225, + 384, + 281, + 396 + ], + "score": 0.92, + "content": "[ c w _ { 1 } , . . . , c w _ { S } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 281, + 383, + 312, + 396 + ], + "score": 1.0, + "content": ", where", + "type": "text" + }, + { + "bbox": [ + 313, + 384, + 321, + 393 + ], + "score": 0.82, + "content": "S", + "type": "inline_equation" + }, + { + "bbox": [ + 321, + 383, + 505, + 396 + ], + "score": 1.0, + "content": "is the length of the question. 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We refer to", + "type": "text" + }, + { + "bbox": [ + 184, + 408, + 190, + 417 + ], + "score": 0.8, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 191, + 405, + 506, + 418 + ], + "score": 1.0, + "content": "as the question representation. Furthermore, we get from the Knowledge-Base", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 417, + 505, + 428 + ], + "spans": [ + { + "bbox": [ + 106, + 417, + 505, + 428 + ], + "score": 1.0, + "content": "sub-unit a static representation of the knowledge base. 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See figure 2.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 36.5 + }, + { + "type": "text", + "bbox": [ + 107, + 616, + 504, + 660 + ], + "lines": [ + { + "bbox": [ + 105, + 615, + 505, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 505, + 628 + ], + "score": 1.0, + "content": "The careful design and imposed interfaces that constrain the interaction between the units inside the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "score": 1.0, + "content": "MAC cell, as described below, serve as structural prior that limits the space of hypotheses it can", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 638, + 505, + 651 + ], + "spans": [ + { + "bbox": [ + 105, + 638, + 505, + 651 + ], + "score": 1.0, + "content": "learn, thereby guiding it towards acquiring the intended reasoning behaviors. 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However, it should be noted that while the", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 244, + 505, + 258 + ], + "spans": [ + { + "bbox": [ + 105, + 244, + 505, + 258 + ], + "score": 1.0, + "content": "input and output units are naturally domain-specific and should be designed to fit the task at hand,", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 255, + 505, + 269 + ], + "spans": [ + { + "bbox": [ + 105, + 255, + 505, + 269 + ], + "score": 1.0, + "content": "the MAC network has been designed to be generic and more broadly applicable, and may prove", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 267, + 505, + 280 + ], + "spans": [ + { + "bbox": [ + 106, + 267, + 505, + 280 + ], + "score": 1.0, + "content": "useful in contexts beyond those explored in the paper, such as machine comprehension or question", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 278, + 475, + 290 + ], + "spans": [ + { + "bbox": [ + 106, + 278, + 475, + 290 + ], + "score": 1.0, + "content": "answering over knowledge bases, which in our belief is a promising avenue for future work.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 12, + "bbox_fs": [ + 105, + 234, + 505, + 290 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 302, + 204, + 313 + ], + "lines": [ + { + "bbox": [ + 105, + 300, + 205, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 300, + 205, + 315 + ], + "score": 1.0, + "content": "3.1 THE INPUT UNIT", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 15 + }, + { + "type": "text", + "bbox": [ + 107, + 322, + 505, + 367 + ], + "lines": [ + { + "bbox": [ + 105, + 322, + 506, + 335 + ], + "spans": [ + { + "bbox": [ + 105, + 322, + 506, + 335 + ], + "score": 1.0, + "content": "The input unit processes the raw inputs given to the system into distributed vector representations. It", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "score": 1.0, + "content": "receives a text question (or in general, a query), and an image (or in general, a Knowledge Base (KB))", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "score": 1.0, + "content": "and processes each of them with a matching sub-unit, for the query and the KB, here a biLSTM and", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 356, + 412, + 367 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 412, + 367 + ], + "score": 1.0, + "content": "a CNN. More details can be found in the supplementary material, section A.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 17.5, + "bbox_fs": [ + 105, + 322, + 506, + 367 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 372, + 505, + 450 + ], + "lines": [ + { + "bbox": [ + 105, + 372, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 105, + 372, + 505, + 385 + ], + "score": 1.0, + "content": "At the end of this stage, we get from the query sub-unit a series of biLSTM output states, which we", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 224, + 396 + ], + "score": 1.0, + "content": "refer to as contextual words,", + "type": "text" + }, + { + "bbox": [ + 225, + 384, + 281, + 396 + ], + "score": 0.92, + "content": "[ c w _ { 1 } , . . . , c w _ { S } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 281, + 383, + 312, + 396 + ], + "score": 1.0, + "content": ", where", + "type": "text" + }, + { + "bbox": [ + 313, + 384, + 321, + 393 + ], + "score": 0.82, + "content": "S", + "type": "inline_equation" + }, + { + "bbox": [ + 321, + 383, + 505, + 396 + ], + "score": 1.0, + "content": "is the length of the question. In addition, we", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 394, + 506, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 122, + 407 + ], + "score": 1.0, + "content": "get", + "type": "text" + }, + { + "bbox": [ + 122, + 394, + 185, + 407 + ], + "score": 0.94, + "content": "q = [ \\overleftrightarrow { \\lambda \\omega _ { 1 } } , \\overrightarrow { c w _ { S } } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 186, + 394, + 506, + 407 + ], + "score": 1.0, + "content": ", the concatenation of the hidden states from the backward and forward LSTM", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 405, + 506, + 418 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 184, + 418 + ], + "score": 1.0, + "content": "passes. We refer to", + "type": "text" + }, + { + "bbox": [ + 184, + 408, + 190, + 417 + ], + "score": 0.8, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 191, + 405, + 506, + 418 + ], + "score": 1.0, + "content": "as the question representation. Furthermore, we get from the Knowledge-Base", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 417, + 505, + 428 + ], + "spans": [ + { + "bbox": [ + 106, + 417, + 505, + 428 + ], + "score": 1.0, + "content": "sub-unit a static representation of the knowledge base. For the case of VQA, it will be represented", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 427, + 505, + 440 + ], + "spans": [ + { + "bbox": [ + 105, + 427, + 201, + 440 + ], + "score": 1.0, + "content": "by a continuous matrix", + "type": "text" + }, + { + "bbox": [ + 201, + 428, + 226, + 438 + ], + "score": 0.91, + "content": "K B _ { V }", + "type": "inline_equation" + }, + { + "bbox": [ + 226, + 427, + 282, + 440 + ], + "score": 1.0, + "content": "of dimension", + "type": "text" + }, + { + "bbox": [ + 282, + 428, + 316, + 439 + ], + "score": 0.88, + "content": "H , W , d", + "type": "inline_equation" + }, + { + "bbox": [ + 316, + 427, + 346, + 440 + ], + "score": 1.0, + "content": ", where", + "type": "text" + }, + { + "bbox": [ + 346, + 428, + 405, + 438 + ], + "score": 0.91, + "content": "H = W = 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 405, + 427, + 505, + 440 + ], + "score": 1.0, + "content": "are the height and width", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 438, + 359, + 451 + ], + "spans": [ + { + "bbox": [ + 105, + 438, + 359, + 451 + ], + "score": 1.0, + "content": "of the transformed image, corresponding to each of its regions.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 23, + "bbox_fs": [ + 105, + 372, + 506, + 451 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 462, + 200, + 474 + ], + "lines": [ + { + "bbox": [ + 105, + 461, + 203, + 476 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 203, + 476 + ], + "score": 1.0, + "content": "3.2 THE MAC CELL", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 27 + }, + { + "type": "text", + "bbox": [ + 107, + 483, + 505, + 560 + ], + "lines": [ + { + "bbox": [ + 105, + 483, + 505, + 496 + ], + "spans": [ + { + "bbox": [ + 105, + 483, + 505, + 496 + ], + "score": 1.0, + "content": "The MAC network, which is the heart of our model, chains a sequence of small building blocks,", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 495, + 505, + 507 + ], + "spans": [ + { + "bbox": [ + 105, + 495, + 505, + 507 + ], + "score": 1.0, + "content": "called MAC cells, each responsible for performing one reasoning step. The model is provided access", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 505, + 505, + 519 + ], + "spans": [ + { + "bbox": [ + 105, + 505, + 505, + 519 + ], + "score": 1.0, + "content": "to a Knowledge Base (KB), which is, for the specific case of VQA, the given image, and then upon", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 517, + 505, + 529 + ], + "spans": [ + { + "bbox": [ + 105, + 517, + 378, + 529 + ], + "score": 1.0, + "content": "receiving a query, i.e. a question, the model iteratively focuses, in", + "type": "text" + }, + { + "bbox": [ + 379, + 518, + 385, + 528 + ], + "score": 0.78, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 386, + 517, + 505, + 529 + ], + "score": 1.0, + "content": "steps, on the query’s various", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 527, + 505, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 505, + 541 + ], + "score": 1.0, + "content": "parts, each reflects in turn the current reasoning step, which we term the control. 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Each has a clearly", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 599, + 462, + 611 + ], + "spans": [ + { + "bbox": [ + 106, + 599, + 462, + 611 + ], + "score": 1.0, + "content": "defined role and an interface through which it interacts with the other units. See figure 2.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 36.5, + "bbox_fs": [ + 105, + 566, + 506, + 611 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 616, + 504, + 660 + ], + "lines": [ + { + "bbox": [ + 105, + 615, + 505, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 505, + 628 + ], + "score": 1.0, + "content": "The careful design and imposed interfaces that constrain the interaction between the units inside the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "score": 1.0, + "content": "MAC cell, as described below, serve as structural prior that limits the space of hypotheses it can", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 638, + 505, + 651 + ], + "spans": [ + { + "bbox": [ + 105, + 638, + 505, + 651 + ], + "score": 1.0, + "content": "learn, thereby guiding it towards acquiring the intended reasoning behaviors. 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(2017), we allow the question to interact with the", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 106, + 677, + 505, + 689 + ], + "spans": [ + { + "bbox": [ + 106, + 677, + 505, + 689 + ], + "score": 1.0, + "content": "Knowledge Base – the image for the case of VQA, only through indirect means: by guiding the cell", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "score": 1.0, + "content": "to attend to different elements in the KB, as well as controlling its operation through gating mecha-", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 698, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 505, + 712 + ], + "score": 1.0, + "content": "nisms. Thus, in both cases, the interaction between these mediums, visual and textual, or knowledge", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 710, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 106, + 710, + 505, + 722 + ], + "score": 1.0, + "content": "and query, is mediated through probability distributions, either in the form of attention maps, or as", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 506, + 734 + ], + "score": 1.0, + "content": "gates, further detailed below. This stands in stark contrast to many common approaches that fuse the", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 106, + 251, + 505, + 265 + ], + "spans": [ + { + "bbox": [ + 106, + 251, + 505, + 265 + ], + "score": 1.0, + "content": "question and image together into the same vector space through linear combinations, multiplication,", + "type": "text", + "cross_page": true + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 262, + 505, + 275 + ], + "spans": [ + { + "bbox": [ + 105, + 262, + 505, + 275 + ], + "score": 1.0, + "content": "or concatenation. 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Blue shows the control flow and red shows the memory flow. See section 3.2 for details.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 8.5 + } + ], + "index": 6.0 + }, + { + "type": "text", + "bbox": [ + 108, + 252, + 504, + 285 + ], + "lines": [ + { + "bbox": [ + 106, + 251, + 505, + 265 + ], + "spans": [ + { + "bbox": [ + 106, + 251, + 505, + 265 + ], + "score": 1.0, + "content": "question and image together into the same vector space through linear combinations, multiplication,", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 262, + 505, + 275 + ], + "spans": [ + { + "bbox": [ + 105, + 262, + 505, + 275 + ], + "score": 1.0, + "content": "or concatenation. 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The memory", + "type": "text" + }, + { + "bbox": [ + 430, + 408, + 443, + 417 + ], + "score": 0.85, + "content": "m _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 444, + 406, + 506, + 419 + ], + "score": 1.0, + "content": "represents the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 416, + 506, + 431 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 506, + 431 + ], + "score": 1.0, + "content": "current context information deemed relevant to respond to the query, or answer the question.This is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "represented practically by a weighted average over elements from the KB, or for the case of VQA,", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 439, + 506, + 452 + ], + "spans": [ + { + "bbox": [ + 105, + 439, + 194, + 452 + ], + "score": 1.0, + "content": "regions in the image.", + "type": "text" + }, + { + "bbox": [ + 195, + 441, + 209, + 450 + ], + "score": 0.85, + "content": "m _ { 0 }", + "type": "inline_equation" + }, + { + "bbox": [ + 210, + 439, + 227, + 452 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 228, + 441, + 238, + 450 + ], + "score": 0.85, + "content": "c _ { 0 }", + "type": "inline_equation" + }, + { + "bbox": [ + 239, + 439, + 495, + 452 + ], + "score": 1.0, + "content": "are initialized each to a random vector parameter of dimension", + "type": "text" + }, + { + "bbox": [ + 495, + 439, + 501, + 449 + ], + "score": 0.73, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 502, + 439, + 506, + 452 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 449, + 506, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 449, + 506, + 462 + ], + "score": 1.0, + "content": "The memory and control states are passed from one cell to the next in a recurrent fashion, and used", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 461, + 484, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 484, + 474 + ], + "score": 1.0, + "content": "in a way reminiscent of Key-Value memory networks (Miller et al., 2016), as discussed below.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 24 + }, + { + "type": "title", + "bbox": [ + 108, + 484, + 228, + 496 + ], + "lines": [ + { + "bbox": [ + 105, + 482, + 230, + 497 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 230, + 497 + ], + "score": 1.0, + "content": "3.2.1 THE CONTROL UNIT", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 107, + 503, + 505, + 536 + ], + "lines": [ + { + "bbox": [ + 105, + 502, + 505, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 502, + 505, + 516 + ], + "score": 1.0, + "content": "The control unit determines the reasoning operation that should be applied at this step. It receives", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 514, + 505, + 527 + ], + "spans": [ + { + "bbox": [ + 105, + 514, + 195, + 527 + ], + "score": 1.0, + "content": "the contextual words", + "type": "text" + }, + { + "bbox": [ + 195, + 514, + 252, + 526 + ], + "score": 0.92, + "content": "[ c w _ { 1 } , . . . , c w _ { S } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 252, + 514, + 369, + 527 + ], + "score": 1.0, + "content": ", the question representation", + "type": "text" + }, + { + "bbox": [ + 370, + 516, + 376, + 525 + ], + "score": 0.76, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 376, + 514, + 505, + 527 + ], + "score": 1.0, + "content": ", and the control state from the", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 525, + 369, + 537 + ], + "spans": [ + { + "bbox": [ + 105, + 525, + 184, + 537 + ], + "score": 1.0, + "content": "previous MAC cell", + "type": "text" + }, + { + "bbox": [ + 185, + 527, + 204, + 537 + ], + "score": 0.88, + "content": "c _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 204, + 525, + 358, + 537 + ], + "score": 1.0, + "content": ", all of which are vectors of dimension", + "type": "text" + }, + { + "bbox": [ + 358, + 526, + 365, + 535 + ], + "score": 0.76, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 365, + 525, + 369, + 537 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 31 + }, + { + "type": "text", + "bbox": [ + 107, + 542, + 505, + 609 + ], + "lines": [ + { + "bbox": [ + 105, + 541, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 505, + 555 + ], + "score": 1.0, + "content": "We would like to allow our MAC cell to perform continuously varied and adaptive range of behav-", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 105, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "iors, as demanded by the question. 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Blue shows the control flow and red shows the memory flow. See section 3.2 for details.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 8.5 + } + ], + "index": 6.0 + }, + { + "type": "text", + "bbox": [ + 108, + 252, + 504, + 285 + ], + "lines": [], + "index": 11, + "bbox_fs": [ + 105, + 251, + 505, + 286 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 107, + 290, + 505, + 368 + ], + "lines": [ + { + "bbox": [ + 106, + 290, + 505, + 303 + ], + "spans": [ + { + "bbox": [ + 106, + 290, + 505, + 303 + ], + "score": 1.0, + "content": "The MAC cell has been designed to replace the discrete and predefined “modules” used in the", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 301, + 505, + 314 + ], + "spans": [ + { + "bbox": [ + 105, + 301, + 505, + 314 + ], + "score": 1.0, + "content": "modular approach (Andreas et al., 2016a;b; Hu et al., 2017; Johnson et al., 2017). Rather, we", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 312, + 505, + 325 + ], + "spans": [ + { + "bbox": [ + 105, + 312, + 505, + 325 + ], + "score": 1.0, + "content": "create one universal and versatile cell that is applied across all the reasoning steps, sharing both", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 324, + 505, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 505, + 336 + ], + "score": 1.0, + "content": "its architecture as well as its parameters, across all of its instantiations. In contrast to the discrete", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 334, + 505, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 505, + 347 + ], + "score": 1.0, + "content": "modules, each trained to specialize to some specific elementary reasoning task, the MAC cell is", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 505, + 357 + ], + "score": 1.0, + "content": "capable of demonstrating a continuous range of possible reasoning behaviors conditioned on the", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 356, + 427, + 369 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 427, + 369 + ], + "score": 1.0, + "content": "context in which it is applied – namely, the inputs it receives from the prior cell.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 16, + "bbox_fs": [ + 105, + 290, + 505, + 369 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 373, + 505, + 472 + ], + "lines": [ + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 145, + 385 + ], + "score": 1.0, + "content": "Each cell", + "type": "text" + }, + { + "bbox": [ + 146, + 373, + 176, + 384 + ], + "score": 0.91, + "content": "M A C _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 176, + 373, + 314, + 385 + ], + "score": 1.0, + "content": "maintains two dual states: control", + "type": "text" + }, + { + "bbox": [ + 315, + 375, + 323, + 384 + ], + "score": 0.85, + "content": "c _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 324, + 373, + 375, + 385 + ], + "score": 1.0, + "content": "and memory", + "type": "text" + }, + { + "bbox": [ + 376, + 375, + 389, + 384 + ], + "score": 0.85, + "content": "m _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 389, + 373, + 505, + 385 + ], + "score": 1.0, + "content": ", both are continuous vectors", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 384, + 504, + 396 + ], + "spans": [ + { + "bbox": [ + 106, + 384, + 161, + 396 + ], + "score": 1.0, + "content": "of dimension", + "type": "text" + }, + { + "bbox": [ + 162, + 385, + 168, + 394 + ], + "score": 0.7, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 168, + 384, + 221, + 396 + ], + "score": 1.0, + "content": ". 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The memory", + "type": "text" + }, + { + "bbox": [ + 430, + 408, + 443, + 417 + ], + "score": 0.85, + "content": "m _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 444, + 406, + 506, + 419 + ], + "score": 1.0, + "content": "represents the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 416, + 506, + 431 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 506, + 431 + ], + "score": 1.0, + "content": "current context information deemed relevant to respond to the query, or answer the question.This is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "represented practically by a weighted average over elements from the KB, or for the case of VQA,", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 439, + 506, + 452 + ], + "spans": [ + { + "bbox": [ + 105, + 439, + 194, + 452 + ], + "score": 1.0, + "content": "regions in the image.", + "type": "text" + }, + { + "bbox": [ + 195, + 441, + 209, + 450 + ], + "score": 0.85, + "content": "m _ { 0 }", + "type": "inline_equation" + }, + { + "bbox": [ + 210, + 439, + 227, + 452 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 228, + 441, + 238, + 450 + ], + "score": 0.85, + "content": "c _ { 0 }", + "type": "inline_equation" + }, + { + "bbox": [ + 239, + 439, + 495, + 452 + ], + "score": 1.0, + "content": "are initialized each to a random vector parameter of dimension", + "type": "text" + }, + { + "bbox": [ + 495, + 439, + 501, + 449 + ], + "score": 0.73, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 502, + 439, + 506, + 452 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 449, + 506, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 449, + 506, + 462 + ], + "score": 1.0, + "content": "The memory and control states are passed from one cell to the next in a recurrent fashion, and used", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 461, + 484, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 484, + 474 + ], + "score": 1.0, + "content": "in a way reminiscent of Key-Value memory networks (Miller et al., 2016), as discussed below.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 24, + "bbox_fs": [ + 105, + 373, + 506, + 474 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 484, + 228, + 496 + ], + "lines": [ + { + "bbox": [ + 105, + 482, + 230, + 497 + ], + "spans": [ + { + "bbox": [ + 105, + 482, + 230, + 497 + ], + "score": 1.0, + "content": "3.2.1 THE CONTROL UNIT", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 29 + }, + { + "type": "text", + "bbox": [ + 107, + 503, + 505, + 536 + ], + "lines": [ + { + "bbox": [ + 105, + 502, + 505, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 502, + 505, + 516 + ], + "score": 1.0, + "content": "The control unit determines the reasoning operation that should be applied at this step. 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This will allow the cell to adapt its behavior – the reason-", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "score": 1.0, + "content": "ing operation it performs – to the question it receives, instead of having a fixed set of predefined", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 596, + 500, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 500, + 610 + ], + "score": 1.0, + "content": "behaviours as is the case in competing approaches Andreas et al. (2016a;b); Johnson et al. 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See section 3.2.1 for details. 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1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 328, + 635, + 394, + 649 + ], + "score": 1.0, + "content": "is computed by:", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 37.5 + }, + { + "type": "interline_equation", + "bbox": [ + 250, + 662, + 361, + 678 + ], + "lines": [ + { + "bbox": [ + 250, + 662, + 361, + 678 + ], + "spans": [ + { + "bbox": [ + 250, + 662, + 361, + 678 + ], + "score": 0.91, + "content": "m _ { i - 1 } ^ { \\prime } = W ^ { d , d } \\cdot m _ { i - 1 } + b ^ { d }", + "type": "interline_equation", + "image_path": "92d034ab00623a6201883d47cd297723d599fe90d135f474e1198ca4a4622a1a.jpg" + } + ] + } + ], + "index": 39, + "virtual_lines": [ + { + "bbox": [ + 250, + 662, + 361, + 678 + ], + "spans": [], + "index": 39 + } + ] + }, + { + "type": "interline_equation", + "bbox": [ + 240, + 696, + 370, + 732 + ], + "lines": [ + { + "bbox": [ + 240, + 696, + 370, + 732 + ], + "spans": [ + { + "bbox": [ + 240, + 696, + 370, + 732 + ], + "score": 0.91, + "content": "\\begin{array} { r l } { \\boldsymbol { K } \\boldsymbol { B } _ { h , w } ^ { \\prime } = \\boldsymbol { W } ^ { d , d } \\cdot \\boldsymbol { K } \\boldsymbol { B } _ { h , w } + \\boldsymbol { b } ^ { d } } & { { } } \\\\ { \\big ( \\boldsymbol { I } _ { m - 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In order to achieve such capability, the read unit concatenates the", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "score": 1.0, + "content": "original KB elements to each corresponding memory-KB interaction, which are then projected back", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 334, + 259, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 117, + 347 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 117, + 335, + 123, + 344 + ], + "score": 0.79, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 124, + 334, + 259, + 347 + ], + "score": 1.0, + "content": "-dimensional space (equation 6a):", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 15, + "bbox_fs": [ + 105, + 268, + 505, + 347 + ] + }, + { + "type": "interline_equation", + "bbox": [ + 219, + 363, + 392, + 398 + ], + "lines": [ + { + "bbox": [ + 219, + 363, + 392, + 398 + ], + "spans": [ + { + "bbox": [ + 219, + 363, + 392, + 398 + ], + "score": 0.9, + "content": "\\begin{array} { c } { { I _ { m - K B } , ^ { \\prime } = W ^ { 2 d , d } \\left[ I _ { m - K B } , K B _ { h , w } \\right] + b ^ { d } } } \\\\ { { I _ { c m - K B } = c _ { i } \\circ \\left( I _ { m - K B } \\right) ^ { \\prime } } } \\end{array}", + "type": "interline_equation", + "image_path": "ada1fba84ad0bac03e8e9a8f8cc72cbc8bba767e7414ee8b9b65dab9dee93066.jpg" + } + ] + } + ], + "index": 19.5, + "virtual_lines": [ + { + "bbox": [ + 219, + 363, + 392, + 380.5 + ], + "spans": [], + "index": 19 + }, + { + "bbox": [ + 219, + 380.5, + 392, + 398.0 + ], + "spans": [], + "index": 20 + } + ] + }, + { + "type": "text", + "bbox": [ + 107, + 405, + 504, + 460 + ], + "lines": [ + { + "bbox": [ + 105, + 405, + 506, + 418 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 333, + 418 + ], + "score": 1.0, + "content": "At the second stage, the read unit compares the current", + "type": "text" + }, + { + "bbox": [ + 334, + 407, + 343, + 416 + ], + "score": 0.85, + "content": "c _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 343, + 405, + 506, + 418 + ], + "score": 1.0, + "content": "with these memory-KB interactions, in", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 416, + 505, + 428 + ], + "spans": [ + { + "bbox": [ + 106, + 416, + 505, + 428 + ], + "score": 1.0, + "content": "order to focus on the information that is relevant to the current reasoning operation that the MAC", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 425, + 506, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 425, + 506, + 441 + ], + "score": 1.0, + "content": "cell seeks to accomplish. The result is then passed to a softmax layer yielding an attention map", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 437, + 506, + 451 + ], + "spans": [ + { + "bbox": [ + 106, + 439, + 125, + 449 + ], + "score": 0.85, + "content": "m v _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 126, + 437, + 506, + 451 + ], + "score": 1.0, + "content": "over the KB, which is used in turn to retrieve the relevant information to perform the current", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 448, + 169, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 448, + 169, + 462 + ], + "score": 1.0, + "content": "reasoning step.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 23, + "bbox_fs": [ + 105, + 405, + 506, + 462 + ] + }, + { + "type": "interline_equation", + "bbox": [ + 224, + 478, + 387, + 532 + ], + "lines": [ + { + "bbox": [ + 224, + 478, + 387, + 532 + ], + "spans": [ + { + "bbox": [ + 224, + 478, + 387, + 532 + ], + "score": 0.91, + "content": "\\begin{array} { r } { m v _ { i } = \\operatorname { s o f t m a x } \\left( W ^ { d , d } \\cdot I _ { c m - K B } + b ^ { d } \\right) } \\\\ { m _ { n e w } = \\displaystyle \\sum _ { h , w = 1 , 1 } ^ { H , W } \\left( m v _ { i } \\right) _ { h , w } \\cdot K B _ { h , w } } \\end{array}", + "type": "interline_equation", + "image_path": "1af356ca707634550da691f9e1c5befb2540a27633c9abfa602dcd4cdf1d0ea5.jpg" + } + ] + } + ], + "index": 27.5, + "virtual_lines": [ + { + "bbox": [ + 224, + 478, + 387, + 491.5 + ], + "spans": [], + "index": 26 + }, + { + "bbox": [ + 224, + 491.5, + 387, + 505.0 + ], + "spans": [], + "index": 27 + }, + { + "bbox": [ + 224, + 505.0, + 387, + 518.5 + ], + "spans": [], + "index": 28 + }, + { + "bbox": [ + 224, + 518.5, + 387, + 532.0 + ], + "spans": [], + "index": 29 + } + ] + }, + { + "type": "text", + "bbox": [ + 105, + 541, + 504, + 563 + ], + "lines": [ + { + "bbox": [ + 105, + 538, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 358, + 555 + ], + "score": 1.0, + "content": "Finally, the read unit returns the newly retrieved information", + "type": "text" + }, + { + "bbox": [ + 358, + 543, + 383, + 552 + ], + "score": 0.89, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 383, + 538, + 505, + 555 + ], + "score": 1.0, + "content": ", along with an attention map", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 552, + 233, + 564 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 125, + 563 + ], + "score": 0.84, + "content": "m v _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 125, + 552, + 233, + 564 + ], + "score": 1.0, + "content": "over the Knowledge Base.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 30.5, + "bbox_fs": [ + 105, + 538, + 505, + 564 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 568, + 505, + 678 + ], + "lines": [ + { + "bbox": [ + 105, + 568, + 506, + 582 + ], + "spans": [ + { + "bbox": [ + 105, + 568, + 406, + 582 + ], + "score": 1.0, + "content": "To give an example of the read unit operation, assume a given question", + "type": "text" + }, + { + "bbox": [ + 406, + 571, + 413, + 580 + ], + "score": 0.76, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 413, + 568, + 506, + 582 + ], + "score": 1.0, + "content": "such as “What object", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 579, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 105, + 579, + 505, + 592 + ], + "score": 1.0, + "content": "is located left to the blue ball?”, whose associated answer is “cube”. 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However, in", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 622, + 506, + 636 + ], + "score": 1.0, + "content": "the second iteration, the control unit, after re-examining the question, may realize it should now", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 634, + 505, + 647 + ], + "spans": [ + { + "bbox": [ + 105, + 634, + 250, + 647 + ], + "score": 1.0, + "content": "look left, storing the word “left” in", + "type": "text" + }, + { + "bbox": [ + 250, + 636, + 260, + 645 + ], + "score": 0.84, + "content": "c _ { 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 260, + 634, + 385, + 647 + ], + "score": 1.0, + "content": ". 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Blue refers to control flow and red to memory flow. See", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 177, + 223, + 192 + ], + "spans": [ + { + "bbox": [ + 105, + 177, + 223, + 192 + ], + "score": 1.0, + "content": "section 3.2.3 for description.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 5.5 + } + ], + "index": 3.75 + }, + { + "type": "text", + "bbox": [ + 107, + 209, + 505, + 243 + ], + "lines": [ + { + "bbox": [ + 105, + 209, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 105, + 209, + 316, + 222 + ], + "score": 1.0, + "content": "reasoning process. It receives the last memory state", + "type": "text" + }, + { + "bbox": [ + 316, + 212, + 340, + 222 + ], + "score": 0.89, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 209, + 505, + 222 + ], + "score": 1.0, + "content": "from the previous MAC cell, along with", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 220, + 506, + 234 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 403, + 234 + ], + "score": 1.0, + "content": "the newly retrieved information from the read unit in the current iteration,", + "type": "text" + }, + { + "bbox": [ + 404, + 222, + 429, + 232 + ], + "score": 0.89, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 220, + 506, + 234 + ], + "score": 1.0, + "content": ". See figure 5 for a", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 230, + 145, + 245 + ], + "spans": [ + { + "bbox": [ + 105, + 230, + 145, + 245 + ], + "score": 1.0, + "content": "diagram.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 8 + }, + { + "type": "text", + "bbox": [ + 106, + 248, + 504, + 271 + ], + "lines": [ + { + "bbox": [ + 105, + 247, + 505, + 261 + ], + "spans": [ + { + "bbox": [ + 105, + 247, + 505, + 261 + ], + "score": 1.0, + "content": "In the main design we have explored, merging the new information with the previous memory state", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 259, + 275, + 272 + ], + "spans": [ + { + "bbox": [ + 105, + 259, + 275, + 272 + ], + "score": 1.0, + "content": "is done simply by a linear transformation.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 10.5 + }, + { + "type": "interline_equation", + "bbox": [ + 240, + 285, + 371, + 300 + ], + "lines": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "spans": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "score": 0.93, + "content": "m _ { i } ^ { \\prime } = W ^ { 2 d , d } [ m _ { n e w } , m _ { i - 1 } ] + b ^ { d }", + "type": "interline_equation", + "image_path": "fae487b9caae60fb93edbf054820528c6b18061cf0a3ab7e592c6d4fbd281b4a.jpg" + } + ] + } + ], + "index": 12, + "virtual_lines": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "spans": [], + "index": 12 + } + ] + }, + { + "type": "text", + "bbox": [ + 106, + 306, + 505, + 372 + ], + "lines": [ + { + "bbox": [ + 106, + 306, + 505, + 319 + ], + "spans": [ + { + "bbox": [ + 106, + 306, + 505, + 319 + ], + "score": 1.0, + "content": "In addition, we have explored two variations of this design. The first, self-attention, allows consid-", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 317, + 505, + 330 + ], + "spans": [ + { + "bbox": [ + 105, + 317, + 343, + 330 + ], + "score": 1.0, + "content": "ering any previous memories rather than just the last one", + "type": "text" + }, + { + "bbox": [ + 343, + 319, + 366, + 329 + ], + "score": 0.9, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 367, + 317, + 505, + 330 + ], + "score": 1.0, + "content": ", thus providing the network with", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 329, + 505, + 341 + ], + "spans": [ + { + "bbox": [ + 106, + 329, + 505, + 341 + ], + "score": 1.0, + "content": "the capacity to model non-sequential reasoning processes. The second variation is adding gating", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 340, + 505, + 351 + ], + "spans": [ + { + "bbox": [ + 106, + 340, + 505, + 351 + ], + "score": 1.0, + "content": "mechanisms to the writing unit. These may allow the model to dynamically adjust the practical", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 349, + 505, + 363 + ], + "spans": [ + { + "bbox": [ + 105, + 349, + 505, + 363 + ], + "score": 1.0, + "content": "length of the computation to the question complexity and stabilize the memory content throughout", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 362, + 333, + 373 + ], + "spans": [ + { + "bbox": [ + 106, + 362, + 333, + 373 + ], + "score": 1.0, + "content": "the sequential network (similarly to GRUs and LSTMs).", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 15.5 + }, + { + "type": "text", + "bbox": [ + 106, + 384, + 505, + 462 + ], + "lines": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "score": 1.0, + "content": "Self-Attention. The current architecture that we have presented allows the model to perform rea-", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 396, + 505, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 505, + 407 + ], + "score": 1.0, + "content": "soning steps in a sequence, passing control and memory states from one cell to the following. How-", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 406, + 506, + 419 + ], + "spans": [ + { + "bbox": [ + 105, + 406, + 506, + 419 + ], + "score": 1.0, + "content": "ever, we would like to grant the system with more flexibility. Particularly, we would like to allow it", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 417, + 505, + 430 + ], + "spans": [ + { + "bbox": [ + 105, + 417, + 505, + 430 + ], + "score": 1.0, + "content": "to capture more complicated reasoning processes such as trees and graphs - Directed Acyclic Graph", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 428, + 506, + 441 + ], + "spans": [ + { + "bbox": [ + 106, + 428, + 506, + 441 + ], + "score": 1.0, + "content": "(DAG) in particular, where several branches of reasoning sub-processes are merged together in later", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 104, + 438, + 506, + 453 + ], + "spans": [ + { + "bbox": [ + 104, + 438, + 506, + 453 + ], + "score": 1.0, + "content": "stages. 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It receives the last memory state", + "type": "text" + }, + { + "bbox": [ + 316, + 212, + 340, + 222 + ], + "score": 0.89, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 209, + 505, + 222 + ], + "score": 1.0, + "content": "from the previous MAC cell, along with", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 220, + 506, + 234 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 403, + 234 + ], + "score": 1.0, + "content": "the newly retrieved information from the read unit in the current iteration,", + "type": "text" + }, + { + "bbox": [ + 404, + 222, + 429, + 232 + ], + "score": 0.89, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 220, + 506, + 234 + ], + "score": 1.0, + "content": ". See figure 5 for a", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 230, + 145, + 245 + ], + "spans": [ + { + "bbox": [ + 105, + 230, + 145, + 245 + ], + "score": 1.0, + "content": "diagram.", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 8, + "bbox_fs": [ + 105, + 209, + 506, + 245 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 248, + 504, + 271 + ], + "lines": [ + { + "bbox": [ + 105, + 247, + 505, + 261 + ], + "spans": [ + { + "bbox": [ + 105, + 247, + 505, + 261 + ], + "score": 1.0, + "content": "In the main design we have explored, merging the new information with the previous memory state", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 259, + 275, + 272 + ], + "spans": [ + { + "bbox": [ + 105, + 259, + 275, + 272 + ], + "score": 1.0, + "content": "is done simply by a linear transformation.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 10.5, + "bbox_fs": [ + 105, + 247, + 505, + 272 + ] + }, + { + "type": "interline_equation", + "bbox": [ + 240, + 285, + 371, + 300 + ], + "lines": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "spans": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "score": 0.93, + "content": "m _ { i } ^ { \\prime } = W ^ { 2 d , d } [ m _ { n e w } , m _ { i - 1 } ] + b ^ { d }", + "type": "interline_equation", + "image_path": "fae487b9caae60fb93edbf054820528c6b18061cf0a3ab7e592c6d4fbd281b4a.jpg" + } + ] + } + ], + "index": 12, + "virtual_lines": [ + { + "bbox": [ + 240, + 285, + 371, + 300 + ], + "spans": [], + "index": 12 + } + ] + }, + { + "type": "text", + "bbox": [ + 106, + 306, + 505, + 372 + ], + "lines": [ + { + "bbox": [ + 106, + 306, + 505, + 319 + ], + "spans": [ + { + "bbox": [ + 106, + 306, + 505, + 319 + ], + "score": 1.0, + "content": "In addition, we have explored two variations of this design. 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The second variation is adding gating", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 340, + 505, + 351 + ], + "spans": [ + { + "bbox": [ + 106, + 340, + 505, + 351 + ], + "score": 1.0, + "content": "mechanisms to the writing unit. These may allow the model to dynamically adjust the practical", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 349, + 505, + 363 + ], + "spans": [ + { + "bbox": [ + 105, + 349, + 505, + 363 + ], + "score": 1.0, + "content": "length of the computation to the question complexity and stabilize the memory content throughout", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 362, + 333, + 373 + ], + "spans": [ + { + "bbox": [ + 106, + 362, + 333, + 373 + ], + "score": 1.0, + "content": "the sequential network (similarly to GRUs and LSTMs).", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 15.5, + "bbox_fs": [ + 105, + 306, + 505, + 373 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 384, + 505, + 462 + ], + "lines": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "score": 1.0, + "content": "Self-Attention. The current architecture that we have presented allows the model to perform rea-", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 396, + 505, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 505, + 407 + ], + "score": 1.0, + "content": "soning steps in a sequence, passing control and memory states from one cell to the following. How-", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 406, + 506, + 419 + ], + "spans": [ + { + "bbox": [ + 105, + 406, + 506, + 419 + ], + "score": 1.0, + "content": "ever, we would like to grant the system with more flexibility. 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ModelOverallCountExistCompare NumbersQuery AttributeCompare Attribute
Human (Johnson et al., 2017)92.686.796.686.595.096.0
Q-type baseline (Johnson et al., 2017)41.834.650.251.036.051.3
LSTM (Johnson et al., 2017)46.841.761.169.836.851.8
CNN+LSTM (Johnson et al., 2017)52.343.765.267.149.353.0
CNN+LSTM+SA (Johnson et al., 2016)76.664.482.777.482.675.4
N2NMN* (Hu et al. 2017)83.768.585.784.990.088.7
PG+EE (9K prog.)* (Johnson et al., 2017)88.679.789.779.192.696.0
PG+EE (70oK prog.)* (Johnson et al., 2017)96.992.797.198.798.198.9
CNN+LSTM+RN†‡ (Santoro et al.,2017)95.590.197.893.697.997.1
CNN+GRU+FiLM (Perez et al.,2017)97.794.399.196.899.199.1
CNN+GRU+FiLM* (Perez et al., 2017)97.694.399.393.499.399.3
CNN+GRU+FiLM (Perez et al., 2017)97.794.399.196.899.199.1
MAC (this paper)-val98.997.199.599.399.299.2
MAC-val99.097.299.599.599.599.4
MAC-test98.997.299.599.499.399.5
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ModelOverallCountExistCompare NumbersQuery AttributeCompare Attribute
Human (Johnson et al., 2017)92.686.796.686.595.096.0
Q-type baseline (Johnson et al., 2017)41.834.650.251.036.051.3
LSTM (Johnson et al., 2017)46.841.761.169.836.851.8
CNN+LSTM (Johnson et al., 2017)52.343.765.267.149.353.0
CNN+LSTM+SA (Johnson et al., 2016)76.664.482.777.482.675.4
N2NMN* (Hu et al. 2017)83.768.585.784.990.088.7
PG+EE (9K prog.)* (Johnson et al., 2017)88.679.789.779.192.696.0
PG+EE (70oK prog.)* (Johnson et al., 2017)96.992.797.198.798.198.9
CNN+LSTM+RN†‡ (Santoro et al.,2017)95.590.197.893.697.997.1
CNN+GRU+FiLM (Perez et al.,2017)97.794.399.196.899.199.1
CNN+GRU+FiLM* (Perez et al., 2017)97.694.399.393.499.399.3
CNN+GRU+FiLM (Perez et al., 2017)97.794.399.196.899.199.1
MAC (this paper)-val98.997.199.599.399.299.2
MAC-val99.097.299.599.599.599.4
MAC-test98.997.299.599.499.399.5
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Remarkably, our performance on questions testing count-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 522, + 504, + 536 + ], + "spans": [ + { + "bbox": [ + 105, + 522, + 504, + 536 + ], + "score": 1.0, + "content": "ing and numerical comparisons is significantly higher than the competing models, which consis-", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 533, + 505, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 505, + 546 + ], + "score": 1.0, + "content": "tently struggle on this question type. Again, we nearly halve the corresponding error rate. 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We examine the learning curves of our and com-", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 582, + 506, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 582, + 506, + 595 + ], + "score": 1.0, + "content": "peting models. We have trained all models on the same architecture and used the author code for", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 594, + 506, + 607 + ], + "spans": [ + { + "bbox": [ + 105, + 594, + 506, + 607 + ], + "score": 1.0, + "content": "the other models. Aiming at having equal settings for comparison, we ran all models including ours", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 605, + 505, + 617 + ], + "spans": [ + { + "bbox": [ + 106, + 605, + 505, + 617 + ], + "score": 1.0, + "content": "with learned random words vectors. In order to make sure the results are statistically significant we", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 616, + 506, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 616, + 506, + 629 + ], + "score": 1.0, + "content": "ran each model multiple (10) times, and plotted the averages and confidence intervals (figure 4). The", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 626, + 506, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 626, + 506, + 640 + ], + "score": 1.0, + "content": "results show that our model learns significantly faster than the other leading methods, FiLM (Perez", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 637, + 505, + 650 + ], + "spans": [ + { + "bbox": [ + 105, + 637, + 174, + 650 + ], + "score": 1.0, + "content": "et al., 2017) and", + "type": "text" + }, + { + "bbox": [ + 175, + 638, + 208, + 649 + ], + "score": 0.75, + "content": "\\mathrm { P G + E E }", + "type": "inline_equation" + }, + { + "bbox": [ + 208, + 637, + 505, + 650 + ], + "score": 1.0, + "content": "(Johnson et al., 2017). While we do not have learning curves for the Re-", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 649, + 506, + 662 + ], + "spans": [ + { + "bbox": [ + 105, + 649, + 506, + 662 + ], + "score": 1.0, + "content": "lational Network model, Santoro et al. 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ModelTrain CLEVRTrain CLEVR + fine-tune HUMANS
LSTM (Johnson et al.,2017)27.536.5
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MAC-test58.682.5
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These results demonstrate the robustness of our architecture", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 720, + 484, + 732 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 484, + 732 + ], + "score": 1.0, + "content": "and its key role as a structural prior guiding our network to learn the intended reasoning skills.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 30, + "bbox_fs": [ + 104, + 655, + 506, + 732 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 108, + 83, + 363, + 93 + ], + "lines": [ + { + "bbox": [ + 105, + 81, + 364, + 96 + ], + "spans": [ + { + "bbox": [ + 105, + 81, + 364, + 96 + ], + "score": 1.0, + "content": "4.2 CLEVR HUMANS - NATURAL LANGUAGE QUESTIONS", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 104, + 505, + 138 + ], + "lines": [ + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "score": 1.0, + "content": "We analyze our model performance on the CLEVR-Humans dataset (Johnson et al., 2017), consist-", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 116, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 106, + 116, + 505, + 128 + ], + "score": 1.0, + "content": "ing of natural language questions collected through crowdsourcing. 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See text for full detail.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 21 + } + ], + "index": 20.0 + }, + { + "type": "text", + "bbox": [ + 106, + 654, + 505, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "score": 1.0, + "content": "Based on the validation set, we have conducted an ablation study on our model to understand better", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 665, + 505, + 678 + ], + "spans": [ + { + "bbox": [ + 105, + 665, + 505, + 678 + ], + "score": 1.0, + "content": "the contribution of each of its component to the overall performance. 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dimension 12897.677.0
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The results show that for the standard dataset there is", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 467, + 506, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 467, + 308, + 480 + ], + "score": 1.0, + "content": "only a small difference between these settings of", + "type": "text" + }, + { + "bbox": [ + 308, + 468, + 323, + 478 + ], + "score": 0.86, + "content": "1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 467, + 506, + 480 + ], + "score": 1.0, + "content": ". However, for less data, we see much more", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 479, + 172, + 491 + ], + "score": 1.0, + "content": "significant drop", + "type": "text" + }, + { + "bbox": [ + 172, + 478, + 199, + 489 + ], + "score": 0.86, + "content": "1 6 . 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 199, + 479, + 505, + 491 + ], + "score": 1.0, + "content": "in the unshared-parameters setting compared to the shared one. Indeed, we", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "score": 1.0, + "content": "observe that a model with less parameter is more data-efficient and has a lower tendency to overfit", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 499, + 143, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 499, + 143, + 513 + ], + "score": 1.0, + "content": "the data.", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 27.5 + }, + { + "type": "text", + "bbox": [ + 106, + 517, + 505, + 639 + ], + "lines": [ + { + "bbox": [ + 106, + 517, + 505, + 529 + ], + "spans": [ + { + "bbox": [ + 106, + 517, + 505, + 529 + ], + "score": 1.0, + "content": "Control Unit. We have performed several ablations in the control unit to understand its contribution", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "score": 1.0, + "content": "to the overall model performance. Based on the results, first, we can see the the question information", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 540, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 540, + 505, + 552 + ], + "score": 1.0, + "content": "is crucial for the model to handle the questions, as can be noted by the low performance of the model", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 550, + 505, + 563 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 505, + 563 + ], + "score": 1.0, + "content": "when there is no use of control signal whatsoever. Second, we have tested the model performance", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 560, + 506, + 575 + ], + "spans": [ + { + "bbox": [ + 105, + 560, + 506, + 575 + ], + "score": 1.0, + "content": "when using the continuous control state computed by question (2) in section 3.2.1, without having", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "spans": [ + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "score": 1.0, + "content": "word-attention, in order to understand its relative contribution. Based on the results, we can indeed", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 583, + 505, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 505, + 596 + ], + "score": 1.0, + "content": "see that using word-attention is useful for accelerating the training process and achieving higher", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 594, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 594, + 505, + 606 + ], + "score": 1.0, + "content": "accuracies both for the standard dataset as well as for the small subset, where using word-attention", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 604, + 504, + 617 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 183, + 617 + ], + "score": 1.0, + "content": "increases results in", + "type": "text" + }, + { + "bbox": [ + 183, + 605, + 211, + 615 + ], + "score": 0.87, + "content": "2 1 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 211, + 604, + 504, + 617 + ], + "score": 1.0, + "content": ". We also see that using the “contextual words” produced by the question-", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 615, + 505, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 505, + 629 + ], + "score": 1.0, + "content": "unit LSTM is useful in accelerating the model performance, when compared to using the word-", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 627, + 172, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 172, + 639 + ], + "score": 1.0, + "content": "vectors directly.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 36 + }, + { + "type": "text", + "bbox": [ + 106, + 643, + 505, + 732 + ], + "lines": [ + { + "bbox": [ + 105, + 643, + 505, + 655 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 505, + 655 + ], + "score": 1.0, + "content": "Reading Unit. We have conducted several ablations for the reading unit to better understand its", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "score": 1.0, + "content": "behavior and contribution to the performance of the model. The standard MAC reading unit uses the", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 666, + 505, + 677 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 505, + 677 + ], + "score": 1.0, + "content": "control state – which averages the question words based on attention distributions computed per each", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 677, + 505, + 689 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 505, + 689 + ], + "score": 1.0, + "content": "reasoning step. In this ablation experiment, we have tested using the full question representation", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 688, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 106, + 690, + 113, + 699 + ], + "score": 0.74, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 113, + 688, + 505, + 700 + ], + "score": 1.0, + "content": "instead across all reasoning steps to gain better understanding of the the contribution of word-", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 104, + 698, + 504, + 712 + ], + "spans": [ + { + "bbox": [ + 104, + 698, + 374, + 712 + ], + "score": 1.0, + "content": "attention to the model performance. 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ModelStandard CLEVR10% CLEVR
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dimension 25698.476.3
dimension 12897.677.0
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We have tested the model performance as a function of the network’s length –", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 312, + 505, + 325 + ], + "spans": [ + { + "bbox": [ + 105, + 312, + 505, + 325 + ], + "score": 1.0, + "content": "the number of MAC cells that were sequenced together. The results show the positive correlation", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "score": 1.0, + "content": "between the network length and its performance. 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The performance keeps improving up to lengths 8-16 that achieve", + "type": "text" + }, + { + "bbox": [ + 404, + 346, + 451, + 357 + ], + "score": 0.87, + "content": "9 8 . 9 – 9 9 . 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 452, + 345, + 505, + 359 + ], + "score": 1.0, + "content": ". The results", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 356, + 506, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 506, + 370 + ], + "score": 1.0, + "content": "also teach us about the complexity of the dataset, by showing the relatively significant benefits of", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 367, + 327, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 367, + 327, + 381 + ], + "score": 1.0, + "content": "having at least 4 cells, each modeling a reasoning step.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 16, + "bbox_fs": [ + 105, + 302, + 506, + 381 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 385, + 505, + 440 + ], + "lines": [ + { + "bbox": [ + 106, + 385, + 505, + 397 + ], + "spans": [ + { + "bbox": [ + 106, + 385, + 505, + 397 + ], + "score": 1.0, + "content": "Network Dimension. 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However, for", + "type": "text" + }, + { + "bbox": [ + 392, + 418, + 411, + 428 + ], + "score": 0.87, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 411, + 416, + 506, + 432 + ], + "score": 1.0, + "content": "of CLEVR, the larger", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 428, + 400, + 442 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 282, + 442 + ], + "score": 1.0, + "content": "512-dimension allows accuracy increase by", + "type": "text" + }, + { + "bbox": [ + 282, + 429, + 304, + 439 + ], + "score": 0.86, + "content": "7 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 305, + 428, + 400, + 442 + ], + "score": 1.0, + "content": "over dimension of 128.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 22, + "bbox_fs": [ + 105, + 385, + 506, + 442 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 445, + 505, + 511 + ], + "lines": [ + { + "bbox": [ + 106, + 446, + 505, + 457 + ], + "spans": [ + { + "bbox": [ + 106, + 446, + 505, + 457 + ], + "score": 1.0, + "content": "Weight Sharing. We have tested the impact of sharing weights between cell has on the model", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 456, + 506, + 469 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 249, + 469 + ], + "score": 1.0, + "content": "performance for network of length", + "type": "text" + }, + { + "bbox": [ + 249, + 457, + 281, + 468 + ], + "score": 0.91, + "content": "p = 1 2", + "type": "inline_equation" + }, + { + "bbox": [ + 281, + 456, + 506, + 469 + ], + "score": 1.0, + "content": ". The results show that for the standard dataset there is", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 467, + 506, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 467, + 308, + 480 + ], + "score": 1.0, + "content": "only a small difference between these settings of", + "type": "text" + }, + { + "bbox": [ + 308, + 468, + 323, + 478 + ], + "score": 0.86, + "content": "1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 467, + 506, + 480 + ], + "score": 1.0, + "content": ". 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Indeed, we", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "score": 1.0, + "content": "observe that a model with less parameter is more data-efficient and has a lower tendency to overfit", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 499, + 143, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 499, + 143, + 513 + ], + "score": 1.0, + "content": "the data.", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 27.5, + "bbox_fs": [ + 105, + 446, + 506, + 513 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 517, + 505, + 639 + ], + "lines": [ + { + "bbox": [ + 106, + 517, + 505, + 529 + ], + "spans": [ + { + "bbox": [ + 106, + 517, + 505, + 529 + ], + "score": 1.0, + "content": "Control Unit. We have performed several ablations in the control unit to understand its contribution", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 528, + 505, + 541 + ], + "score": 1.0, + "content": "to the overall model performance. Based on the results, first, we can see the the question information", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 540, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 540, + 505, + 552 + ], + "score": 1.0, + "content": "is crucial for the model to handle the questions, as can be noted by the low performance of the model", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 550, + 505, + 563 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 505, + 563 + ], + "score": 1.0, + "content": "when there is no use of control signal whatsoever. Second, we have tested the model performance", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 560, + 506, + 575 + ], + "spans": [ + { + "bbox": [ + 105, + 560, + 506, + 575 + ], + "score": 1.0, + "content": "when using the continuous control state computed by question (2) in section 3.2.1, without having", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "spans": [ + { + "bbox": [ + 106, + 573, + 505, + 584 + ], + "score": 1.0, + "content": "word-attention, in order to understand its relative contribution. Based on the results, we can indeed", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 583, + 505, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 505, + 596 + ], + "score": 1.0, + "content": "see that using word-attention is useful for accelerating the training process and achieving higher", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 594, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 105, + 594, + 505, + 606 + ], + "score": 1.0, + "content": "accuracies both for the standard dataset as well as for the small subset, where using word-attention", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 604, + 504, + 617 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 183, + 617 + ], + "score": 1.0, + "content": "increases results in", + "type": "text" + }, + { + "bbox": [ + 183, + 605, + 211, + 615 + ], + "score": 0.87, + "content": "2 1 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 211, + 604, + 504, + 617 + ], + "score": 1.0, + "content": ". We also see that using the “contextual words” produced by the question-", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 615, + 505, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 505, + 629 + ], + "score": 1.0, + "content": "unit LSTM is useful in accelerating the model performance, when compared to using the word-", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 627, + 172, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 172, + 639 + ], + "score": 1.0, + "content": "vectors directly.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 36, + "bbox_fs": [ + 105, + 517, + 506, + 639 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 643, + 505, + 732 + ], + "lines": [ + { + "bbox": [ + 105, + 643, + 505, + 655 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 505, + 655 + ], + "score": 1.0, + "content": "Reading Unit. We have conducted several ablations for the reading unit to better understand its", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "score": 1.0, + "content": "behavior and contribution to the performance of the model. The standard MAC reading unit uses the", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 666, + 505, + 677 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 505, + 677 + ], + "score": 1.0, + "content": "control state – which averages the question words based on attention distributions computed per each", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 677, + 505, + 689 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 505, + 689 + ], + "score": 1.0, + "content": "reasoning step. In this ablation experiment, we have tested using the full question representation", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 688, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 106, + 690, + 113, + 699 + ], + "score": 0.74, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 113, + 688, + 505, + 700 + ], + "score": 1.0, + "content": "instead across all reasoning steps to gain better understanding of the the contribution of word-", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 104, + 698, + 504, + 712 + ], + "spans": [ + { + "bbox": [ + 104, + 698, + 374, + 712 + ], + "score": 1.0, + "content": "attention to the model performance. Indeed, we can see that using", + "type": "text" + }, + { + "bbox": [ + 375, + 701, + 381, + 710 + ], + "score": 0.77, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 381, + 698, + 495, + 712 + ], + "score": 1.0, + "content": "rather then the control state", + "type": "text" + }, + { + "bbox": [ + 495, + 701, + 504, + 710 + ], + "score": 0.8, + "content": "c _ { i }", + "type": "inline_equation" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 710, + 504, + 721 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 282, + 721 + ], + "score": 1.0, + "content": "results in a significant drops in performance", + "type": "text" + }, + { + "bbox": [ + 282, + 710, + 316, + 720 + ], + "score": 0.8, + "content": "- 1 9 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 316, + 710, + 443, + 721 + ], + "score": 1.0, + "content": "for the full CLEVR dataset and", + "type": "text" + }, + { + "bbox": [ + 443, + 710, + 470, + 720 + ], + "score": 0.86, + "content": "1 9 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 470, + 710, + 484, + 721 + ], + "score": 1.0, + "content": "for", + "type": "text" + }, + { + "bbox": [ + 485, + 710, + 504, + 720 + ], + "score": 0.85, + "content": "10 \\%", + "type": "inline_equation" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 720, + 154, + 732 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 154, + 732 + ], + "score": 1.0, + "content": "of the data.", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 45.5, + "bbox_fs": [ + 104, + 643, + 505, + 732 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 159 + ], + "lines": [ + { + "bbox": [ + 106, + 81, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 81, + 505, + 95 + ], + "score": 1.0, + "content": "We have conducted additional ablation experiment to better understand the contribution of using", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "score": 1.0, + "content": "the KB features directly in the first-stage information retrieval process described in section 3.2.2,", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 104, + 102, + 506, + 119 + ], + "spans": [ + { + "bbox": [ + 104, + 102, + 477, + 119 + ], + "score": 1.0, + "content": "compared to using only the dot-products of the KB elements with the previous memory state", + "type": "text" + }, + { + "bbox": [ + 478, + 106, + 501, + 116 + ], + "score": 0.88, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 501, + 102, + 506, + 119 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "score": 1.0, + "content": "For the full CLEVR dataset, we can see that this component has only a small impact in the final", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 127, + 258, + 138 + ], + "score": 1.0, + "content": "performance - ultimately resulting in", + "type": "text" + }, + { + "bbox": [ + 258, + 126, + 285, + 137 + ], + "score": 0.87, + "content": "0 . 0 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 285, + 127, + 458, + 138 + ], + "score": 1.0, + "content": "performance difference. However, for the", + "type": "text" + }, + { + "bbox": [ + 458, + 127, + 478, + 137 + ], + "score": 0.86, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 478, + 127, + 505, + 138 + ], + "score": 1.0, + "content": "of the", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "spans": [ + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "score": 1.0, + "content": "data, we can see that the difference in performance when ablating this component is much larger -", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 148, + 138, + 160 + ], + "spans": [ + { + "bbox": [ + 107, + 148, + 133, + 159 + ], + "score": 0.83, + "content": "1 1 . 2 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 134, + 148, + 138, + 160 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 3 + }, + { + "type": "text", + "bbox": [ + 107, + 165, + 504, + 253 + ], + "lines": [ + { + "bbox": [ + 106, + 165, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 106, + 165, + 505, + 178 + ], + "score": 1.0, + "content": "Writing Unit Ablations. In our main MAC model variant, the memory unit merges the new in-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 506, + 190 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 149, + 190 + ], + "score": 1.0, + "content": "formation", + "type": "text" + }, + { + "bbox": [ + 149, + 178, + 174, + 187 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 174, + 176, + 306, + 190 + ], + "score": 1.0, + "content": "with the previous memory state", + "type": "text" + }, + { + "bbox": [ + 306, + 178, + 329, + 188 + ], + "score": 0.89, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 330, + 176, + 506, + 190 + ], + "score": 1.0, + "content": "by combining them through a linear trans-", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 187, + 504, + 201 + ], + "spans": [ + { + "bbox": [ + 105, + 187, + 452, + 201 + ], + "score": 1.0, + "content": "formation. In this experiment, we have explored other variations, such as assigning", + "type": "text" + }, + { + "bbox": [ + 452, + 189, + 477, + 199 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 478, + 187, + 490, + 201 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 490, + 189, + 504, + 198 + ], + "score": 0.83, + "content": "m _ { i }", + "type": "inline_equation" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 199, + 506, + 210 + ], + "spans": [ + { + "bbox": [ + 106, + 199, + 436, + 210 + ], + "score": 1.0, + "content": "directly – ignoring previous memories, or doing a linear transformation based on", + "type": "text" + }, + { + "bbox": [ + 436, + 200, + 461, + 209 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 199, + 506, + 210 + ], + "score": 1.0, + "content": "only. The", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 208, + 506, + 221 + ], + "spans": [ + { + "bbox": [ + 105, + 208, + 443, + 221 + ], + "score": 1.0, + "content": "results show that in fact such variant is only slightly worse than our main variant –", + "type": "text" + }, + { + "bbox": [ + 443, + 210, + 465, + 220 + ], + "score": 0.73, + "content": "0 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 465, + 208, + 506, + 221 + ], + "score": 1.0, + "content": ". We also", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 219, + 505, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 219, + 505, + 233 + ], + "score": 1.0, + "content": "conducted an experiment in which we merge the new information with the previous memory just by", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 231, + 506, + 243 + ], + "spans": [ + { + "bbox": [ + 105, + 231, + 506, + 243 + ], + "score": 1.0, + "content": "a having a gate that does a weighted average of them. The results show that this variant performs", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 243, + 339, + 253 + ], + "spans": [ + { + "bbox": [ + 105, + 243, + 339, + 253 + ], + "score": 1.0, + "content": "equivalently to our standard linear-transformation variant.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 10.5 + }, + { + "type": "text", + "bbox": [ + 107, + 258, + 504, + 336 + ], + "lines": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "spans": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "score": 1.0, + "content": "Writing Unit Additions. We have explored the impact of the writing unit variants described in sec-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 269, + 506, + 283 + ], + "spans": [ + { + "bbox": [ + 105, + 269, + 506, + 283 + ], + "score": 1.0, + "content": "tion 3.2.3 – adding self-attention, gating mechanisms, or both, compared to our standard main model", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 280, + 406, + 294 + ], + "score": 1.0, + "content": "that uses a linear transformation to merge the newly retrieved information", + "type": "text" + }, + { + "bbox": [ + 406, + 282, + 431, + 292 + ], + "score": 0.89, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 432, + 280, + 506, + 294 + ], + "score": 1.0, + "content": "with the previous", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 104, + 290, + 506, + 306 + ], + "spans": [ + { + "bbox": [ + 104, + 290, + 142, + 306 + ], + "score": 1.0, + "content": "memory", + "type": "text" + }, + { + "bbox": [ + 142, + 294, + 155, + 303 + ], + "score": 0.86, + "content": "m _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 156, + 290, + 506, + 306 + ], + "score": 1.0, + "content": ". For the complete CLEVR dataset we can see that indeed both these variants are very", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 303, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 506, + 315 + ], + "score": 1.0, + "content": "helpful in increasing the model performance. Compared to our standard MAC model that achieves", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 313, + 506, + 326 + ], + "spans": [ + { + "bbox": [ + 106, + 314, + 138, + 324 + ], + "score": 0.87, + "content": "9 8 . 9 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 139, + 313, + 362, + 326 + ], + "score": 1.0, + "content": "on the validation set, self-attention yields accuracy of", + "type": "text" + }, + { + "bbox": [ + 362, + 314, + 394, + 325 + ], + "score": 0.87, + "content": "9 9 . 2 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 394, + 313, + 453, + 326 + ], + "score": 1.0, + "content": ", gating yields", + "type": "text" + }, + { + "bbox": [ + 454, + 314, + 486, + 324 + ], + "score": 0.87, + "content": "9 9 . 3 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 313, + 506, + 326 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 325, + 228, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 325, + 192, + 337 + ], + "score": 1.0, + "content": "adding both achieves", + "type": "text" + }, + { + "bbox": [ + 192, + 325, + 225, + 335 + ], + "score": 0.87, + "content": "9 9 . 4 8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 225, + 325, + 228, + 337 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 342, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "score": 1.0, + "content": "Output Unit. In our standard model, the final predictions made in the output unit are based on the", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 353, + 505, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 353, + 183, + 365 + ], + "score": 1.0, + "content": "final memory state", + "type": "text" + }, + { + "bbox": [ + 184, + 354, + 198, + 365 + ], + "score": 0.87, + "content": "m _ { p }", + "type": "inline_equation" + }, + { + "bbox": [ + 199, + 353, + 336, + 365 + ], + "score": 1.0, + "content": "as well as question representation", + "type": "text" + }, + { + "bbox": [ + 337, + 354, + 343, + 364 + ], + "score": 0.77, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 353, + 505, + 365 + ], + "score": 1.0, + "content": "(stands for the final hidden states of the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 363, + 505, + 376 + ], + "spans": [ + { + "bbox": [ + 105, + 363, + 505, + 376 + ], + "score": 1.0, + "content": "backward and forwards passes of the LSTM). We have explored the contribution of basing the model", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 374, + 505, + 388 + ], + "spans": [ + { + "bbox": [ + 105, + 374, + 505, + 388 + ], + "score": 1.0, + "content": "prediction on the latter, by testing the model performance when prediction is based on memory", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 385, + 505, + 398 + ], + "spans": [ + { + "bbox": [ + 105, + 385, + 225, + 398 + ], + "score": 1.0, + "content": "alone, for the complete and", + "type": "text" + }, + { + "bbox": [ + 225, + 385, + 245, + 396 + ], + "score": 0.87, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 245, + 385, + 505, + 398 + ], + "score": 1.0, + "content": "datasets. We can see that in both settings basing the model’s", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 397, + 505, + 409 + ], + "spans": [ + { + "bbox": [ + 105, + 397, + 505, + 409 + ], + "score": 1.0, + "content": "predictions on the question representation allows faster training and higher accuracies. Notable is", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 408, + 231, + 420 + ], + "score": 1.0, + "content": "the gap in performance for the", + "type": "text" + }, + { + "bbox": [ + 232, + 407, + 251, + 418 + ], + "score": 0.85, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 252, + 408, + 294, + 420 + ], + "score": 1.0, + "content": "CLEVR -", + "type": "text" + }, + { + "bbox": [ + 294, + 407, + 321, + 418 + ], + "score": 0.82, + "content": "1 9 . 8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 321, + 408, + 505, + 420 + ], + "score": 1.0, + "content": "increase by using the question representation", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 419, + 505, + 431 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 505, + 431 + ], + "score": 1.0, + "content": "to make predictions. These results are very reasonable intuitively, since the model is structured such", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 428, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 505, + 443 + ], + "score": 1.0, + "content": "that the memory holds only information that was retrieved from the image. Thus, questions that may", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 439, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 439, + 505, + 454 + ], + "score": 1.0, + "content": "ask for instance on different aspects (such as color or shape) of the same object in the image may", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 451, + 506, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 506, + 464 + ], + "score": 1.0, + "content": "result in the same memory content, which is thus does not directly contain enough information to", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 462, + 204, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 462, + 204, + 474 + ], + "score": 1.0, + "content": "respond such questions.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 27.5 + }, + { + "type": "text", + "bbox": [ + 106, + 479, + 505, + 556 + ], + "lines": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "score": 1.0, + "content": "Position. In our standard model, similarly to the practice of competing models (Santoro et al., 2017;", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "score": 1.0, + "content": "Perez et al., 2017; Hu et al., 2017), we have concatenated positional information to each region of the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "spans": [ + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "score": 1.0, + "content": "image, in order to increase the model capability to perform spatial reasoning. We have explored both", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 511, + 505, + 525 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 237, + 525 + ], + "score": 1.0, + "content": "simple linear maps at a constant", + "type": "text" + }, + { + "bbox": [ + 238, + 512, + 266, + 524 + ], + "score": 0.9, + "content": "[ - 1 , 1 ]", + "type": "inline_equation" + }, + { + "bbox": [ + 267, + 511, + 505, + 525 + ], + "score": 1.0, + "content": "as well as more complex positional encoding suggested by", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 522, + 505, + 534 + ], + "spans": [ + { + "bbox": [ + 106, + 522, + 429, + 534 + ], + "score": 1.0, + "content": "(Vaswani et al., 2017). However, the results for both the standard dataset and the", + "type": "text" + }, + { + "bbox": [ + 430, + 523, + 449, + 533 + ], + "score": 0.87, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 449, + 522, + 505, + 534 + ], + "score": 1.0, + "content": "version show", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 533, + 506, + 547 + ], + "spans": [ + { + "bbox": [ + 104, + 533, + 506, + 547 + ], + "score": 1.0, + "content": "a very negligible improvement at best when adding positional encoding information, demonstrating", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 546, + 424, + 557 + ], + "spans": [ + { + "bbox": [ + 106, + 546, + 424, + 557 + ], + "score": 1.0, + "content": "the capability of MAC to perform spatial reasoning without data augmentation.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37 + }, + { + "type": "text", + "bbox": [ + 107, + 561, + 505, + 650 + ], + "lines": [ + { + "bbox": [ + 105, + 561, + 505, + 574 + ], + "spans": [ + { + "bbox": [ + 105, + 561, + 505, + 574 + ], + "score": 1.0, + "content": "Gate Bias Initialization. For our model variant with gating mechanism (described in section 3.2.3)", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 573, + 505, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 382, + 585 + ], + "score": 1.0, + "content": "we have tested the effect of setting different values for the gate bias -", + "type": "text" + }, + { + "bbox": [ + 382, + 573, + 406, + 584 + ], + "score": 0.85, + "content": "- 1 , 0", + "type": "inline_equation" + }, + { + "bbox": [ + 406, + 573, + 448, + 585 + ], + "score": 1.0, + "content": "and 1. for", + "type": "text" + }, + { + "bbox": [ + 448, + 573, + 462, + 583 + ], + "score": 0.66, + "content": "- 1", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 573, + 505, + 585 + ], + "score": 1.0, + "content": "the model", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 584, + 505, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 505, + 596 + ], + "score": 1.0, + "content": "is initialized to biased for keeping the previous memory value whereas for 1 it will be biased for", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 595, + 505, + 607 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 505, + 607 + ], + "score": 1.0, + "content": "using the new memory instead. We can see that for the complete dataset setting the bias to 1 is", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 606, + 505, + 618 + ], + "spans": [ + { + "bbox": [ + 106, + 606, + 505, + 618 + ], + "score": 1.0, + "content": "optimal – apparently since the model has enough data to learn to apply each cell effectively. In", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 616, + 506, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 616, + 195, + 629 + ], + "score": 1.0, + "content": "contrast, for the small", + "type": "text" + }, + { + "bbox": [ + 196, + 617, + 215, + 627 + ], + "score": 0.85, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 216, + 616, + 506, + 629 + ], + "score": 1.0, + "content": "CLEVR data, setting the bias to 0 shows better performance, biasing the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 628, + 505, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 505, + 640 + ], + "score": 1.0, + "content": "model to using less cells overall which results ultimately in a theoretically-simpler model that can", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 638, + 223, + 651 + ], + "spans": [ + { + "bbox": [ + 105, + 638, + 223, + 651 + ], + "score": 1.0, + "content": "fit less data more effectively.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 44.5 + }, + { + "type": "title", + "bbox": [ + 108, + 674, + 213, + 685 + ], + "lines": [ + { + "bbox": [ + 106, + 674, + 215, + 686 + ], + "spans": [ + { + "bbox": [ + 106, + 674, + 215, + 686 + ], + "score": 1.0, + "content": "4.4 INTERPRETABILITY", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 49 + }, + { + "type": "text", + "bbox": [ + 108, + 699, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 698, + 506, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 698, + 506, + 712 + ], + "score": 1.0, + "content": "We have looked into attention maps over the image and question that the model produces during its", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 709, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 505, + 722 + ], + "score": 1.0, + "content": "computation and provide a few examples in figure 4.4. The first example shows us how the model", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 721, + 505, + 733 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 505, + 733 + ], + "score": 1.0, + "content": "parses the question in steps, first focusing on the main entity that the question is about, then on", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 51 + } + ], + "page_idx": 12, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2018", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 301, + 751, + 311, + 760 + ], + "lines": [ + { + "bbox": [ + 298, + 750, + 312, + 763 + ], + "spans": [ + { + "bbox": [ + 298, + 750, + 312, + 763 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 13, + "width": 14 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 159 + ], + "lines": [ + { + "bbox": [ + 106, + 81, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 81, + 505, + 95 + ], + "score": 1.0, + "content": "We have conducted additional ablation experiment to better understand the contribution of using", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 106 + ], + "score": 1.0, + "content": "the KB features directly in the first-stage information retrieval process described in section 3.2.2,", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 104, + 102, + 506, + 119 + ], + "spans": [ + { + "bbox": [ + 104, + 102, + 477, + 119 + ], + "score": 1.0, + "content": "compared to using only the dot-products of the KB elements with the previous memory state", + "type": "text" + }, + { + "bbox": [ + 478, + 106, + 501, + 116 + ], + "score": 0.88, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 501, + 102, + 506, + 119 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 505, + 128 + ], + "score": 1.0, + "content": "For the full CLEVR dataset, we can see that this component has only a small impact in the final", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 127, + 258, + 138 + ], + "score": 1.0, + "content": "performance - ultimately resulting in", + "type": "text" + }, + { + "bbox": [ + 258, + 126, + 285, + 137 + ], + "score": 0.87, + "content": "0 . 0 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 285, + 127, + 458, + 138 + ], + "score": 1.0, + "content": "performance difference. However, for the", + "type": "text" + }, + { + "bbox": [ + 458, + 127, + 478, + 137 + ], + "score": 0.86, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 478, + 127, + 505, + 138 + ], + "score": 1.0, + "content": "of the", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "spans": [ + { + "bbox": [ + 105, + 137, + 505, + 150 + ], + "score": 1.0, + "content": "data, we can see that the difference in performance when ablating this component is much larger -", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 148, + 138, + 160 + ], + "spans": [ + { + "bbox": [ + 107, + 148, + 133, + 159 + ], + "score": 0.83, + "content": "1 1 . 2 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 134, + 148, + 138, + 160 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 3, + "bbox_fs": [ + 104, + 81, + 506, + 160 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 165, + 504, + 253 + ], + "lines": [ + { + "bbox": [ + 106, + 165, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 106, + 165, + 505, + 178 + ], + "score": 1.0, + "content": "Writing Unit Ablations. In our main MAC model variant, the memory unit merges the new in-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 506, + 190 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 149, + 190 + ], + "score": 1.0, + "content": "formation", + "type": "text" + }, + { + "bbox": [ + 149, + 178, + 174, + 187 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 174, + 176, + 306, + 190 + ], + "score": 1.0, + "content": "with the previous memory state", + "type": "text" + }, + { + "bbox": [ + 306, + 178, + 329, + 188 + ], + "score": 0.89, + "content": "m _ { i - 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 330, + 176, + 506, + 190 + ], + "score": 1.0, + "content": "by combining them through a linear trans-", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 187, + 504, + 201 + ], + "spans": [ + { + "bbox": [ + 105, + 187, + 452, + 201 + ], + "score": 1.0, + "content": "formation. In this experiment, we have explored other variations, such as assigning", + "type": "text" + }, + { + "bbox": [ + 452, + 189, + 477, + 199 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 478, + 187, + 490, + 201 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 490, + 189, + 504, + 198 + ], + "score": 0.83, + "content": "m _ { i }", + "type": "inline_equation" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 199, + 506, + 210 + ], + "spans": [ + { + "bbox": [ + 106, + 199, + 436, + 210 + ], + "score": 1.0, + "content": "directly – ignoring previous memories, or doing a linear transformation based on", + "type": "text" + }, + { + "bbox": [ + 436, + 200, + 461, + 209 + ], + "score": 0.88, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 199, + 506, + 210 + ], + "score": 1.0, + "content": "only. The", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 208, + 506, + 221 + ], + "spans": [ + { + "bbox": [ + 105, + 208, + 443, + 221 + ], + "score": 1.0, + "content": "results show that in fact such variant is only slightly worse than our main variant –", + "type": "text" + }, + { + "bbox": [ + 443, + 210, + 465, + 220 + ], + "score": 0.73, + "content": "0 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 465, + 208, + 506, + 221 + ], + "score": 1.0, + "content": ". We also", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 219, + 505, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 219, + 505, + 233 + ], + "score": 1.0, + "content": "conducted an experiment in which we merge the new information with the previous memory just by", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 231, + 506, + 243 + ], + "spans": [ + { + "bbox": [ + 105, + 231, + 506, + 243 + ], + "score": 1.0, + "content": "a having a gate that does a weighted average of them. The results show that this variant performs", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 243, + 339, + 253 + ], + "spans": [ + { + "bbox": [ + 105, + 243, + 339, + 253 + ], + "score": 1.0, + "content": "equivalently to our standard linear-transformation variant.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 10.5, + "bbox_fs": [ + 105, + 165, + 506, + 253 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 258, + 504, + 336 + ], + "lines": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "spans": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "score": 1.0, + "content": "Writing Unit Additions. We have explored the impact of the writing unit variants described in sec-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 269, + 506, + 283 + ], + "spans": [ + { + "bbox": [ + 105, + 269, + 506, + 283 + ], + "score": 1.0, + "content": "tion 3.2.3 – adding self-attention, gating mechanisms, or both, compared to our standard main model", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 280, + 406, + 294 + ], + "score": 1.0, + "content": "that uses a linear transformation to merge the newly retrieved information", + "type": "text" + }, + { + "bbox": [ + 406, + 282, + 431, + 292 + ], + "score": 0.89, + "content": "m _ { n e w }", + "type": "inline_equation" + }, + { + "bbox": [ + 432, + 280, + 506, + 294 + ], + "score": 1.0, + "content": "with the previous", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 104, + 290, + 506, + 306 + ], + "spans": [ + { + "bbox": [ + 104, + 290, + 142, + 306 + ], + "score": 1.0, + "content": "memory", + "type": "text" + }, + { + "bbox": [ + 142, + 294, + 155, + 303 + ], + "score": 0.86, + "content": "m _ { i }", + "type": "inline_equation" + }, + { + "bbox": [ + 156, + 290, + 506, + 306 + ], + "score": 1.0, + "content": ". For the complete CLEVR dataset we can see that indeed both these variants are very", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 303, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 506, + 315 + ], + "score": 1.0, + "content": "helpful in increasing the model performance. Compared to our standard MAC model that achieves", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 313, + 506, + 326 + ], + "spans": [ + { + "bbox": [ + 106, + 314, + 138, + 324 + ], + "score": 0.87, + "content": "9 8 . 9 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 139, + 313, + 362, + 326 + ], + "score": 1.0, + "content": "on the validation set, self-attention yields accuracy of", + "type": "text" + }, + { + "bbox": [ + 362, + 314, + 394, + 325 + ], + "score": 0.87, + "content": "9 9 . 2 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 394, + 313, + 453, + 326 + ], + "score": 1.0, + "content": ", gating yields", + "type": "text" + }, + { + "bbox": [ + 454, + 314, + 486, + 324 + ], + "score": 0.87, + "content": "9 9 . 3 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 313, + 506, + 326 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 325, + 228, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 325, + 192, + 337 + ], + "score": 1.0, + "content": "adding both achieves", + "type": "text" + }, + { + "bbox": [ + 192, + 325, + 225, + 335 + ], + "score": 0.87, + "content": "9 9 . 4 8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 225, + 325, + 228, + 337 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 18, + "bbox_fs": [ + 104, + 259, + 506, + 337 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 342, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "score": 1.0, + "content": "Output Unit. In our standard model, the final predictions made in the output unit are based on the", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 353, + 505, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 353, + 183, + 365 + ], + "score": 1.0, + "content": "final memory state", + "type": "text" + }, + { + "bbox": [ + 184, + 354, + 198, + 365 + ], + "score": 0.87, + "content": "m _ { p }", + "type": "inline_equation" + }, + { + "bbox": [ + 199, + 353, + 336, + 365 + ], + "score": 1.0, + "content": "as well as question representation", + "type": "text" + }, + { + "bbox": [ + 337, + 354, + 343, + 364 + ], + "score": 0.77, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 353, + 505, + 365 + ], + "score": 1.0, + "content": "(stands for the final hidden states of the", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 363, + 505, + 376 + ], + "spans": [ + { + "bbox": [ + 105, + 363, + 505, + 376 + ], + "score": 1.0, + "content": "backward and forwards passes of the LSTM). We have explored the contribution of basing the model", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 374, + 505, + 388 + ], + "spans": [ + { + "bbox": [ + 105, + 374, + 505, + 388 + ], + "score": 1.0, + "content": "prediction on the latter, by testing the model performance when prediction is based on memory", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 385, + 505, + 398 + ], + "spans": [ + { + "bbox": [ + 105, + 385, + 225, + 398 + ], + "score": 1.0, + "content": "alone, for the complete and", + "type": "text" + }, + { + "bbox": [ + 225, + 385, + 245, + 396 + ], + "score": 0.87, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 245, + 385, + 505, + 398 + ], + "score": 1.0, + "content": "datasets. We can see that in both settings basing the model’s", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 397, + 505, + 409 + ], + "spans": [ + { + "bbox": [ + 105, + 397, + 505, + 409 + ], + "score": 1.0, + "content": "predictions on the question representation allows faster training and higher accuracies. Notable is", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 408, + 231, + 420 + ], + "score": 1.0, + "content": "the gap in performance for the", + "type": "text" + }, + { + "bbox": [ + 232, + 407, + 251, + 418 + ], + "score": 0.85, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 252, + 408, + 294, + 420 + ], + "score": 1.0, + "content": "CLEVR -", + "type": "text" + }, + { + "bbox": [ + 294, + 407, + 321, + 418 + ], + "score": 0.82, + "content": "1 9 . 8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 321, + 408, + 505, + 420 + ], + "score": 1.0, + "content": "increase by using the question representation", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 419, + 505, + 431 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 505, + 431 + ], + "score": 1.0, + "content": "to make predictions. These results are very reasonable intuitively, since the model is structured such", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 428, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 505, + 443 + ], + "score": 1.0, + "content": "that the memory holds only information that was retrieved from the image. Thus, questions that may", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 439, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 439, + 505, + 454 + ], + "score": 1.0, + "content": "ask for instance on different aspects (such as color or shape) of the same object in the image may", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 451, + 506, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 506, + 464 + ], + "score": 1.0, + "content": "result in the same memory content, which is thus does not directly contain enough information to", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 462, + 204, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 462, + 204, + 474 + ], + "score": 1.0, + "content": "respond such questions.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 27.5, + "bbox_fs": [ + 105, + 342, + 506, + 474 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 479, + 505, + 556 + ], + "lines": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 478, + 505, + 491 + ], + "score": 1.0, + "content": "Position. In our standard model, similarly to the practice of competing models (Santoro et al., 2017;", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 505, + 502 + ], + "score": 1.0, + "content": "Perez et al., 2017; Hu et al., 2017), we have concatenated positional information to each region of the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "spans": [ + { + "bbox": [ + 106, + 501, + 505, + 513 + ], + "score": 1.0, + "content": "image, in order to increase the model capability to perform spatial reasoning. We have explored both", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 511, + 505, + 525 + ], + "spans": [ + { + "bbox": [ + 105, + 511, + 237, + 525 + ], + "score": 1.0, + "content": "simple linear maps at a constant", + "type": "text" + }, + { + "bbox": [ + 238, + 512, + 266, + 524 + ], + "score": 0.9, + "content": "[ - 1 , 1 ]", + "type": "inline_equation" + }, + { + "bbox": [ + 267, + 511, + 505, + 525 + ], + "score": 1.0, + "content": "as well as more complex positional encoding suggested by", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 522, + 505, + 534 + ], + "spans": [ + { + "bbox": [ + 106, + 522, + 429, + 534 + ], + "score": 1.0, + "content": "(Vaswani et al., 2017). However, the results for both the standard dataset and the", + "type": "text" + }, + { + "bbox": [ + 430, + 523, + 449, + 533 + ], + "score": 0.87, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 449, + 522, + 505, + 534 + ], + "score": 1.0, + "content": "version show", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 533, + 506, + 547 + ], + "spans": [ + { + "bbox": [ + 104, + 533, + 506, + 547 + ], + "score": 1.0, + "content": "a very negligible improvement at best when adding positional encoding information, demonstrating", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 546, + 424, + 557 + ], + "spans": [ + { + "bbox": [ + 106, + 546, + 424, + 557 + ], + "score": 1.0, + "content": "the capability of MAC to perform spatial reasoning without data augmentation.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37, + "bbox_fs": [ + 104, + 478, + 506, + 557 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 561, + 505, + 650 + ], + "lines": [ + { + "bbox": [ + 105, + 561, + 505, + 574 + ], + "spans": [ + { + "bbox": [ + 105, + 561, + 505, + 574 + ], + "score": 1.0, + "content": "Gate Bias Initialization. For our model variant with gating mechanism (described in section 3.2.3)", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 573, + 505, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 382, + 585 + ], + "score": 1.0, + "content": "we have tested the effect of setting different values for the gate bias -", + "type": "text" + }, + { + "bbox": [ + 382, + 573, + 406, + 584 + ], + "score": 0.85, + "content": "- 1 , 0", + "type": "inline_equation" + }, + { + "bbox": [ + 406, + 573, + 448, + 585 + ], + "score": 1.0, + "content": "and 1. for", + "type": "text" + }, + { + "bbox": [ + 448, + 573, + 462, + 583 + ], + "score": 0.66, + "content": "- 1", + "type": "inline_equation" + }, + { + "bbox": [ + 462, + 573, + 505, + 585 + ], + "score": 1.0, + "content": "the model", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 584, + 505, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 505, + 596 + ], + "score": 1.0, + "content": "is initialized to biased for keeping the previous memory value whereas for 1 it will be biased for", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 595, + 505, + 607 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 505, + 607 + ], + "score": 1.0, + "content": "using the new memory instead. We can see that for the complete dataset setting the bias to 1 is", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 606, + 505, + 618 + ], + "spans": [ + { + "bbox": [ + 106, + 606, + 505, + 618 + ], + "score": 1.0, + "content": "optimal – apparently since the model has enough data to learn to apply each cell effectively. In", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 616, + 506, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 616, + 195, + 629 + ], + "score": 1.0, + "content": "contrast, for the small", + "type": "text" + }, + { + "bbox": [ + 196, + 617, + 215, + 627 + ], + "score": 0.85, + "content": "10 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 216, + 616, + 506, + 629 + ], + "score": 1.0, + "content": "CLEVR data, setting the bias to 0 shows better performance, biasing the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 628, + 505, + 640 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 505, + 640 + ], + "score": 1.0, + "content": "model to using less cells overall which results ultimately in a theoretically-simpler model that can", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 638, + 223, + 651 + ], + "spans": [ + { + "bbox": [ + 105, + 638, + 223, + 651 + ], + "score": 1.0, + "content": "fit less data more effectively.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 44.5, + "bbox_fs": [ + 105, + 561, + 506, + 651 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 674, + 213, + 685 + ], + "lines": [ + { + "bbox": [ + 106, + 674, + 215, + 686 + ], + "spans": [ + { + "bbox": [ + 106, + 674, + 215, + 686 + ], + "score": 1.0, + "content": "4.4 INTERPRETABILITY", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 49 + }, + { + "type": "text", + "bbox": [ + 108, + 699, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 698, + 506, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 698, + 506, + 712 + ], + "score": 1.0, + "content": "We have looked into attention maps over the image and question that the model produces during its", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 709, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 505, + 722 + ], + "score": 1.0, + "content": "computation and provide a few examples in figure 4.4. The first example shows us how the model", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 721, + 505, + 733 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 505, + 733 + ], + "score": 1.0, + "content": "parses the question in steps, first focusing on the main entity that the question is about, then on", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 51, + "bbox_fs": [ + 105, + 698, + 506, + 733 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 105, + 82, + 504, + 105 + ], + "lines": [ + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 82, + 505, + 95 + ], + "score": 1.0, + "content": "relation of this entity to the “brown matte thing” which is then located in the image. 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Highway networks. ¨ arXiv preprint", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 115, + 409, + 219, + 420 + ], + "spans": [ + { + "bbox": [ + 115, + 409, + 219, + 420 + ], + "score": 1.0, + "content": "arXiv:1505.00387, 2015.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 23.5, + "bbox_fs": [ + 105, + 397, + 505, + 420 + ] + }, + { + "type": "text", + "bbox": [ + 104, + 428, + 504, + 451 + ], + "lines": [ + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "score": 1.0, + "content": "Bob L Sturm. 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For the image processing, we extract conv4 features from ResNet101 (He et al., 2016) pre-", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 129, + 505, + 141 + ], + "spans": [ + { + "bbox": [ + 105, + 129, + 362, + 141 + ], + "score": 1.0, + "content": "trained on ImageNet (Krizhevsky et al., 2012), with dimension", + "type": "text" + }, + { + "bbox": [ + 362, + 129, + 398, + 141 + ], + "score": 0.78, + "content": "H , W , C", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 129, + 426, + 141 + ], + "score": 1.0, + "content": "where", + "type": "text" + }, + { + "bbox": [ + 426, + 129, + 487, + 140 + ], + "score": 0.9, + "content": "H = W = 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 129, + 505, + 141 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 139, + 506, + 153 + ], + "spans": [ + { + "bbox": [ + 106, + 140, + 149, + 151 + ], + "score": 0.9, + "content": "C = 1 0 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 149, + 139, + 450, + 153 + ], + "score": 1.0, + "content": ", followed by 2 CNN layers with kernel size 2. We use MAC network with", + "type": "text" + }, + { + "bbox": [ + 450, + 141, + 481, + 151 + ], + "score": 0.89, + "content": "p = 1 2", + "type": "inline_equation" + }, + { + "bbox": [ + 481, + 139, + 506, + 153 + ], + "score": 1.0, + "content": "cells,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 104, + 150, + 506, + 163 + ], + "spans": [ + { + "bbox": [ + 104, + 151, + 379, + 163 + ], + "score": 1.0, + "content": "and train it using Adam (Kingma & Ba, 2014), with learning rate", + "type": "text" + }, + { + "bbox": [ + 379, + 150, + 401, + 162 + ], + "score": 0.9, + "content": "1 0 ^ { - 4 }", + "type": "inline_equation" + }, + { + "bbox": [ + 401, + 151, + 506, + 163 + ], + "score": 1.0, + "content": ". We train our model for", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 161, + 506, + 175 + ], + "spans": [ + { + "bbox": [ + 106, + 162, + 141, + 173 + ], + "score": 0.76, + "content": "1 0 - 2 0", + "type": "inline_equation" + }, + { + "bbox": [ + 141, + 161, + 506, + 175 + ], + "score": 1.0, + "content": "epochs, with batch size 64, and use early stopping based on validation accuracies. During", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 173, + 506, + 186 + ], + "spans": [ + { + "bbox": [ + 105, + 173, + 506, + 186 + ], + "score": 1.0, + "content": "training, the moving averages of all weights of the model are maintained with the exponential de-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 106, + 184, + 505, + 197 + ], + "spans": [ + { + "bbox": [ + 106, + 184, + 505, + 197 + ], + "score": 1.0, + "content": "cay rate of 0.999. At test time, the moving averages instead of the raw weights are used. We use", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 195, + 506, + 208 + ], + "spans": [ + { + "bbox": [ + 105, + 195, + 506, + 208 + ], + "score": 1.0, + "content": "dropout 0.85, and ELU (Clevert et al., 2015) which in our experience has reduce the training process", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 206, + 481, + 218 + ], + "spans": [ + { + "bbox": [ + 105, + 206, + 481, + 218 + ], + "score": 1.0, + "content": "compared to RELU.The training process takes roughly 10-20 hours on a single Titan X GPU.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 5.5 + }, + { + "type": "title", + "bbox": [ + 107, + 236, + 342, + 248 + ], + "lines": [ + { + "bbox": [ + 106, + 234, + 344, + 250 + ], + "spans": [ + { + "bbox": [ + 106, + 234, + 344, + 250 + ], + "score": 1.0, + "content": "C FURTHER DISCUSSION OF RELATED WORK", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 11 + }, + { + "type": "text", + "bbox": [ + 107, + 261, + 505, + 328 + ], + "lines": [ + { + "bbox": [ + 105, + 261, + 505, + 273 + ], + "spans": [ + { + "bbox": [ + 105, + 261, + 505, + 273 + ], + "score": 1.0, + "content": "In this section we provide detailed discussion of related work. Several models have been applied", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 271, + 505, + 286 + ], + "spans": [ + { + "bbox": [ + 105, + 271, + 505, + 286 + ], + "score": 1.0, + "content": "to the CLEVR task. These can be partitioned into two groups, module networks that use the strong", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 283, + 505, + 296 + ], + "spans": [ + { + "bbox": [ + 105, + 283, + 505, + 296 + ], + "score": 1.0, + "content": "supervision provided as a tree-structured functional program associated with each instance, and", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 295, + 505, + 306 + ], + "spans": [ + { + "bbox": [ + 106, + 295, + 505, + 306 + ], + "score": 1.0, + "content": "end-to-end, fully differentiable networks that combine a fairly standard stack of CNNs with com-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 306, + 506, + 317 + ], + "spans": [ + { + "bbox": [ + 105, + 306, + 506, + 317 + ], + "score": 1.0, + "content": "ponents that aid them in performing reasoning tasks. We also discuss the relation of MAC to other", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 317, + 353, + 330 + ], + "spans": [ + { + "bbox": [ + 106, + 317, + 353, + 330 + ], + "score": 1.0, + "content": "approaches, such as memory networks and neural computers.", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14.5 + }, + { + "type": "title", + "bbox": [ + 108, + 343, + 224, + 354 + ], + "lines": [ + { + "bbox": [ + 106, + 342, + 226, + 356 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 226, + 356 + ], + "score": 1.0, + "content": "C.1 MODULE NETWORKS", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 364, + 505, + 452 + ], + "lines": [ + { + "bbox": [ + 105, + 364, + 505, + 377 + ], + "spans": [ + { + "bbox": [ + 105, + 364, + 505, + 377 + ], + "score": 1.0, + "content": "The modular approach (Andreas et al., 2016a;b; Hu et al., 2017; Johnson et al., 2017) first translates", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 376, + 505, + 388 + ], + "spans": [ + { + "bbox": [ + 105, + 376, + 505, + 388 + ], + "score": 1.0, + "content": "the given question into a tree-structured action plan, aiming to imitate the ground-truth programs", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 387, + 506, + 398 + ], + "spans": [ + { + "bbox": [ + 105, + 387, + 506, + 398 + ], + "score": 1.0, + "content": "provided as a form of strong-supervision. Then, it constructs a tailor-made network that executes", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 398, + 505, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 398, + 505, + 410 + ], + "score": 1.0, + "content": "the plan on the image in multiple steps. This network is composed of discrete units selected out of", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 104, + 407, + 506, + 421 + ], + "spans": [ + { + "bbox": [ + 104, + 407, + 506, + 421 + ], + "score": 1.0, + "content": "a collection of predefined modules, each responsible for an elementary reasoning operation, such as", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 419, + 506, + 433 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 506, + 433 + ], + "score": 1.0, + "content": "identifying an objects color, filtering them for their shape, or comparing two amounts. Each module", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 431, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 431, + 505, + 443 + ], + "score": 1.0, + "content": "has its own set of learned parameters (Johnson et al., 2017), or even hand-crafted design (Andreas", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 442, + 325, + 453 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 325, + 453 + ], + "score": 1.0, + "content": "et al., 2016a) to guide it towards its intended behavior.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 22.5 + }, + { + "type": "text", + "bbox": [ + 107, + 458, + 505, + 502 + ], + "lines": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "score": 1.0, + "content": "Overall, this approach makes discrete choices at two levels: the identity of each module – the be-", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "spans": [ + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "score": 1.0, + "content": "havior it should learn among a fixed set of possible types of behaviors, and the network layout – the", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "spans": [ + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "score": 1.0, + "content": "way in which these modules are wired together to compute the answer progressively. Hence, their", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 490, + 498, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 490, + 498, + 505 + ], + "score": 1.0, + "content": "differentiability is confined to the boundaries of a single module, disallowing end-to-end training.", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 28.5 + }, + { + "type": "text", + "bbox": [ + 107, + 508, + 505, + 650 + ], + "lines": [ + { + "bbox": [ + 106, + 509, + 505, + 520 + ], + "spans": [ + { + "bbox": [ + 106, + 509, + 505, + 520 + ], + "score": 1.0, + "content": "Several key differences exist between our approaches. First, our model replaces the fixed modules", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 518, + 506, + 532 + ], + "spans": [ + { + "bbox": [ + 105, + 518, + 506, + 532 + ], + "score": 1.0, + "content": "collection with one versatile and universal cell that shares both its architecture and parameters across", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 530, + 505, + 542 + ], + "spans": [ + { + "bbox": [ + 105, + 530, + 505, + 542 + ], + "score": 1.0, + "content": "all of its instantiations, and is applied across all the reasoning steps. Second, it replaces the dynamic", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 541, + 505, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 505, + 554 + ], + "score": 1.0, + "content": "recursive tree structures with a sequential topology, augmented by soft attention mechanisms, as", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 552, + 505, + 564 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 505, + 564 + ], + "score": 1.0, + "content": "done in Bahdanau et al. (2014). This confers our network with a virtual capacity to represent arbi-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 563, + 505, + 575 + ], + "spans": [ + { + "bbox": [ + 106, + 563, + 505, + 575 + ], + "score": 1.0, + "content": "trarily complex Directed Acyclic Graphs (DAGs) while still having efficient and readily deployed", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 574, + 506, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 574, + 506, + 587 + ], + "score": 1.0, + "content": "physical sequential structure. Together, both of these relaxations allow us to effectively train our", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 585, + 505, + 597 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 505, + 597 + ], + "score": 1.0, + "content": "model end-to-end by backpropagation alone, whereas module networks demand a more involved", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "score": 1.0, + "content": "training scheme that relies on the strongly-supervised programs at the first stage, and on various", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 607, + 505, + 619 + ], + "spans": [ + { + "bbox": [ + 105, + 607, + 505, + 619 + ], + "score": 1.0, + "content": "Reinforcement Learning (RL) techniques at the second. Furthermore, while our model can be train", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "score": 1.0, + "content": "without the strong supervisory programs, developing adaptive reasoning skills to address the task", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "score": 1.0, + "content": "is it trained for, the modular approach reliance on questions structured and formal representation", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 639, + 277, + 652 + ], + "spans": [ + { + "bbox": [ + 106, + 639, + 277, + 652 + ], + "score": 1.0, + "content": "hinder its applicability to real-world tasks.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 37 + }, + { + "type": "title", + "bbox": [ + 106, + 666, + 359, + 677 + ], + "lines": [ + { + "bbox": [ + 106, + 665, + 360, + 679 + ], + "spans": [ + { + "bbox": [ + 106, + 665, + 360, + 679 + ], + "score": 1.0, + "content": "C.2 AUGMENTED CONVOLUTIONAL NEURAL NETWORKS", + "type": "text" + } + ], + "index": 44 + } + ], + "index": 44 + }, + { + "type": "text", + "bbox": [ + 108, + 687, + 504, + 731 + ], + "lines": [ + { + "bbox": [ + 105, + 686, + 506, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 686, + 506, + 701 + ], + "score": 1.0, + "content": "Alternative approaches for the CLEVR task that do not rely on the provided programs as a strong", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "score": 1.0, + "content": "supervision signal are Santoro et al. (2017) and Perez et al. (2017). Both complement standard", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 709, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 506, + 723 + ], + "score": 1.0, + "content": "multi-layer Convolutional Neural Networks (CNNs) with components that aid them in handling", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 721, + 265, + 733 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 265, + 733 + ], + "score": 1.0, + "content": "compositional and relational questions.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 46.5 + } + ], + "page_idx": 17, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2018", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 301, + 751, + 311, + 760 + ], + "lines": [ + { + "bbox": [ + 299, + 750, + 312, + 763 + ], + "spans": [ + { + "bbox": [ + 299, + 750, + 312, + 763 + ], + "score": 1.0, + "content": "18", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "title", + "bbox": [ + 107, + 81, + 348, + 94 + ], + "lines": [ + { + "bbox": [ + 105, + 80, + 349, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 80, + 349, + 95 + ], + "score": 1.0, + "content": "B IMPLEMENTATION AND TRAINING DETAILS", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 106, + 107, + 505, + 217 + ], + "lines": [ + { + "bbox": [ + 106, + 107, + 505, + 120 + ], + "spans": [ + { + "bbox": [ + 106, + 107, + 505, + 120 + ], + "score": 1.0, + "content": "For the question processing, we use GloVE (Pennington et al., 2014) word-vectors with dimension", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 117, + 505, + 131 + ], + "spans": [ + { + "bbox": [ + 105, + 117, + 505, + 131 + ], + "score": 1.0, + "content": "300. For the image processing, we extract conv4 features from ResNet101 (He et al., 2016) pre-", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 129, + 505, + 141 + ], + "spans": [ + { + "bbox": [ + 105, + 129, + 362, + 141 + ], + "score": 1.0, + "content": "trained on ImageNet (Krizhevsky et al., 2012), with dimension", + "type": "text" + }, + { + "bbox": [ + 362, + 129, + 398, + 141 + ], + "score": 0.78, + "content": "H , W , C", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 129, + 426, + 141 + ], + "score": 1.0, + "content": "where", + "type": "text" + }, + { + "bbox": [ + 426, + 129, + 487, + 140 + ], + "score": 0.9, + "content": "H = W = 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 129, + 505, + 141 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 139, + 506, + 153 + ], + "spans": [ + { + "bbox": [ + 106, + 140, + 149, + 151 + ], + "score": 0.9, + "content": "C = 1 0 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 149, + 139, + 450, + 153 + ], + "score": 1.0, + "content": ", followed by 2 CNN layers with kernel size 2. We use MAC network with", + "type": "text" + }, + { + "bbox": [ + 450, + 141, + 481, + 151 + ], + "score": 0.89, + "content": "p = 1 2", + "type": "inline_equation" + }, + { + "bbox": [ + 481, + 139, + 506, + 153 + ], + "score": 1.0, + "content": "cells,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 104, + 150, + 506, + 163 + ], + "spans": [ + { + "bbox": [ + 104, + 151, + 379, + 163 + ], + "score": 1.0, + "content": "and train it using Adam (Kingma & Ba, 2014), with learning rate", + "type": "text" + }, + { + "bbox": [ + 379, + 150, + 401, + 162 + ], + "score": 0.9, + "content": "1 0 ^ { - 4 }", + "type": "inline_equation" + }, + { + "bbox": [ + 401, + 151, + 506, + 163 + ], + "score": 1.0, + "content": ". We train our model for", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 161, + 506, + 175 + ], + "spans": [ + { + "bbox": [ + 106, + 162, + 141, + 173 + ], + "score": 0.76, + "content": "1 0 - 2 0", + "type": "inline_equation" + }, + { + "bbox": [ + 141, + 161, + 506, + 175 + ], + "score": 1.0, + "content": "epochs, with batch size 64, and use early stopping based on validation accuracies. During", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 173, + 506, + 186 + ], + "spans": [ + { + "bbox": [ + 105, + 173, + 506, + 186 + ], + "score": 1.0, + "content": "training, the moving averages of all weights of the model are maintained with the exponential de-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 106, + 184, + 505, + 197 + ], + "spans": [ + { + "bbox": [ + 106, + 184, + 505, + 197 + ], + "score": 1.0, + "content": "cay rate of 0.999. At test time, the moving averages instead of the raw weights are used. We use", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 195, + 506, + 208 + ], + "spans": [ + { + "bbox": [ + 105, + 195, + 506, + 208 + ], + "score": 1.0, + "content": "dropout 0.85, and ELU (Clevert et al., 2015) which in our experience has reduce the training process", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 206, + 481, + 218 + ], + "spans": [ + { + "bbox": [ + 105, + 206, + 481, + 218 + ], + "score": 1.0, + "content": "compared to RELU.The training process takes roughly 10-20 hours on a single Titan X GPU.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 5.5, + "bbox_fs": [ + 104, + 107, + 506, + 218 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 236, + 342, + 248 + ], + "lines": [ + { + "bbox": [ + 106, + 234, + 344, + 250 + ], + "spans": [ + { + "bbox": [ + 106, + 234, + 344, + 250 + ], + "score": 1.0, + "content": "C FURTHER DISCUSSION OF RELATED WORK", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 11 + }, + { + "type": "text", + "bbox": [ + 107, + 261, + 505, + 328 + ], + "lines": [ + { + "bbox": [ + 105, + 261, + 505, + 273 + ], + "spans": [ + { + "bbox": [ + 105, + 261, + 505, + 273 + ], + "score": 1.0, + "content": "In this section we provide detailed discussion of related work. Several models have been applied", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 271, + 505, + 286 + ], + "spans": [ + { + "bbox": [ + 105, + 271, + 505, + 286 + ], + "score": 1.0, + "content": "to the CLEVR task. These can be partitioned into two groups, module networks that use the strong", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 283, + 505, + 296 + ], + "spans": [ + { + "bbox": [ + 105, + 283, + 505, + 296 + ], + "score": 1.0, + "content": "supervision provided as a tree-structured functional program associated with each instance, and", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 295, + 505, + 306 + ], + "spans": [ + { + "bbox": [ + 106, + 295, + 505, + 306 + ], + "score": 1.0, + "content": "end-to-end, fully differentiable networks that combine a fairly standard stack of CNNs with com-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 306, + 506, + 317 + ], + "spans": [ + { + "bbox": [ + 105, + 306, + 506, + 317 + ], + "score": 1.0, + "content": "ponents that aid them in performing reasoning tasks. We also discuss the relation of MAC to other", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 317, + 353, + 330 + ], + "spans": [ + { + "bbox": [ + 106, + 317, + 353, + 330 + ], + "score": 1.0, + "content": "approaches, such as memory networks and neural computers.", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14.5, + "bbox_fs": [ + 105, + 261, + 506, + 330 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 343, + 224, + 354 + ], + "lines": [ + { + "bbox": [ + 106, + 342, + 226, + 356 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 226, + 356 + ], + "score": 1.0, + "content": "C.1 MODULE NETWORKS", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 364, + 505, + 452 + ], + "lines": [ + { + "bbox": [ + 105, + 364, + 505, + 377 + ], + "spans": [ + { + "bbox": [ + 105, + 364, + 505, + 377 + ], + "score": 1.0, + "content": "The modular approach (Andreas et al., 2016a;b; Hu et al., 2017; Johnson et al., 2017) first translates", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 376, + 505, + 388 + ], + "spans": [ + { + "bbox": [ + 105, + 376, + 505, + 388 + ], + "score": 1.0, + "content": "the given question into a tree-structured action plan, aiming to imitate the ground-truth programs", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 387, + 506, + 398 + ], + "spans": [ + { + "bbox": [ + 105, + 387, + 506, + 398 + ], + "score": 1.0, + "content": "provided as a form of strong-supervision. Then, it constructs a tailor-made network that executes", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 398, + 505, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 398, + 505, + 410 + ], + "score": 1.0, + "content": "the plan on the image in multiple steps. This network is composed of discrete units selected out of", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 104, + 407, + 506, + 421 + ], + "spans": [ + { + "bbox": [ + 104, + 407, + 506, + 421 + ], + "score": 1.0, + "content": "a collection of predefined modules, each responsible for an elementary reasoning operation, such as", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 419, + 506, + 433 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 506, + 433 + ], + "score": 1.0, + "content": "identifying an objects color, filtering them for their shape, or comparing two amounts. Each module", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 431, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 105, + 431, + 505, + 443 + ], + "score": 1.0, + "content": "has its own set of learned parameters (Johnson et al., 2017), or even hand-crafted design (Andreas", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 442, + 325, + 453 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 325, + 453 + ], + "score": 1.0, + "content": "et al., 2016a) to guide it towards its intended behavior.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 22.5, + "bbox_fs": [ + 104, + 364, + 506, + 453 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 458, + 505, + 502 + ], + "lines": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "score": 1.0, + "content": "Overall, this approach makes discrete choices at two levels: the identity of each module – the be-", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "spans": [ + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "score": 1.0, + "content": "havior it should learn among a fixed set of possible types of behaviors, and the network layout – the", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "spans": [ + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "score": 1.0, + "content": "way in which these modules are wired together to compute the answer progressively. Hence, their", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 490, + 498, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 490, + 498, + 505 + ], + "score": 1.0, + "content": "differentiability is confined to the boundaries of a single module, disallowing end-to-end training.", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 28.5, + "bbox_fs": [ + 105, + 459, + 505, + 505 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 508, + 505, + 650 + ], + "lines": [ + { + "bbox": [ + 106, + 509, + 505, + 520 + ], + "spans": [ + { + "bbox": [ + 106, + 509, + 505, + 520 + ], + "score": 1.0, + "content": "Several key differences exist between our approaches. First, our model replaces the fixed modules", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 518, + 506, + 532 + ], + "spans": [ + { + "bbox": [ + 105, + 518, + 506, + 532 + ], + "score": 1.0, + "content": "collection with one versatile and universal cell that shares both its architecture and parameters across", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 530, + 505, + 542 + ], + "spans": [ + { + "bbox": [ + 105, + 530, + 505, + 542 + ], + "score": 1.0, + "content": "all of its instantiations, and is applied across all the reasoning steps. Second, it replaces the dynamic", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 541, + 505, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 505, + 554 + ], + "score": 1.0, + "content": "recursive tree structures with a sequential topology, augmented by soft attention mechanisms, as", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 552, + 505, + 564 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 505, + 564 + ], + "score": 1.0, + "content": "done in Bahdanau et al. (2014). This confers our network with a virtual capacity to represent arbi-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 563, + 505, + 575 + ], + "spans": [ + { + "bbox": [ + 106, + 563, + 505, + 575 + ], + "score": 1.0, + "content": "trarily complex Directed Acyclic Graphs (DAGs) while still having efficient and readily deployed", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 574, + 506, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 574, + 506, + 587 + ], + "score": 1.0, + "content": "physical sequential structure. Together, both of these relaxations allow us to effectively train our", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 585, + 505, + 597 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 505, + 597 + ], + "score": 1.0, + "content": "model end-to-end by backpropagation alone, whereas module networks demand a more involved", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "score": 1.0, + "content": "training scheme that relies on the strongly-supervised programs at the first stage, and on various", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 607, + 505, + 619 + ], + "spans": [ + { + "bbox": [ + 105, + 607, + 505, + 619 + ], + "score": 1.0, + "content": "Reinforcement Learning (RL) techniques at the second. Furthermore, while our model can be train", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "score": 1.0, + "content": "without the strong supervisory programs, developing adaptive reasoning skills to address the task", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 505, + 642 + ], + "score": 1.0, + "content": "is it trained for, the modular approach reliance on questions structured and formal representation", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 639, + 277, + 652 + ], + "spans": [ + { + "bbox": [ + 106, + 639, + 277, + 652 + ], + "score": 1.0, + "content": "hinder its applicability to real-world tasks.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 37, + "bbox_fs": [ + 105, + 509, + 506, + 652 + ] + }, + { + "type": "title", + "bbox": [ + 106, + 666, + 359, + 677 + ], + "lines": [ + { + "bbox": [ + 106, + 665, + 360, + 679 + ], + "spans": [ + { + "bbox": [ + 106, + 665, + 360, + 679 + ], + "score": 1.0, + "content": "C.2 AUGMENTED CONVOLUTIONAL NEURAL NETWORKS", + "type": "text" + } + ], + "index": 44 + } + ], + "index": 44 + }, + { + "type": "text", + "bbox": [ + 108, + 687, + 504, + 731 + ], + "lines": [ + { + "bbox": [ + 105, + 686, + 506, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 686, + 506, + 701 + ], + "score": 1.0, + "content": "Alternative approaches for the CLEVR task that do not rely on the provided programs as a strong", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "score": 1.0, + "content": "supervision signal are Santoro et al. (2017) and Perez et al. (2017). Both complement standard", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 709, + 506, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 506, + 723 + ], + "score": 1.0, + "content": "multi-layer Convolutional Neural Networks (CNNs) with components that aid them in handling", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 721, + 265, + 733 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 265, + 733 + ], + "score": 1.0, + "content": "compositional and relational questions.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 46.5, + "bbox_fs": [ + 105, + 686, + 506, + 733 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 214 + ], + "lines": [ + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "score": 1.0, + "content": "Relational Networks. Santoro et al. (2017) appends a Relation Network (RN) layer to the CNN.", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "score": 1.0, + "content": "This layer inspects all pairs of pixels in the image, thereby enhancing the network capacity to reason", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "score": 1.0, + "content": "over binary relations between objects. While this approach is very simple and elegant conceptually,", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 104, + 114, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 104, + 114, + 505, + 128 + ], + "score": 1.0, + "content": "it suffers from quadratic computational complexity, in contrast to our and other leading approaches.", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "score": 1.0, + "content": "But beyond that, closer inspection reveals that this direct pairwise comparison might be unnecessary.", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 136, + 506, + 151 + ], + "spans": [ + { + "bbox": [ + 105, + 136, + 506, + 151 + ], + "score": 1.0, + "content": "Based on the analogy suggested by Santoro et al. (2017), according to which pixels are equivalent to", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "spans": [ + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "score": 1.0, + "content": "objects and their pairwise interactions to relations, a RN layer attempts to grasp the induced graph", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 159, + 505, + 171 + ], + "spans": [ + { + "bbox": [ + 105, + 159, + 505, + 171 + ], + "score": 1.0, + "content": "between objects all at once in one shallow and broad layer. Conversely, our attention-based model", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 169, + 506, + 185 + ], + "spans": [ + { + "bbox": [ + 105, + 169, + 506, + 185 + ], + "score": 1.0, + "content": "proceeds in steps. It basically compares the image to its current memory and control for this step,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 181, + 505, + 195 + ], + "spans": [ + { + "bbox": [ + 105, + 181, + 505, + 195 + ], + "score": 1.0, + "content": "aggregates the attended regions into the new memory, and repeats the process. By the same analogy,", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 192, + 506, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 192, + 506, + 205 + ], + "score": 1.0, + "content": "it traverses a narrow and deep path, progressively following transitive relations. Consequently, our", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 202, + 460, + 217 + ], + "spans": [ + { + "bbox": [ + 105, + 202, + 460, + 217 + ], + "score": 1.0, + "content": "model exhibits a relational capacity while circumventing the computational inefficiency.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 5.5 + }, + { + "type": "text", + "bbox": [ + 107, + 219, + 505, + 286 + ], + "lines": [ + { + "bbox": [ + 105, + 220, + 505, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 505, + 233 + ], + "score": 1.0, + "content": "FiLM. FiLM (Perez et al., 2017) is a recently proposed method that interleaves standard CNN", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 231, + 505, + 244 + ], + "spans": [ + { + "bbox": [ + 105, + 231, + 505, + 244 + ], + "score": 1.0, + "content": "layers that process the given image with linear layers, reminiscent of layer normalization techniques", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 242, + 505, + 254 + ], + "spans": [ + { + "bbox": [ + 106, + 242, + 505, + 254 + ], + "score": 1.0, + "content": "(Ba et al., 2016; Ioffe & Szegedy, 2015). Each of these layers, called FiLM, is conditioned on", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 253, + 505, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 505, + 266 + ], + "score": 1.0, + "content": "the question: the question words are processed by a GRU, and its output is linearly transformed", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 263, + 505, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 263, + 505, + 277 + ], + "score": 1.0, + "content": "into matching biases and variances for each of the CNN layers, tilting its activations to reflect the", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 274, + 424, + 289 + ], + "spans": [ + { + "bbox": [ + 105, + 274, + 424, + 289 + ], + "score": 1.0, + "content": "specifics of the given question and affect the computation done over the image.", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14.5 + }, + { + "type": "text", + "bbox": [ + 107, + 291, + 505, + 468 + ], + "lines": [ + { + "bbox": [ + 106, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 106, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "Similarly to our model, this approach features distant modulation between the question and the", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 303, + 505, + 316 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 505, + 316 + ], + "score": 1.0, + "content": "image, where rather than being fused together into the same vector space, the question can affect the", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 314, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 314, + 505, + 326 + ], + "score": 1.0, + "content": "image processing only through constrained means – for the case of FiLM – linear transformations.", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 324, + 505, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 505, + 337 + ], + "score": 1.0, + "content": "However, since the same transformation is applied to all the activations homogeneously, agnostic to", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 336, + 505, + 348 + ], + "spans": [ + { + "bbox": [ + 105, + 336, + 505, + 348 + ], + "score": 1.0, + "content": "both their spatial location as well as the features values, this approach does not allow the question", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 346, + 506, + 360 + ], + "spans": [ + { + "bbox": [ + 105, + 346, + 506, + 360 + ], + "score": 1.0, + "content": "to differentiate between regions in the image based on the objects or concepts they represent – on", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "score": 1.0, + "content": "the content of the image. This stands in stark contrast to our attention-based model, which readily", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 368, + 505, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 368, + 505, + 381 + ], + "score": 1.0, + "content": "allows and actually encourages the question to inform the model about relevant regions to focus", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 379, + 505, + 392 + ], + "spans": [ + { + "bbox": [ + 105, + 379, + 505, + 392 + ], + "score": 1.0, + "content": "on. We speculate that this still distant, yet more direct interaction between the question and the", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 390, + 505, + 403 + ], + "spans": [ + { + "bbox": [ + 105, + 390, + 505, + 403 + ], + "score": 1.0, + "content": "data, or image, for the case of VQA, facilitates learning and increases generalizability. It may be", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 402, + 505, + 414 + ], + "spans": [ + { + "bbox": [ + 105, + 402, + 505, + 414 + ], + "score": 1.0, + "content": "more suitable to VQA tasks, and CLEVR in particular, where the questions demand the responder", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "score": 1.0, + "content": "to focus on specific objects, and reason about their properties or relations, rather than respond based", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 424, + 506, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 506, + 436 + ], + "score": 1.0, + "content": "only on a holistic view of the image that may lead to sub-optimal results (Yang et al., 2016), as is", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 434, + 505, + 447 + ], + "spans": [ + { + "bbox": [ + 105, + 434, + 505, + 447 + ], + "score": 1.0, + "content": "the case of FiLM. Indeed, as demonstrated in 4, there is significant evidence showing our models", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 445, + 506, + 458 + ], + "spans": [ + { + "bbox": [ + 105, + 445, + 506, + 458 + ], + "score": 1.0, + "content": "better generalization capacity, allowing it to achieve high accuracies much faster, and from less data", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 456, + 275, + 469 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 275, + 469 + ], + "score": 1.0, + "content": "than FiLM and other competing methods.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 25.5 + }, + { + "type": "title", + "bbox": [ + 108, + 500, + 246, + 511 + ], + "lines": [ + { + "bbox": [ + 106, + 500, + 249, + 512 + ], + "spans": [ + { + "bbox": [ + 106, + 500, + 249, + 512 + ], + "score": 1.0, + "content": "C.3 MEMORY AND ATTENTION", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 107, + 528, + 505, + 627 + ], + "lines": [ + { + "bbox": [ + 106, + 528, + 504, + 540 + ], + "spans": [ + { + "bbox": [ + 106, + 528, + 504, + 540 + ], + "score": 1.0, + "content": "Our architecture draws inspiration from recent research on memory and attention (Kumar et al.,", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 538, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 505, + 552 + ], + "score": 1.0, + "content": "2016; Xiong et al., 2016; Graves et al., 2014; 2016). Kumar et al. (2016); Xiong et al. (2016)", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 104, + 550, + 506, + 563 + ], + "spans": [ + { + "bbox": [ + 104, + 550, + 506, + 563 + ], + "score": 1.0, + "content": "propose the Dynamic Memory Network model that proceeds in an iterative process, applying soft", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 561, + 505, + 573 + ], + "spans": [ + { + "bbox": [ + 105, + 561, + 505, + 573 + ], + "score": 1.0, + "content": "attention to retrieve relevant information from a visual or textual KB, which is in turn accumulated", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 572, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 506, + 585 + ], + "score": 1.0, + "content": "into memory passed from one iteration to the next. However, in contrast to our model, it views the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 582, + 505, + 597 + ], + "spans": [ + { + "bbox": [ + 105, + 582, + 505, + 597 + ], + "score": 1.0, + "content": "question as an atomic unit, whereas our model decomposes it into a multi-step action plan informing", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 595, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 106, + 595, + 505, + 606 + ], + "score": 1.0, + "content": "each cell in our sequential network about its current objective. Another key difference is the distant", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 604, + 505, + 618 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 505, + 618 + ], + "score": 1.0, + "content": "interaction between the question and the KB that characterizes our model. Conversely, DMN fuses", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 615, + 394, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 394, + 630 + ], + "score": 1.0, + "content": "their corresponding representations together into the same vector space.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 39 + }, + { + "type": "text", + "bbox": [ + 107, + 633, + 505, + 731 + ], + "lines": [ + { + "bbox": [ + 106, + 633, + 504, + 645 + ], + "spans": [ + { + "bbox": [ + 106, + 633, + 504, + 645 + ], + "score": 1.0, + "content": "Graves et al. (2016; 2014) complements a neural network with a memory array it can interact with,", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 644, + 505, + 656 + ], + "spans": [ + { + "bbox": [ + 106, + 644, + 505, + 656 + ], + "score": 1.0, + "content": "through the means of soft attention. Analogously to our model, it partitions the model into a core", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "score": 1.0, + "content": "neural network, called controller, as well as reading and writing heads that interact with external", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 666, + 505, + 678 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 505, + 678 + ], + "score": 1.0, + "content": "memory array. However, a main point distinguishing our model from this approach, is the use of", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 677, + 506, + 690 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 690 + ], + "score": 1.0, + "content": "dynamic memory, as in Kumar et al. (2016), instead of a fixed-array memory. Each MAC cell is", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "score": 1.0, + "content": "associated with a memory state, our reading unit inspects only the latest memory passed from the", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 699, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 505, + 712 + ], + "score": 1.0, + "content": "previous state, and our writing unit creates a new memory state rather than writing to multiple slots", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "score": 1.0, + "content": "in a fixed shared external memory. Notably, our approach is much more reminiscent of the widely", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 721, + 380, + 732 + ], + "spans": [ + { + "bbox": [ + 105, + 721, + 380, + 732 + ], + "score": 1.0, + "content": "successful RNN structure, rather than to Graves et al. (2016; 2014) .", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 48 + } + ], + "page_idx": 18, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 294, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2018", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 301, + 752, + 311, + 760 + ], + "lines": [ + { + "bbox": [ + 299, + 750, + 312, + 765 + ], + "spans": [ + { + "bbox": [ + 299, + 750, + 312, + 765 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 15, + "width": 13 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 214 + ], + "lines": [ + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "score": 1.0, + "content": "Relational Networks. Santoro et al. (2017) appends a Relation Network (RN) layer to the CNN.", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "score": 1.0, + "content": "This layer inspects all pairs of pixels in the image, thereby enhancing the network capacity to reason", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "score": 1.0, + "content": "over binary relations between objects. While this approach is very simple and elegant conceptually,", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 104, + 114, + 505, + 128 + ], + "spans": [ + { + "bbox": [ + 104, + 114, + 505, + 128 + ], + "score": 1.0, + "content": "it suffers from quadratic computational complexity, in contrast to our and other leading approaches.", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 126, + 506, + 140 + ], + "score": 1.0, + "content": "But beyond that, closer inspection reveals that this direct pairwise comparison might be unnecessary.", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 136, + 506, + 151 + ], + "spans": [ + { + "bbox": [ + 105, + 136, + 506, + 151 + ], + "score": 1.0, + "content": "Based on the analogy suggested by Santoro et al. (2017), according to which pixels are equivalent to", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "spans": [ + { + "bbox": [ + 105, + 148, + 505, + 161 + ], + "score": 1.0, + "content": "objects and their pairwise interactions to relations, a RN layer attempts to grasp the induced graph", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 159, + 505, + 171 + ], + "spans": [ + { + "bbox": [ + 105, + 159, + 505, + 171 + ], + "score": 1.0, + "content": "between objects all at once in one shallow and broad layer. Conversely, our attention-based model", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 169, + 506, + 185 + ], + "spans": [ + { + "bbox": [ + 105, + 169, + 506, + 185 + ], + "score": 1.0, + "content": "proceeds in steps. It basically compares the image to its current memory and control for this step,", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 181, + 505, + 195 + ], + "spans": [ + { + "bbox": [ + 105, + 181, + 505, + 195 + ], + "score": 1.0, + "content": "aggregates the attended regions into the new memory, and repeats the process. By the same analogy,", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 192, + 506, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 192, + 506, + 205 + ], + "score": 1.0, + "content": "it traverses a narrow and deep path, progressively following transitive relations. Consequently, our", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 202, + 460, + 217 + ], + "spans": [ + { + "bbox": [ + 105, + 202, + 460, + 217 + ], + "score": 1.0, + "content": "model exhibits a relational capacity while circumventing the computational inefficiency.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 5.5, + "bbox_fs": [ + 104, + 83, + 506, + 217 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 219, + 505, + 286 + ], + "lines": [ + { + "bbox": [ + 105, + 220, + 505, + 233 + ], + "spans": [ + { + "bbox": [ + 105, + 220, + 505, + 233 + ], + "score": 1.0, + "content": "FiLM. FiLM (Perez et al., 2017) is a recently proposed method that interleaves standard CNN", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 231, + 505, + 244 + ], + "spans": [ + { + "bbox": [ + 105, + 231, + 505, + 244 + ], + "score": 1.0, + "content": "layers that process the given image with linear layers, reminiscent of layer normalization techniques", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 242, + 505, + 254 + ], + "spans": [ + { + "bbox": [ + 106, + 242, + 505, + 254 + ], + "score": 1.0, + "content": "(Ba et al., 2016; Ioffe & Szegedy, 2015). Each of these layers, called FiLM, is conditioned on", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 253, + 505, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 505, + 266 + ], + "score": 1.0, + "content": "the question: the question words are processed by a GRU, and its output is linearly transformed", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 263, + 505, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 263, + 505, + 277 + ], + "score": 1.0, + "content": "into matching biases and variances for each of the CNN layers, tilting its activations to reflect the", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 274, + 424, + 289 + ], + "spans": [ + { + "bbox": [ + 105, + 274, + 424, + 289 + ], + "score": 1.0, + "content": "specifics of the given question and affect the computation done over the image.", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 14.5, + "bbox_fs": [ + 105, + 220, + 505, + 289 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 291, + 505, + 468 + ], + "lines": [ + { + "bbox": [ + 106, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 106, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "Similarly to our model, this approach features distant modulation between the question and the", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 303, + 505, + 316 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 505, + 316 + ], + "score": 1.0, + "content": "image, where rather than being fused together into the same vector space, the question can affect the", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 314, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 314, + 505, + 326 + ], + "score": 1.0, + "content": "image processing only through constrained means – for the case of FiLM – linear transformations.", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 324, + 505, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 505, + 337 + ], + "score": 1.0, + "content": "However, since the same transformation is applied to all the activations homogeneously, agnostic to", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 336, + 505, + 348 + ], + "spans": [ + { + "bbox": [ + 105, + 336, + 505, + 348 + ], + "score": 1.0, + "content": "both their spatial location as well as the features values, this approach does not allow the question", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 346, + 506, + 360 + ], + "spans": [ + { + "bbox": [ + 105, + 346, + 506, + 360 + ], + "score": 1.0, + "content": "to differentiate between regions in the image based on the objects or concepts they represent – on", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 357, + 505, + 370 + ], + "score": 1.0, + "content": "the content of the image. This stands in stark contrast to our attention-based model, which readily", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 368, + 505, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 368, + 505, + 381 + ], + "score": 1.0, + "content": "allows and actually encourages the question to inform the model about relevant regions to focus", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 379, + 505, + 392 + ], + "spans": [ + { + "bbox": [ + 105, + 379, + 505, + 392 + ], + "score": 1.0, + "content": "on. We speculate that this still distant, yet more direct interaction between the question and the", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 390, + 505, + 403 + ], + "spans": [ + { + "bbox": [ + 105, + 390, + 505, + 403 + ], + "score": 1.0, + "content": "data, or image, for the case of VQA, facilitates learning and increases generalizability. It may be", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 402, + 505, + 414 + ], + "spans": [ + { + "bbox": [ + 105, + 402, + 505, + 414 + ], + "score": 1.0, + "content": "more suitable to VQA tasks, and CLEVR in particular, where the questions demand the responder", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "spans": [ + { + "bbox": [ + 105, + 412, + 505, + 425 + ], + "score": 1.0, + "content": "to focus on specific objects, and reason about their properties or relations, rather than respond based", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 424, + 506, + 436 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 506, + 436 + ], + "score": 1.0, + "content": "only on a holistic view of the image that may lead to sub-optimal results (Yang et al., 2016), as is", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 434, + 505, + 447 + ], + "spans": [ + { + "bbox": [ + 105, + 434, + 505, + 447 + ], + "score": 1.0, + "content": "the case of FiLM. Indeed, as demonstrated in 4, there is significant evidence showing our models", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 445, + 506, + 458 + ], + "spans": [ + { + "bbox": [ + 105, + 445, + 506, + 458 + ], + "score": 1.0, + "content": "better generalization capacity, allowing it to achieve high accuracies much faster, and from less data", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 456, + 275, + 469 + ], + "spans": [ + { + "bbox": [ + 105, + 456, + 275, + 469 + ], + "score": 1.0, + "content": "than FiLM and other competing methods.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 25.5, + "bbox_fs": [ + 105, + 291, + 506, + 469 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 500, + 246, + 511 + ], + "lines": [ + { + "bbox": [ + 106, + 500, + 249, + 512 + ], + "spans": [ + { + "bbox": [ + 106, + 500, + 249, + 512 + ], + "score": 1.0, + "content": "C.3 MEMORY AND ATTENTION", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 107, + 528, + 505, + 627 + ], + "lines": [ + { + "bbox": [ + 106, + 528, + 504, + 540 + ], + "spans": [ + { + "bbox": [ + 106, + 528, + 504, + 540 + ], + "score": 1.0, + "content": "Our architecture draws inspiration from recent research on memory and attention (Kumar et al.,", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 538, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 505, + 552 + ], + "score": 1.0, + "content": "2016; Xiong et al., 2016; Graves et al., 2014; 2016). Kumar et al. (2016); Xiong et al. (2016)", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 104, + 550, + 506, + 563 + ], + "spans": [ + { + "bbox": [ + 104, + 550, + 506, + 563 + ], + "score": 1.0, + "content": "propose the Dynamic Memory Network model that proceeds in an iterative process, applying soft", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 561, + 505, + 573 + ], + "spans": [ + { + "bbox": [ + 105, + 561, + 505, + 573 + ], + "score": 1.0, + "content": "attention to retrieve relevant information from a visual or textual KB, which is in turn accumulated", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 572, + 506, + 585 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 506, + 585 + ], + "score": 1.0, + "content": "into memory passed from one iteration to the next. However, in contrast to our model, it views the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 582, + 505, + 597 + ], + "spans": [ + { + "bbox": [ + 105, + 582, + 505, + 597 + ], + "score": 1.0, + "content": "question as an atomic unit, whereas our model decomposes it into a multi-step action plan informing", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 595, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 106, + 595, + 505, + 606 + ], + "score": 1.0, + "content": "each cell in our sequential network about its current objective. Another key difference is the distant", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 604, + 505, + 618 + ], + "spans": [ + { + "bbox": [ + 105, + 604, + 505, + 618 + ], + "score": 1.0, + "content": "interaction between the question and the KB that characterizes our model. Conversely, DMN fuses", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 615, + 394, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 394, + 630 + ], + "score": 1.0, + "content": "their corresponding representations together into the same vector space.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 39, + "bbox_fs": [ + 104, + 528, + 506, + 630 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 633, + 505, + 731 + ], + "lines": [ + { + "bbox": [ + 106, + 633, + 504, + 645 + ], + "spans": [ + { + "bbox": [ + 106, + 633, + 504, + 645 + ], + "score": 1.0, + "content": "Graves et al. (2016; 2014) complements a neural network with a memory array it can interact with,", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 644, + 505, + 656 + ], + "spans": [ + { + "bbox": [ + 106, + 644, + 505, + 656 + ], + "score": 1.0, + "content": "through the means of soft attention. Analogously to our model, it partitions the model into a core", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 505, + 667 + ], + "score": 1.0, + "content": "neural network, called controller, as well as reading and writing heads that interact with external", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 666, + 505, + 678 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 505, + 678 + ], + "score": 1.0, + "content": "memory array. However, a main point distinguishing our model from this approach, is the use of", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 677, + 506, + 690 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 690 + ], + "score": 1.0, + "content": "dynamic memory, as in Kumar et al. (2016), instead of a fixed-array memory. Each MAC cell is", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 505, + 700 + ], + "score": 1.0, + "content": "associated with a memory state, our reading unit inspects only the latest memory passed from the", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 699, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 505, + 712 + ], + "score": 1.0, + "content": "previous state, and our writing unit creates a new memory state rather than writing to multiple slots", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "score": 1.0, + "content": "in a fixed shared external memory. Notably, our approach is much more reminiscent of the widely", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 721, + 380, + 732 + ], + "spans": [ + { + "bbox": [ + 105, + 721, + 380, + 732 + ], + "score": 1.0, + "content": "successful RNN structure, rather than to Graves et al. (2016; 2014) .", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 48, + "bbox_fs": [ + 105, + 633, + 506, + 732 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 505, + 149 + ], + "lines": [ + { + "bbox": [ + 106, + 83, + 505, + 94 + ], + "spans": [ + { + "bbox": [ + 106, + 83, + 505, + 94 + ], + "score": 1.0, + "content": "Finally, our approach has potential ties to the VQA models Hu et al. (2017); Lu et al. 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