diff --git "a/parse/dev/3KWnuT-R1bh/3KWnuT-R1bh_middle.json" "b/parse/dev/3KWnuT-R1bh/3KWnuT-R1bh_middle.json" new file mode 100644--- /dev/null +++ "b/parse/dev/3KWnuT-R1bh/3KWnuT-R1bh_middle.json" @@ -0,0 +1,47155 @@ +{ + "pdf_info": [ + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 108, + 78, + 502, + 115 + ], + "lines": [ + { + "bbox": [ + 106, + 79, + 505, + 97 + ], + "spans": [ + { + "bbox": [ + 106, + 79, + 505, + 97 + ], + "score": 1.0, + "content": "CONDITIONAL POSITIONAL ENCODINGS FOR VISION", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 98, + 231, + 117 + ], + "spans": [ + { + "bbox": [ + 106, + 98, + 231, + 117 + ], + "score": 1.0, + "content": "TRANSFORMERS", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "text", + "bbox": [ + 112, + 134, + 452, + 180 + ], + "lines": [ + { + "bbox": [ + 111, + 133, + 455, + 148 + ], + "spans": [ + { + "bbox": [ + 111, + 133, + 163, + 148 + ], + "score": 1.0, + "content": "Xiangxiang", + "type": "text" + }, + { + "bbox": [ + 164, + 135, + 187, + 146 + ], + "score": 0.75, + "content": "\\mathbf { C h u ^ { 1 } }", + "type": "inline_equation" + }, + { + "bbox": [ + 188, + 133, + 455, + 148 + ], + "score": 1.0, + "content": ", Zhi Tian1, Bo Zhang1, Xinlong Wang2, Chunhua Shen3∗", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 110, + 143, + 411, + 162 + ], + "spans": [ + { + "bbox": [ + 110, + 143, + 411, + 162 + ], + "score": 1.0, + "content": "1 Meituan Inc. 2 Beijing Academy of AI 3 Zhejiang University, China", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 112, + 158, + 380, + 171 + ], + "spans": [ + { + "bbox": [ + 112, + 158, + 380, + 171 + ], + "score": 1.0, + "content": "{chuxiangxiang, tianzhi02, zhangbo97}@meituan.com,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 112, + 170, + 330, + 181 + ], + "spans": [ + { + "bbox": [ + 112, + 170, + 330, + 181 + ], + "score": 1.0, + "content": "xinlong.wang96@gmail.com, chunhua@me.com", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3.5 + }, + { + "type": "title", + "bbox": [ + 278, + 209, + 333, + 221 + ], + "lines": [ + { + "bbox": [ + 276, + 209, + 335, + 222 + ], + "spans": [ + { + "bbox": [ + 276, + 209, + 335, + 222 + ], + "score": 1.0, + "content": "ABSTRACT", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 6 + }, + { + "type": "text", + "bbox": [ + 143, + 235, + 468, + 377 + ], + "lines": [ + { + "bbox": [ + 142, + 235, + 470, + 248 + ], + "spans": [ + { + "bbox": [ + 142, + 235, + 470, + 248 + ], + "score": 1.0, + "content": "We propose a conditional positional encoding (CPE) scheme for vision Trans-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 142, + 246, + 469, + 258 + ], + "spans": [ + { + "bbox": [ + 142, + 246, + 469, + 258 + ], + "score": 1.0, + "content": "formers (Dosovitskiy et al., 2021; Touvron et al., 2020). 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We implement CPE with a simple Position Encoding Generator (PEG)", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 141, + 323, + 470, + 335 + ], + "spans": [ + { + "bbox": [ + 141, + 323, + 470, + 335 + ], + "score": 1.0, + "content": "to get seamlessly incorporated into the current Transformer framework. Built on", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 141, + 333, + 470, + 346 + ], + "spans": [ + { + "bbox": [ + 141, + 333, + 470, + 346 + ], + "score": 1.0, + "content": "PEG, we present Conditional Position encoding Vision Transformer (CPVT). We", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 141, + 344, + 469, + 357 + ], + "spans": [ + { + "bbox": [ + 141, + 344, + 469, + 357 + ], + "score": 1.0, + "content": "demonstrate that CPVT has visually similar attention maps compared to those", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 141, + 355, + 470, + 368 + ], + "spans": [ + { + "bbox": [ + 141, + 355, + 470, + 368 + ], + "score": 1.0, + "content": "with learned positional encodings and delivers outperforming results. Our Code", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 142, + 367, + 321, + 378 + ], + "spans": [ + { + "bbox": [ + 142, + 367, + 321, + 378 + ], + "score": 1.0, + "content": "is available at: https://git.io/CPVT.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 13, + "bbox_fs": [ + 141, + 235, + 470, + 378 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 400, + 205, + 413 + ], + "lines": [ + { + "bbox": [ + 105, + 399, + 208, + 416 + ], + "spans": [ + { + "bbox": [ + 105, + 399, + 208, + 416 + ], + "score": 1.0, + "content": "1 INTRODUCTION", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 20 + }, + { + "type": "text", + "bbox": [ + 107, + 426, + 505, + 503 + ], + "lines": [ + { + "bbox": [ + 105, + 426, + 505, + 438 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 505, + 438 + ], + "score": 1.0, + "content": "Recently, Transformers (Vaswani et al., 2017) have been viewed as a strong alternative to Convolu-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 437, + 505, + 450 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 505, + 450 + ], + "score": 1.0, + "content": "tional Neural Networks (CNNs) in visual recognition tasks such as classification (Dosovitskiy et al.,", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "spans": [ + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "score": 1.0, + "content": "2021) and detection (Carion et al., 2020; Zhu et al., 2021). Unlike the convolution operation in", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 459, + 505, + 471 + ], + "spans": [ + { + "bbox": [ + 106, + 459, + 505, + 471 + ], + "score": 1.0, + "content": "CNNs, which has a limited receptive field, the self-attention mechanism in the Transformers can", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 470, + 506, + 483 + ], + "spans": [ + { + "bbox": [ + 105, + 470, + 506, + 483 + ], + "score": 1.0, + "content": "capture the long-distance information and dynamically adapt the receptive field according to the", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "spans": [ + { + "bbox": [ + 105, + 480, + 505, + 493 + ], + "score": 1.0, + "content": "image content. Consequently, Transformers are considered more flexible and powerful than CNNs,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 493, + 363, + 504 + ], + "spans": [ + { + "bbox": [ + 106, + 493, + 363, + 504 + ], + "score": 1.0, + "content": "being promising to achieve more progress in visual recognition.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 24, + "bbox_fs": [ + 105, + 426, + 506, + 504 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 509, + 505, + 618 + ], + "lines": [ + { + "bbox": [ + 106, + 509, + 505, + 521 + ], + "spans": [ + { + "bbox": [ + 106, + 509, + 505, + 521 + ], + "score": 1.0, + "content": "However, the self-attention operation in Transformers is permutation-invariant, which discards the", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 520, + 505, + 532 + ], + "spans": [ + { + "bbox": [ + 106, + 520, + 505, + 532 + ], + "score": 1.0, + "content": "order of the tokens in an input sequence. To mitigate this issue, previous works (Vaswani et al.,", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 529, + 506, + 544 + ], + "spans": [ + { + "bbox": [ + 105, + 529, + 506, + 544 + ], + "score": 1.0, + "content": "2017; Dosovitskiy et al., 2021) add the absolute positional encodings to each input token (see Fig-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "spans": [ + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "score": 1.0, + "content": "ure 1a), which enables order-awareness. The positional encoding can either be learnable or fixed", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 552, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 505, + 565 + ], + "score": 1.0, + "content": "with sinusoidal functions of different frequencies. Despite being effective, these positional encod-", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 563, + 506, + 577 + ], + "spans": [ + { + "bbox": [ + 105, + 563, + 506, + 577 + ], + "score": 1.0, + "content": "ings seriously harm the flexibility of the Transformers, hampering their broader applications. Taking", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "score": 1.0, + "content": "the learnable version as an example, the encodings are often a vector of equal length to the input", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 585, + 506, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 506, + 599 + ], + "score": 1.0, + "content": "sequence, which are jointly updated with the network weights during training. As a result, the length", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "score": 1.0, + "content": "and the value of the positional encodings are fixed once trained. During testing, it causes difficulties", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 608, + 375, + 621 + ], + "spans": [ + { + "bbox": [ + 105, + 608, + 375, + 621 + ], + "score": 1.0, + "content": "of handling the sequences longer than the ones in the training data.", + "type": "text" + } + ], + "index": 37 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 509, + 506, + 621 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 624, + 504, + 712 + ], + "lines": [ + { + "bbox": [ + 105, + 624, + 505, + 637 + ], + "spans": [ + { + "bbox": [ + 105, + 624, + 505, + 637 + ], + "score": 1.0, + "content": "The inability to adapt to longer input sequences during testing greatly limits the range of general-", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 635, + 505, + 648 + ], + "spans": [ + { + "bbox": [ + 105, + 635, + 505, + 648 + ], + "score": 1.0, + "content": "ization. For instance, in vision tasks like object detection, we expect the model can be applied to the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 647, + 505, + 659 + ], + "spans": [ + { + "bbox": [ + 106, + 647, + 505, + 659 + ], + "score": 1.0, + "content": "images of any size during inference, which might be much larger than the training images. A possi-", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 657, + 505, + 670 + ], + "spans": [ + { + "bbox": [ + 105, + 657, + 505, + 670 + ], + "score": 1.0, + "content": "ble remedy is to use bicubic interpolation to upsample the positional encodings to the target length,", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 669, + 506, + 681 + ], + "spans": [ + { + "bbox": [ + 106, + 669, + 506, + 681 + ], + "score": 1.0, + "content": "but it degrades the performance without fine-tuning as later shown in our experiments. For vision", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 679, + 506, + 692 + ], + "spans": [ + { + "bbox": [ + 105, + 679, + 506, + 692 + ], + "score": 1.0, + "content": "in general, we expect that the models be translation-equivariant. For example, the output feature", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 106, + 691, + 505, + 703 + ], + "spans": [ + { + "bbox": [ + 106, + 691, + 505, + 703 + ], + "score": 1.0, + "content": "maps of CNNs shift accordingly as the target objects are moved in the input images. However, the", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 700, + 506, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 700, + 506, + 714 + ], + "score": 1.0, + "content": "absolute positional encoding scheme might break the translation equivalence because it adds unique", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 345, + 505, + 358 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 505, + 358 + ], + "score": 1.0, + "content": "positional encodings to each token (or each image patch). One may overcome the issue with rela-", + "type": "text", + "cross_page": true + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 357, + 505, + 369 + ], + "spans": [ + { + "bbox": [ + 106, + 357, + 505, + 369 + ], + "score": 1.0, + "content": "tive positional encodings as in (Shaw et al., 2018). However, relative positional encodings not only", + "type": "text", + "cross_page": true + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 367, + 505, + 380 + ], + "spans": [ + { + "bbox": [ + 105, + 367, + 505, + 380 + ], + "score": 1.0, + "content": "come with extra computational costs, but also require modifying the implementation of the standard", + "type": "text", + "cross_page": true + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 378, + 505, + 391 + ], + "spans": [ + { + "bbox": [ + 105, + 378, + 505, + 391 + ], + "score": 1.0, + "content": "Transformers. Last but not least, the relative positional encodings cannot work equally well as the", + "type": "text", + "cross_page": true + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 389, + 505, + 402 + ], + "spans": [ + { + "bbox": [ + 105, + 389, + 505, + 402 + ], + "score": 1.0, + "content": "absolute ones, because the image recognition task still requires absolute position information (Islam", + "type": "text", + "cross_page": true + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 401, + 378, + 412 + ], + "spans": [ + { + "bbox": [ + 105, + 401, + 378, + 412 + ], + "score": 1.0, + "content": "et al., 2020), which the relative positional encodings fail to provide.", + "type": "text", + "cross_page": true + } + ], + "index": 13 + } + ], + "index": 41.5, + "bbox_fs": [ + 105, + 624, + 506, + 714 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 124, + 80, + 473, + 256 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 124, + 80, + 473, + 256 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 124, + 80, + 473, + 256 + ], + "spans": [ + { + "bbox": [ + 124, + 80, + 473, + 256 + ], + "score": 0.973, + "type": "image", + "image_path": "0c4b2513e8c89ba2d4113b63f9bdb7b99d31471e828098a2ee34f19b9c920289.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 124, + 80, + 473, + 138.66666666666666 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 124, + 138.66666666666666, + 473, + 197.33333333333331 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 124, + 197.33333333333331, + 473, + 255.99999999999997 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 107, + 265, + 504, + 321 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 265, + 504, + 279 + ], + "spans": [ + { + "bbox": [ + 106, + 265, + 504, + 279 + ], + "score": 1.0, + "content": "Figure 1. Vision Transformers: (a) ViT (Dosovitskiy et al., 2021) with explicit 1D learnable posi-", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 107, + 277, + 504, + 289 + ], + "spans": [ + { + "bbox": [ + 107, + 277, + 504, + 289 + ], + "score": 1.0, + "content": "tional encodings (PE) (b) CPVT with conditional positional encoding from the proposed Position", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 287, + 505, + 300 + ], + "spans": [ + { + "bbox": [ + 106, + 287, + 505, + 300 + ], + "score": 1.0, + "content": "Encoding Generator (PEG) plugin, which is the default choice. (c) CPVT-GAP without class token", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 299, + 505, + 311 + ], + "spans": [ + { + "bbox": [ + 107, + 299, + 505, + 311 + ], + "score": 1.0, + "content": "(cls), but with global average pooling (GAP) over all items in the sequence. Note that GAP is a", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 308, + 297, + 323 + ], + "spans": [ + { + "bbox": [ + 106, + 308, + 297, + 323 + ], + "score": 1.0, + "content": "bonus version which has boosted performance.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 107, + 345, + 505, + 412 + ], + "lines": [ + { + "bbox": [ + 105, + 345, + 505, + 358 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 505, + 358 + ], + "score": 1.0, + "content": "positional encodings to each token (or each image patch). One may overcome the issue with rela-", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 357, + 505, + 369 + ], + "spans": [ + { + "bbox": [ + 106, + 357, + 505, + 369 + ], + "score": 1.0, + "content": "tive positional encodings as in (Shaw et al., 2018). However, relative positional encodings not only", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 367, + 505, + 380 + ], + "spans": [ + { + "bbox": [ + 105, + 367, + 505, + 380 + ], + "score": 1.0, + "content": "come with extra computational costs, but also require modifying the implementation of the standard", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 378, + 505, + 391 + ], + "spans": [ + { + "bbox": [ + 105, + 378, + 505, + 391 + ], + "score": 1.0, + "content": "Transformers. Last but not least, the relative positional encodings cannot work equally well as the", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 389, + 505, + 402 + ], + "spans": [ + { + "bbox": [ + 105, + 389, + 505, + 402 + ], + "score": 1.0, + "content": "absolute ones, because the image recognition task still requires absolute position information (Islam", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 401, + 378, + 412 + ], + "spans": [ + { + "bbox": [ + 105, + 401, + 378, + 412 + ], + "score": 1.0, + "content": "et al., 2020), which the relative positional encodings fail to provide.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 10.5 + }, + { + "type": "text", + "bbox": [ + 106, + 417, + 505, + 495 + ], + "lines": [ + { + "bbox": [ + 105, + 416, + 505, + 430 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 505, + 430 + ], + "score": 1.0, + "content": "In this work, we advocate a novel positional encoding (PE) scheme to incorporate the position", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "information into Transformers. Unlike the predefined and input-agnostic positional encodings used", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 104, + 438, + 506, + 453 + ], + "spans": [ + { + "bbox": [ + 104, + 438, + 506, + 453 + ], + "score": 1.0, + "content": "in previous works (Dosovitskiy et al., 2021; Vaswani et al., 2017; Shaw et al., 2018), the proposed", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 450, + 506, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 450, + 506, + 464 + ], + "score": 1.0, + "content": "PE is dynamically generated and conditioned on the local neighborhood of input tokens. Thus, our", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 461, + 505, + 474 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 505, + 474 + ], + "score": 1.0, + "content": "positional encodings can change along with the input size and try to keep translation equivalence. We", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 472, + 505, + 485 + ], + "spans": [ + { + "bbox": [ + 105, + 472, + 505, + 485 + ], + "score": 1.0, + "content": "demonstrate that the vision transformers (Dosovitskiy et al., 2021; Touvron et al., 2020) with our new", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 104, + 482, + 506, + 497 + ], + "spans": [ + { + "bbox": [ + 104, + 482, + 506, + 497 + ], + "score": 1.0, + "content": "PE (i.e. CPVT, see Figure 1c) achieve even better performance. We summarize our contributions as,", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17 + }, + { + "type": "text", + "bbox": [ + 134, + 513, + 505, + 579 + ], + "lines": [ + { + "bbox": [ + 132, + 512, + 506, + 526 + ], + "spans": [ + { + "bbox": [ + 132, + 512, + 506, + 526 + ], + "score": 1.0, + "content": "• We propose a novel positional encoding (PE) scheme, termed conditional position encod-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 140, + 523, + 506, + 536 + ], + "spans": [ + { + "bbox": [ + 140, + 523, + 506, + 536 + ], + "score": 1.0, + "content": "ings (CPE). CPE is dynamically generated with Positional Encoding Generators (PEG) and", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 141, + 536, + 506, + 548 + ], + "spans": [ + { + "bbox": [ + 141, + 536, + 506, + 548 + ], + "score": 1.0, + "content": "can be effortlessly implemented by the modern deep learning frameworks (Paszke et al.,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 141, + 545, + 506, + 559 + ], + "spans": [ + { + "bbox": [ + 141, + 545, + 506, + 559 + ], + "score": 1.0, + "content": "2019; Abadi et al., 2016; Chen et al., 2015), requiring no changes to the current Trans-", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 141, + 557, + 506, + 569 + ], + "spans": [ + { + "bbox": [ + 141, + 557, + 506, + 569 + ], + "score": 1.0, + "content": "former APIs. Through an in-depth analysis and thorough experimentations, we unveil that", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 141, + 568, + 482, + 581 + ], + "spans": [ + { + "bbox": [ + 141, + 568, + 482, + 581 + ], + "score": 1.0, + "content": "this design affords both absolute and relative encoding yet it goes above and beyond.", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 23.5 + }, + { + "type": "text", + "bbox": [ + 134, + 593, + 504, + 626 + ], + "lines": [ + { + "bbox": [ + 133, + 592, + 506, + 606 + ], + "spans": [ + { + "bbox": [ + 133, + 592, + 506, + 606 + ], + "score": 1.0, + "content": "• As opposed to widely-used absolute positional encodings, CPE can provide a kind of", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 142, + 604, + 505, + 616 + ], + "spans": [ + { + "bbox": [ + 142, + 604, + 505, + 616 + ], + "score": 1.0, + "content": "stronger explicit bias towards the translation equivalence which is important to improve", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 142, + 615, + 279, + 627 + ], + "spans": [ + { + "bbox": [ + 142, + 615, + 279, + 627 + ], + "score": 1.0, + "content": "the performance of Transformers.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 28 + }, + { + "type": "text", + "bbox": [ + 134, + 640, + 505, + 673 + ], + "lines": [ + { + "bbox": [ + 133, + 639, + 506, + 653 + ], + "spans": [ + { + "bbox": [ + 133, + 639, + 506, + 653 + ], + "score": 1.0, + "content": "• Built on CPE, we propose Conditional Position encoding Vision Transformer (CPVT). It", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 141, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 141, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "achieves better performance than previous vison transformers (Dosovitskiy et al., 2021;", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 142, + 662, + 231, + 675 + ], + "spans": [ + { + "bbox": [ + 142, + 662, + 231, + 675 + ], + "score": 1.0, + "content": "Touvron et al., 2020).", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 31 + }, + { + "type": "text", + "bbox": [ + 135, + 687, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 133, + 687, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 133, + 687, + 505, + 701 + ], + "score": 1.0, + "content": "• CPE can well generalize to arbitrary input resolutions, which are required in many impor-", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 141, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 141, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "tant downstream tasks such as segmentation and detection. 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Vision Transformers: (a) ViT (Dosovitskiy et al., 2021) with explicit 1D learnable posi-", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 107, + 277, + 504, + 289 + ], + "spans": [ + { + "bbox": [ + 107, + 277, + 504, + 289 + ], + "score": 1.0, + "content": "tional encodings (PE) (b) CPVT with conditional positional encoding from the proposed Position", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 287, + 505, + 300 + ], + "spans": [ + { + "bbox": [ + 106, + 287, + 505, + 300 + ], + "score": 1.0, + "content": "Encoding Generator (PEG) plugin, which is the default choice. (c) CPVT-GAP without class token", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 299, + 505, + 311 + ], + "spans": [ + { + "bbox": [ + 107, + 299, + 505, + 311 + ], + "score": 1.0, + "content": "(cls), but with global average pooling (GAP) over all items in the sequence. 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We will show", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 433, + 505, + 446 + ], + "spans": [ + { + "bbox": [ + 106, + 433, + 505, + 446 + ], + "score": 1.0, + "content": "that the tokens on the borders can be aware of their absolute positions due to the commonly-used", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 442, + 168, + 458 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 168, + 458 + ], + "score": 1.0, + "content": "zero paddings.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 20, + "bbox_fs": [ + 105, + 356, + 506, + 458 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 461, + 210, + 548 + ], + "lines": [ + { + "bbox": [ + 106, + 460, + 212, + 474 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 212, + 474 + ], + "score": 1.0, + "content": "Therefore, we propose", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 472, + 212, + 484 + ], + "spans": [ + { + "bbox": [ + 106, + 472, + 212, + 484 + ], + "score": 1.0, + "content": "positional encoding gen-", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 483, + 211, + 495 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 211, + 495 + ], + "score": 1.0, + "content": "erators (PEG) to dynam-", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 493, + 212, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 212, + 506 + ], + "score": 1.0, + "content": "ically produce the posi-", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 504, + 212, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 504, + 212, + 516 + ], + "score": 1.0, + "content": "tional encodings condi-", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 515, + 212, + 528 + ], + "spans": [ + { + "bbox": [ + 105, + 515, + 212, + 528 + ], + "score": 1.0, + "content": "tioned on the local neigh-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 526, + 212, + 539 + ], + "spans": [ + { + "bbox": [ + 105, + 526, + 212, + 539 + ], + "score": 1.0, + "content": "borhood of an input to-", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 536, + 127, + 550 + ], + "spans": [ + { + "bbox": [ + 105, + 536, + 127, + 550 + ], + "score": 1.0, + "content": "ken.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 28.5, + "bbox_fs": [ + 105, + 460, + 212, + 550 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 554, + 212, + 610 + ], + "lines": [ + { + "bbox": [ + 105, + 552, + 213, + 567 + ], + "spans": [ + { + "bbox": [ + 105, + 552, + 152, + 566 + ], + "score": 1.0, + "content": "Positional", + "type": "text" + }, + { + "bbox": [ + 167, + 552, + 213, + 567 + ], + "score": 1.0, + "content": "Encoding", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 564, + 213, + 577 + ], + "spans": [ + { + "bbox": [ + 105, + 564, + 156, + 577 + ], + "score": 1.0, + "content": "Generator.", + "type": "text" + }, + { + "bbox": [ + 172, + 564, + 213, + 577 + ], + "score": 1.0, + "content": "PEG is", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 575, + 212, + 588 + ], + "spans": [ + { + "bbox": [ + 105, + 575, + 212, + 588 + ], + "score": 1.0, + "content": "illustrated in Figure 2.", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 586, + 212, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 586, + 212, + 599 + ], + "score": 1.0, + "content": "To condition on the", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 598, + 212, + 611 + ], + "spans": [ + { + "bbox": [ + 105, + 598, + 212, + 611 + ], + "score": 1.0, + "content": "local neighbors, we first", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 35, + "bbox_fs": [ + 105, + 552, + 213, + 611 + ] + }, + { + "type": "image", + "bbox": [ + 223, + 461, + 502, + 557 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 223, + 461, + 502, + 557 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 223, + 461, + 502, + 557 + ], + "spans": [ + { + "bbox": [ + 223, + 461, + 502, + 557 + ], + "score": 0.969, + "type": "image", + "image_path": "e12ed07e330e1cbad3fc807996e87af006eec2275384ddc7e13bc09ba9a2aac7.jpg" + } + ] + } + ], + "index": 38, + "virtual_lines": [ + { + "bbox": [ + 223, + 461, + 502, + 493.0 + ], + "spans": [], + "index": 37 + }, + { + "bbox": [ + 223, + 493.0, + 502, + 525.0 + ], + "spans": [], + "index": 38 + }, + { + "bbox": [ + 223, + 525.0, + 502, + 557.0 + ], + "spans": [], + "index": 39 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 218, + 567, + 504, + 590 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 218, + 565, + 505, + 580 + ], + "spans": [ + { + "bbox": [ + 218, + 565, + 505, + 580 + ], + "score": 1.0, + "content": "Figure 2. Schematic illustration of Positional Encoding Generator", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 219, + 578, + 480, + 590 + ], + "spans": [ + { + "bbox": [ + 219, + 578, + 271, + 590 + ], + "score": 1.0, + "content": "(PEG). Note", + "type": "text" + }, + { + "bbox": [ + 271, + 578, + 278, + 588 + ], + "score": 0.74, + "content": "d", + "type": "inline_equation" + }, + { + "bbox": [ + 279, + 578, + 370, + 590 + ], + "score": 1.0, + "content": "is the embedding size,", + "type": "text" + }, + { + "bbox": [ + 370, + 578, + 380, + 588 + ], + "score": 0.8, + "content": "N", + "type": "inline_equation" + }, + { + "bbox": [ + 381, + 578, + 480, + 590 + ], + "score": 1.0, + "content": "is the number of tokens.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 40.5 + } + ], + "index": 39.25 + }, + { + "type": "text", + "bbox": [ + 106, + 609, + 505, + 676 + ], + "lines": [ + { + "bbox": [ + 104, + 606, + 507, + 623 + ], + "spans": [ + { + "bbox": [ + 104, + 606, + 257, + 623 + ], + "score": 1.0, + "content": "reshape the flattened input sequence", + "type": "text" + }, + { + "bbox": [ + 257, + 608, + 322, + 619 + ], + "score": 0.92, + "content": "X \\in \\mathbb { R } ^ { B \\times N \\times C }", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 606, + 394, + 623 + ], + "score": 1.0, + "content": "of DeiT back to", + "type": "text" + }, + { + "bbox": [ + 394, + 608, + 477, + 620 + ], + "score": 0.92, + "content": "X ^ { \\prime } \\in \\mathbb { R } ^ { B \\times H \\times W \\times C }", + "type": "inline_equation" + }, + { + "bbox": [ + 477, + 606, + 507, + 623 + ], + "score": 1.0, + "content": "in the", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 619, + 505, + 632 + ], + "spans": [ + { + "bbox": [ + 105, + 619, + 305, + 632 + ], + "score": 1.0, + "content": "2-D image space. Then, a function (denoted by", + "type": "text" + }, + { + "bbox": [ + 306, + 621, + 315, + 631 + ], + "score": 0.82, + "content": "\\mathcal { F }", + "type": "inline_equation" + }, + { + "bbox": [ + 316, + 619, + 505, + 632 + ], + "score": 1.0, + "content": "in Figure 2) is repeatedly applied to the local", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 102, + 626, + 507, + 647 + ], + "spans": [ + { + "bbox": [ + 102, + 626, + 141, + 647 + ], + "score": 1.0, + "content": "patch in", + "type": "text" + }, + { + "bbox": [ + 142, + 631, + 155, + 641 + ], + "score": 0.88, + "content": "X ^ { \\prime }", + "type": "inline_equation" + }, + { + "bbox": [ + 155, + 626, + 349, + 647 + ], + "score": 1.0, + "content": "to produce the conditional positional encodings", + "type": "text" + }, + { + "bbox": [ + 350, + 630, + 406, + 641 + ], + "score": 0.91, + "content": "E ^ { \\tilde { B } \\times H \\times \\tilde { W } \\times C }", + "type": "inline_equation" + }, + { + "bbox": [ + 406, + 626, + 507, + 647 + ], + "score": 1.0, + "content": ". PEG can be efficiently", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 104, + 639, + 506, + 667 + ], + "spans": [ + { + "bbox": [ + 104, + 639, + 306, + 667 + ], + "score": 1.0, + "content": "implemented with a 2-D convolution with kernel zero paddings here are important to make the mo", + "type": "text" + }, + { + "bbox": [ + 306, + 643, + 313, + 653 + ], + "score": 0.65, + "content": "k", + "type": "inline_equation" + }, + { + "bbox": [ + 314, + 639, + 317, + 667 + ], + "score": 1.0, + "content": "l", + "type": "text" + }, + { + "bbox": [ + 317, + 642, + 346, + 654 + ], + "score": 0.82, + "content": "( k \\geq 3 )", + "type": "inline_equation" + }, + { + "bbox": [ + 347, + 639, + 367, + 667 + ], + "score": 1.0, + "content": "and re of", + "type": "text" + }, + { + "bbox": [ + 368, + 641, + 385, + 655 + ], + "score": 0.91, + "content": "\\frac { k - 1 } { 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 386, + 639, + 478, + 667 + ], + "score": 1.0, + "content": "zero paddings. Note tabsolute positions, and", + "type": "text" + }, + { + "bbox": [ + 488, + 639, + 506, + 667 + ], + "score": 1.0, + "content": "thecan", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 478, + 654, + 488, + 663 + ], + "spans": [ + { + "bbox": [ + 478, + 654, + 488, + 663 + ], + "score": 0.83, + "content": "\\mathcal { F }", + "type": "inline_equation" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 663, + 408, + 677 + ], + "spans": [ + { + "bbox": [ + 105, + 663, + 408, + 677 + ], + "score": 1.0, + "content": "be of various forms such as various types of convolutions and many others.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 45.5, + "bbox_fs": [ + 102, + 606, + 507, + 677 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 689, + 405, + 700 + ], + "lines": [ + { + "bbox": [ + 105, + 688, + 406, + 702 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 406, + 702 + ], + "score": 1.0, + "content": "3.3 CONDITIONAL POSITIONAL ENCODING VISION TRANSFORMERS", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 49 + }, + { + "type": "text", + "bbox": [ + 107, + 709, + 501, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 709, + 503, + 722 + ], + "spans": [ + { + "bbox": [ + 106, + 709, + 503, + 722 + ], + "score": 1.0, + "content": "Built on the conditional positional encodings, we propose our Conditional Positional Encoding Vi-", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 720, + 503, + 733 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 503, + 733 + ], + "score": 1.0, + "content": "sion Transformers (CPVT). Except that our positional encodings are conditional, we exactly follow", + "type": "text" + } + ], + "index": 51 + } + ], + "index": 50.5, + "bbox_fs": [ + 105, + 709, + 503, + 733 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 82, + 504, + 126 + ], + "lines": [ + { + "bbox": [ + 105, + 82, + 506, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 82, + 506, + 95 + ], + "score": 1.0, + "content": "ViT and DeiT to design our vision transformers and we also have three sizes CPVT-Ti, CPVT-S and", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 92, + 506, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 92, + 506, + 107 + ], + "score": 1.0, + "content": "CPVT-B. Similar to the original positional encodings in DeiT, the conditional positional encodings", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 105, + 506, + 118 + ], + "spans": [ + { + "bbox": [ + 105, + 105, + 506, + 118 + ], + "score": 1.0, + "content": "are also added to the input sequence, as shown in Figure 1 (b). In CPVT, the position where PEG is", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 115, + 454, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 454, + 128 + ], + "score": 1.0, + "content": "applied is also important to the performance, which will be studied in the experiments.", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 1.5 + }, + { + "type": "text", + "bbox": [ + 107, + 132, + 505, + 187 + ], + "lines": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 131, + 506, + 145 + ], + "score": 1.0, + "content": "In addition, both DeiT and ViT utilize an extra learnable class token to perform classification (i.e.,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 144, + 505, + 156 + ], + "spans": [ + { + "bbox": [ + 106, + 144, + 505, + 156 + ], + "score": 1.0, + "content": "cls token shown in Figure 1 (a) and (b)). By design, the class token is not translation-invariant,", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 153, + 505, + 168 + ], + "spans": [ + { + "bbox": [ + 105, + 153, + 505, + 168 + ], + "score": 1.0, + "content": "although it can learn to be so. A simple alternative is to directly replace it with a global average", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "score": 1.0, + "content": "pooling (GAP), which is inherently translation-invariant, resulting in our CVPT-GAP. Together with", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 175, + 403, + 190 + ], + "spans": [ + { + "bbox": [ + 105, + 175, + 403, + 190 + ], + "score": 1.0, + "content": "CPE, CVPT-GAP achieves much better image classification performance.", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 6 + }, + { + "type": "title", + "bbox": [ + 108, + 205, + 200, + 218 + ], + "lines": [ + { + "bbox": [ + 105, + 205, + 201, + 219 + ], + "spans": [ + { + "bbox": [ + 105, + 205, + 201, + 219 + ], + "score": 1.0, + "content": "4 EXPERIMENTS", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 9 + }, + { + "type": "title", + "bbox": [ + 107, + 231, + 160, + 243 + ], + "lines": [ + { + "bbox": [ + 105, + 230, + 160, + 244 + ], + "spans": [ + { + "bbox": [ + 105, + 230, + 160, + 244 + ], + "score": 1.0, + "content": "4.1 SETUP", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 10 + }, + { + "type": "text", + "bbox": [ + 107, + 252, + 505, + 297 + ], + "lines": [ + { + "bbox": [ + 104, + 251, + 506, + 267 + ], + "spans": [ + { + "bbox": [ + 104, + 251, + 506, + 267 + ], + "score": 1.0, + "content": "Datasets. Following DeiT (Touvron et al., 2020), we use ILSVRC-2012 ImageNet dataset (Deng", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 264, + 505, + 276 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 505, + 276 + ], + "score": 1.0, + "content": "et al., 2009) with 1K classes and 1.3M images to train all our models. We report the results on the", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 275, + 505, + 286 + ], + "spans": [ + { + "bbox": [ + 106, + 275, + 505, + 286 + ], + "score": 1.0, + "content": "validation set with 50K images. Unlike ViT (Dosovitskiy et al., 2021), we do not use the much", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 285, + 330, + 298 + ], + "spans": [ + { + "bbox": [ + 105, + 285, + 330, + 298 + ], + "score": 1.0, + "content": "larger undisclosed JFT-300M dataset (Sun et al., 2017).", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 12.5 + }, + { + "type": "text", + "bbox": [ + 107, + 302, + 504, + 346 + ], + "lines": [ + { + "bbox": [ + 105, + 302, + 504, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 302, + 504, + 315 + ], + "score": 1.0, + "content": "Model variants. We have three models with various sizes to adapt to various computing scenarios.", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 313, + 506, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 313, + 506, + 326 + ], + "score": 1.0, + "content": "The detailed settings are shown in Table 9 (see B.1). All experiments in this paper are performed on", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 324, + 506, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 324, + 506, + 337 + ], + "score": 1.0, + "content": "Tesla V100 machines. Training the tiny model for 300 epochs takes about 1.3 days on a single node", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 335, + 463, + 348 + ], + "spans": [ + { + "bbox": [ + 105, + 335, + 463, + 348 + ], + "score": 1.0, + "content": "with 8 V100 GPU cards. CPVT-S and CPVT-B take about 1.6 and 2.5 days, respectively.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5 + }, + { + "type": "text", + "bbox": [ + 107, + 352, + 505, + 407 + ], + "lines": [ + { + "bbox": [ + 106, + 352, + 505, + 364 + ], + "spans": [ + { + "bbox": [ + 106, + 352, + 505, + 364 + ], + "score": 1.0, + "content": "Training details All the models (except for CPVT-B) are trained for 300 epochs with a global batch", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 362, + 505, + 376 + ], + "spans": [ + { + "bbox": [ + 105, + 362, + 505, + 376 + ], + "score": 1.0, + "content": "size of 2048 on Tesla V100 machines using AdamW optimizer (Loshchilov & Hutter, 2019). 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The detailed", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 397, + 234, + 408 + ], + "spans": [ + { + "bbox": [ + 105, + 397, + 234, + 408 + ], + "score": 1.0, + "content": "hyperparameters are in the B.2.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 21 + }, + { + "type": "title", + "bbox": [ + 107, + 423, + 323, + 434 + ], + "lines": [ + { + "bbox": [ + 105, + 421, + 325, + 435 + ], + "spans": [ + { + "bbox": [ + 105, + 421, + 325, + 435 + ], + "score": 1.0, + "content": "4.2 GENERALIZATION TO HIGHER RESOLUTIONS", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 24 + }, + { + "type": "text", + "bbox": [ + 107, + 443, + 505, + 543 + ], + "lines": [ + { + "bbox": [ + 106, + 443, + 505, + 457 + ], + "spans": [ + { + "bbox": [ + 106, + 443, + 505, + 457 + ], + "score": 1.0, + "content": "As mentioned before, our proposed PEG can directly generalize to larger image sizes without any", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 455, + 506, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 455, + 400, + 468 + ], + "score": 1.0, + "content": "fine-tuning. 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This gap continues to increase as the input resolution", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 531, + 145, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 531, + 145, + 546 + ], + "score": 1.0, + "content": "enlarges.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 29, + "bbox_fs": [ + 105, + 443, + 506, + 546 + ] + }, + { + "type": "table", + "bbox": [ + 126, + 591, + 486, + 706 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 108, + 555, + 503, + 578 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 554, + 504, + 567 + ], + "spans": [ + { + "bbox": [ + 107, + 554, + 504, + 567 + ], + "score": 1.0, + "content": "Table 2. Direct evaluation on other resolutions without fine-tuning. The models are trained on", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 108, + 566, + 453, + 577 + ], + "spans": [ + { + "bbox": [ + 108, + 566, + 147, + 577 + ], + "score": 0.88, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 147, + 566, + 288, + 577 + ], + "score": 1.0, + "content": ". A simple PEG of a single layer of", + "type": "text" + }, + { + "bbox": [ + 288, + 566, + 307, + 577 + ], + "score": 0.87, + "content": "3 \\times 3", + "type": "inline_equation" + }, + { + "bbox": [ + 308, + 566, + 453, + 577 + ], + "score": 1.0, + "content": "depth-wise convolution is used here", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 34.5 + }, + { + "type": "table_body", + "bbox": [ + 126, + 591, + 486, + 706 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 126, + 591, + 486, + 706 + ], + "spans": [ + { + "bbox": [ + 126, + 591, + 486, + 706 + ], + "score": 0.986, + "html": "
ModelParams160(%)224(%)384(%)448(%)512(%)
DeiT-tinyDeiT-tiny (sin)DeiT-tiny (no pos)CPVT-TiCPVT-Ti ‡6M6M6M6M6M65.665.262.166.8(+1.2)67.7 (+2.1)72.272.368.272.4(+0.2)73.4(+1.2)71.270.868.673.2(+2.0)74.2(+3.0)68.868.268.471.8(+3.0)72.6(+3.8)65.965.165.070.3(+4.4)70.8(+4.9)
DeiT-smallCPVT-S22M22M75.676.1(+0.5)79.979.978.180.4(+1.5)75.978.6(+2.7)72.676.8(+4.2)
DeiT-baseCPVT-B86M86M79.180.5(+1.4)81.881.9(+0.1)79.782.3(+2.6)79.882.4(+2.6)78.281.0(+2.8)
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ModelHeadParamsTop-1 Acc(%)Top-5 Acc(%）
DeiT-tiny (Touvron et al., 2020)DeiT-tinyCPVT-Ti tCPVT-Ti tCLTGAPCLTGAP6M6M6M6M72.272.673.474.991.091.291.892.6
DeiT-small (Touvron et al.,2020)DeiT-smallCPVT-S ‡CPVT-S tCLTGAP22M22M23M79.980.280.595.095.295.295.7
CLT
GAP23M81.5
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Compared with DeiT,", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 642, + 297, + 657 + ], + "spans": [ + { + "bbox": [ + 106, + 642, + 297, + 657 + ], + "score": 1.0, + "content": "CPVT models have much better top-1 accuracy", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 655, + 297, + 667 + ], + "spans": [ + { + "bbox": [ + 106, + 655, + 297, + 667 + ], + "score": 1.0, + "content": "with similar throughputs. Our models can en-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 666, + 297, + 678 + ], + "spans": [ + { + "bbox": [ + 106, + 666, + 297, + 678 + ], + "score": 1.0, + "content": "joy performance improvement when inputs are", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 677, + 297, + 688 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 297, + 688 + ], + "score": 1.0, + "content": "upscaled without fine-tuning, while DeiT de-", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 687, + 297, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 297, + 700 + ], + "score": 1.0, + "content": "grades as discussed in Table 2, see also Fig-", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 699, + 297, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 297, + 711 + ], + "score": 1.0, + "content": "ure 3 for a clear comparison. 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Performance comparison of Class Token (CLT) and global average pooling (GAP) on", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 255, + 321, + 268 + ], + "spans": [ + { + "bbox": [ + 106, + 255, + 321, + 268 + ], + "score": 1.0, + "content": "ImageNet. CPVT’s can be further boosted with GAP", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13.5 + }, + { + "type": "table_body", + "bbox": [ + 155, + 281, + 456, + 397 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 155, + 281, + 456, + 397 + ], + "spans": [ + { + "bbox": [ + 155, + 281, + 456, + 397 + ], + "score": 0.982, + "html": "
ModelHeadParamsTop-1 Acc(%)Top-5 Acc(%）
DeiT-tiny (Touvron et al., 2020)DeiT-tinyCPVT-Ti tCPVT-Ti tCLTGAPCLTGAP6M6M6M6M72.272.673.474.991.091.291.892.6
DeiT-small (Touvron et al.,2020)DeiT-smallCPVT-S ‡CPVT-S tCLTGAP22M22M23M79.980.280.595.095.295.295.7
CLT
GAP23M81.5
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ModelsParams(M) InputInput|throughput*ImNettop-1 %Realtop-1 %
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We can achieve", + "type": "text" + }, + { + "bbox": [ + 438, + 94, + 465, + 104 + ], + "score": 0.87, + "content": "7 1 . 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 465, + 94, + 504, + 106 + ], + "score": 1.0, + "content": "top-1 ac-", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 104, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 104, + 453, + 117 + ], + "score": 1.0, + "content": "curacy on ImageNet with DeiT-tiny. This is significantly better than DeiT-tiny w/o PE", + "type": "text" + }, + { + "bbox": [ + 454, + 105, + 487, + 116 + ], + "score": 0.85, + "content": "( 6 8 . 2 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 104, + 505, + 117 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 114, + 506, + 129 + ], + "spans": [ + { + "bbox": [ + 105, + 114, + 290, + 129 + ], + "score": 1.0, + "content": "is similar to the one with PEG on Q, K and V", + "type": "text" + }, + { + "bbox": [ + 290, + 115, + 324, + 127 + ], + "score": 0.81, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 324, + 114, + 506, + 129 + ], + "score": 1.0, + "content": ", which suggests that PEG mainly serves as a", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 127, + 222, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 127, + 222, + 140 + ], + "score": 1.0, + "content": "positional encoding scheme.", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 2 + }, + { + "type": "text", + "bbox": [ + 107, + 144, + 296, + 275 + ], + "lines": [ + { + "bbox": [ + 106, + 143, + 297, + 155 + ], + "spans": [ + { + "bbox": [ + 106, + 143, + 297, + 155 + ], + "score": 1.0, + "content": "We also design another experiment to remove", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 154, + 296, + 167 + ], + "spans": [ + { + "bbox": [ + 106, + 154, + 276, + 167 + ], + "score": 1.0, + "content": "this concern. By randomly-initializing a", + "type": "text" + }, + { + "bbox": [ + 276, + 154, + 296, + 165 + ], + "score": 0.86, + "content": "3 \\times 3", + "type": "inline_equation" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 164, + 296, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 296, + 178 + ], + "score": 1.0, + "content": "PEG and fixing its weights during the train-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 297, + 189 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 186, + 189 + ], + "score": 1.0, + "content": "ing, we can obtain", + "type": "text" + }, + { + "bbox": [ + 186, + 176, + 214, + 187 + ], + "score": 0.87, + "content": "7 1 . 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 214, + 176, + 297, + 189 + ], + "score": 1.0, + "content": "accuracy (Table 5),", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 187, + 297, + 199 + ], + "spans": [ + { + "bbox": [ + 105, + 187, + 195, + 199 + ], + "score": 1.0, + "content": "which is much higher", + "type": "text" + }, + { + "bbox": [ + 196, + 187, + 228, + 199 + ], + "score": 0.88, + "content": "( 3 . 1 \\% \\uparrow )", + "type": "inline_equation" + }, + { + "bbox": [ + 228, + 187, + 297, + 199 + ], + "score": 1.0, + "content": "than DeiT with-", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 198, + 297, + 209 + ], + "spans": [ + { + "bbox": [ + 106, + 198, + 153, + 209 + ], + "score": 1.0, + "content": "out any PE", + "type": "text" + }, + { + "bbox": [ + 154, + 199, + 186, + 209 + ], + "score": 0.87, + "content": "( 6 8 . 2 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 186, + 198, + 297, + 209 + ], + "score": 1.0, + "content": ". Since the weights of PEG", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 209, + 297, + 221 + ], + "spans": [ + { + "bbox": [ + 106, + 209, + 297, + 221 + ], + "score": 1.0, + "content": "are fixed and the performance improvement can", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 220, + 297, + 232 + ], + "spans": [ + { + "bbox": [ + 106, + 220, + 297, + 232 + ], + "score": 1.0, + "content": "only be due to the introduced position informa-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 230, + 296, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 296, + 243 + ], + "score": 1.0, + "content": "tion. On the contrary, when we exhaustively", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 241, + 297, + 254 + ], + "spans": [ + { + "bbox": [ + 106, + 241, + 297, + 254 + ], + "score": 1.0, + "content": "use 12 convolutional layers (kernel size being", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 252, + 297, + 266 + ], + "spans": [ + { + "bbox": [ + 106, + 252, + 297, + 266 + ], + "score": 1.0, + "content": "1, i.e., not producing local relationship) to re-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 263, + 297, + 276 + ], + "spans": [ + { + "bbox": [ + 106, + 263, + 297, + 276 + ], + "score": 1.0, + "content": "place the PEG, these layers have much more", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 10.5 + }, + { + "type": "table", + "bbox": [ + 304, + 179, + 510, + 259 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 304, + 141, + 504, + 165 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 303, + 140, + 504, + 154 + ], + "spans": [ + { + "bbox": [ + 303, + 140, + 504, + 154 + ], + "score": 1.0, + "content": "Table 5. Positional encoding rather than added pa-", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 303, + 154, + 456, + 165 + ], + "spans": [ + { + "bbox": [ + 303, + 154, + 456, + 165 + ], + "score": 1.0, + "content": "rameters gives the most improvement", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 17.5 + }, + { + "type": "table_body", + "bbox": [ + 304, + 179, + 510, + 259 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 304, + 179, + 510, + 259 + ], + "spans": [ + { + "bbox": [ + 304, + 179, + 510, + 259 + ], + "score": 0.976, + "html": "
KernelStyleParams (M)Top-1 Acc (%)
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However, it only boosts the performance by", + "type": "text" + }, + { + "bbox": [ + 412, + 275, + 434, + 286 + ], + "score": 0.86, + "content": "0 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 434, + 273, + 445, + 288 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 445, + 275, + 472, + 285 + ], + "score": 0.88, + "content": "6 8 . 6 \\%", + "type": "inline_equation" + } + ], + "index": 25 + } + ], + "index": 25 + } + ], + "index": 21.5 + }, + { + "type": "text", + "bbox": [ + 107, + 291, + 505, + 336 + ], + "lines": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "Another interesting finding is that fixing a learned PEG also helps training. When we initialize with", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 303, + 505, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 505, + 315 + ], + "score": 1.0, + "content": "a learned PEG instead of the random values and train the tiny version of the model from scratch", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 313, + 336, + 326 + ], + "score": 1.0, + "content": "while keeping the PEG fixed, the model can also achieve", + "type": "text" + }, + { + "bbox": [ + 337, + 314, + 364, + 324 + ], + "score": 0.87, + "content": "7 2 . 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 364, + 313, + 505, + 326 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet. This", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 325, + 281, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 325, + 244, + 337 + ], + "score": 1.0, + "content": "is very close to the learnable PEG", + "type": "text" + }, + { + "bbox": [ + 245, + 325, + 277, + 336 + ], + "score": 0.86, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 277, + 325, + 281, + 337 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27.5 + }, + { + "type": "title", + "bbox": [ + 107, + 352, + 237, + 363 + ], + "lines": [ + { + "bbox": [ + 105, + 351, + 239, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 239, + 365 + ], + "score": 1.0, + "content": "5.2 PEG POSITION IN CPVT", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 107, + 374, + 505, + 418 + ], + "lines": [ + { + "bbox": [ + 105, + 374, + 505, + 387 + ], + "spans": [ + { + "bbox": [ + 105, + 374, + 505, + 387 + ], + "score": 1.0, + "content": "We also experiment by varying the position of the PEG in the model. Table 6 (left) presents the", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "score": 1.0, + "content": "ablations for variable positions (denoted as PosIdx) based on the tiny model. We consider the input", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 396, + 505, + 408 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 505, + 408 + ], + "score": 1.0, + "content": "of the first encoder by index -1. Therefore, position 0 is the output of the first encoder block. PEG", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 407, + 363, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 215, + 420 + ], + "score": 1.0, + "content": "shows strong performance", + "type": "text" + }, + { + "bbox": [ + 216, + 407, + 255, + 418 + ], + "score": 0.85, + "content": "( \\sim 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 255, + 407, + 363, + 420 + ], + "score": 1.0, + "content": "when it is placed at [0, 3].", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5 + }, + { + "type": "text", + "bbox": [ + 106, + 423, + 505, + 501 + ], + "lines": [ + { + "bbox": [ + 105, + 423, + 505, + 437 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 505, + 437 + ], + "score": 1.0, + "content": "Note that positioning the PEG at 0 can have much better performance than positioning it at -1 (i.e.,", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 435, + 505, + 446 + ], + "spans": [ + { + "bbox": [ + 106, + 435, + 505, + 446 + ], + "score": 1.0, + "content": "before the first encoder), as shown in Table 6 (left). We observe that the difference between the two", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 446, + 505, + 458 + ], + "spans": [ + { + "bbox": [ + 106, + 446, + 505, + 458 + ], + "score": 1.0, + "content": "situations is they have different receptive fields. Specifically, the former has a global field while the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 457, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 105, + 457, + 505, + 470 + ], + "score": 1.0, + "content": "latter can only see a local area. Hence, they are supposed to work similarly well if we enlarge the", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 466, + 505, + 482 + ], + "spans": [ + { + "bbox": [ + 104, + 466, + 505, + 482 + ], + "score": 1.0, + "content": "convolution’s kernel size. To verify our hypothesis, we use a quite large kernel size 27 with a padding", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 479, + 506, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 479, + 506, + 491 + ], + "score": 1.0, + "content": "size 13 at position -1, whose result is reported in Table 6 (right). It achieves similar performance to", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 490, + 400, + 502 + ], + "spans": [ + { + "bbox": [ + 106, + 490, + 239, + 502 + ], + "score": 1.0, + "content": "the one positioning the PEG at 0", + "type": "text" + }, + { + "bbox": [ + 240, + 490, + 271, + 501 + ], + "score": 0.8, + "content": "( 7 2 . 5 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 490, + 400, + 502 + ], + "score": 1.0, + "content": ", which verifies our assumption.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 38 + }, + { + "type": "table", + "bbox": [ + 115, + 540, + 262, + 619 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 123, + 513, + 487, + 526 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 122, + 512, + 487, + 528 + ], + "spans": [ + { + "bbox": [ + 122, + 512, + 487, + 528 + ], + "score": 1.0, + "content": "Table 6. Comparison of different plugin positions (left) and kernels (right) using DeiT-tiny", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 42 + }, + { + "type": "table_body", + "bbox": [ + 115, + 540, + 262, + 619 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 115, + 540, + 262, + 619 + ], + "spans": [ + { + "bbox": [ + 115, + 540, + 262, + 619 + ], + "score": 0.958, + "html": "
PosIdxTop-1 (%)Top-5 (%)
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PosIdxkernelParamsTop-1 (%)Top-5 (%)
-13×35.7M70.690.2
-127×275.8M72.591.3
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We experiment with the 2-D sinusoidal", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 720, + 505, + 733 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 505, + 733 + ], + "score": 1.0, + "content": "encodings and it achieves on-par performance. For RPE, we follow (Shaw et al., 2018) and set the", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 51.5 + } + ], + "page_idx": 7, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2023", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 751, + 308, + 760 + ], + "lines": [ + { + "bbox": [ + 300, + 750, + 309, + 761 + ], + "spans": [ + { + "bbox": [ + 300, + 750, + 309, + 761 + ], + "score": 1.0, + "content": "8", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 82, + 505, + 138 + ], + "lines": [], + "index": 2, + "bbox_fs": [ + 105, + 82, + 506, + 140 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 107, + 144, + 296, + 275 + ], + "lines": [ + { + "bbox": [ + 106, + 143, + 297, + 155 + ], + "spans": [ + { + "bbox": [ + 106, + 143, + 297, + 155 + ], + "score": 1.0, + "content": "We also design another experiment to remove", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 154, + 296, + 167 + ], + "spans": [ + { + "bbox": [ + 106, + 154, + 276, + 167 + ], + "score": 1.0, + "content": "this concern. By randomly-initializing a", + "type": "text" + }, + { + "bbox": [ + 276, + 154, + 296, + 165 + ], + "score": 0.86, + "content": "3 \\times 3", + "type": "inline_equation" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 164, + 296, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 296, + 178 + ], + "score": 1.0, + "content": "PEG and fixing its weights during the train-", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 297, + 189 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 186, + 189 + ], + "score": 1.0, + "content": "ing, we can obtain", + "type": "text" + }, + { + "bbox": [ + 186, + 176, + 214, + 187 + ], + "score": 0.87, + "content": "7 1 . 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 214, + 176, + 297, + 189 + ], + "score": 1.0, + "content": "accuracy (Table 5),", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 187, + 297, + 199 + ], + "spans": [ + { + "bbox": [ + 105, + 187, + 195, + 199 + ], + "score": 1.0, + "content": "which is much higher", + "type": "text" + }, + { + "bbox": [ + 196, + 187, + 228, + 199 + ], + "score": 0.88, + "content": "( 3 . 1 \\% \\uparrow )", + "type": "inline_equation" + }, + { + "bbox": [ + 228, + 187, + 297, + 199 + ], + "score": 1.0, + "content": "than DeiT with-", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 198, + 297, + 209 + ], + "spans": [ + { + "bbox": [ + 106, + 198, + 153, + 209 + ], + "score": 1.0, + "content": "out any PE", + "type": "text" + }, + { + "bbox": [ + 154, + 199, + 186, + 209 + ], + "score": 0.87, + "content": "( 6 8 . 2 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 186, + 198, + 297, + 209 + ], + "score": 1.0, + "content": ". Since the weights of PEG", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 209, + 297, + 221 + ], + "spans": [ + { + "bbox": [ + 106, + 209, + 297, + 221 + ], + "score": 1.0, + "content": "are fixed and the performance improvement can", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 220, + 297, + 232 + ], + "spans": [ + { + "bbox": [ + 106, + 220, + 297, + 232 + ], + "score": 1.0, + "content": "only be due to the introduced position informa-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 230, + 296, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 296, + 243 + ], + "score": 1.0, + "content": "tion. On the contrary, when we exhaustively", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 241, + 297, + 254 + ], + "spans": [ + { + "bbox": [ + 106, + 241, + 297, + 254 + ], + "score": 1.0, + "content": "use 12 convolutional layers (kernel size being", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 252, + 297, + 266 + ], + "spans": [ + { + "bbox": [ + 106, + 252, + 297, + 266 + ], + "score": 1.0, + "content": "1, i.e., not producing local relationship) to re-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 263, + 297, + 276 + ], + "spans": [ + { + "bbox": [ + 106, + 263, + 297, + 276 + ], + "score": 1.0, + "content": "place the PEG, these layers have much more", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 10.5, + "bbox_fs": [ + 105, + 143, + 297, + 276 + ] + }, + { + "type": "table", + "bbox": [ + 304, + 179, + 510, + 259 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 304, + 141, + 504, + 165 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 303, + 140, + 504, + 154 + ], + "spans": [ + { + "bbox": [ + 303, + 140, + 504, + 154 + ], + "score": 1.0, + "content": "Table 5. Positional encoding rather than added pa-", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 303, + 154, + 456, + 165 + ], + "spans": [ + { + "bbox": [ + 303, + 154, + 456, + 165 + ], + "score": 1.0, + "content": "rameters gives the most improvement", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 17.5 + }, + { + "type": "table_body", + "bbox": [ + 304, + 179, + 510, + 259 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 304, + 179, + 510, + 259 + ], + "spans": [ + { + "bbox": [ + 304, + 179, + 510, + 259 + ], + "score": 0.976, + "html": "
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However, it only boosts the performance by", + "type": "text" + }, + { + "bbox": [ + 412, + 275, + 434, + 286 + ], + "score": 0.86, + "content": "0 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 434, + 273, + 445, + 288 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 445, + 275, + 472, + 285 + ], + "score": 0.88, + "content": "6 8 . 6 \\%", + "type": "inline_equation" + } + ], + "index": 25 + } + ], + "index": 25 + } + ], + "index": 21.5 + }, + { + "type": "text", + "bbox": [ + 107, + 291, + 505, + 336 + ], + "lines": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "Another interesting finding is that fixing a learned PEG also helps training. When we initialize with", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 303, + 505, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 303, + 505, + 315 + ], + "score": 1.0, + "content": "a learned PEG instead of the random values and train the tiny version of the model from scratch", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 313, + 505, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 313, + 336, + 326 + ], + "score": 1.0, + "content": "while keeping the PEG fixed, the model can also achieve", + "type": "text" + }, + { + "bbox": [ + 337, + 314, + 364, + 324 + ], + "score": 0.87, + "content": "7 2 . 3 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 364, + 313, + 505, + 326 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet. This", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 325, + 281, + 337 + ], + "spans": [ + { + "bbox": [ + 105, + 325, + 244, + 337 + ], + "score": 1.0, + "content": "is very close to the learnable PEG", + "type": "text" + }, + { + "bbox": [ + 245, + 325, + 277, + 336 + ], + "score": 0.86, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 277, + 325, + 281, + 337 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27.5, + "bbox_fs": [ + 105, + 291, + 505, + 337 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 352, + 237, + 363 + ], + "lines": [ + { + "bbox": [ + 105, + 351, + 239, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 239, + 365 + ], + "score": 1.0, + "content": "5.2 PEG POSITION IN CPVT", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 107, + 374, + 505, + 418 + ], + "lines": [ + { + "bbox": [ + 105, + 374, + 505, + 387 + ], + "spans": [ + { + "bbox": [ + 105, + 374, + 505, + 387 + ], + "score": 1.0, + "content": "We also experiment by varying the position of the PEG in the model. Table 6 (left) presents the", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 397 + ], + "score": 1.0, + "content": "ablations for variable positions (denoted as PosIdx) based on the tiny model. We consider the input", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 396, + 505, + 408 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 505, + 408 + ], + "score": 1.0, + "content": "of the first encoder by index -1. Therefore, position 0 is the output of the first encoder block. PEG", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 407, + 363, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 215, + 420 + ], + "score": 1.0, + "content": "shows strong performance", + "type": "text" + }, + { + "bbox": [ + 216, + 407, + 255, + 418 + ], + "score": 0.85, + "content": "( \\sim 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 255, + 407, + 363, + 420 + ], + "score": 1.0, + "content": "when it is placed at [0, 3].", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 374, + 505, + 420 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 423, + 505, + 501 + ], + "lines": [ + { + "bbox": [ + 105, + 423, + 505, + 437 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 505, + 437 + ], + "score": 1.0, + "content": "Note that positioning the PEG at 0 can have much better performance than positioning it at -1 (i.e.,", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 435, + 505, + 446 + ], + "spans": [ + { + "bbox": [ + 106, + 435, + 505, + 446 + ], + "score": 1.0, + "content": "before the first encoder), as shown in Table 6 (left). We observe that the difference between the two", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 446, + 505, + 458 + ], + "spans": [ + { + "bbox": [ + 106, + 446, + 505, + 458 + ], + "score": 1.0, + "content": "situations is they have different receptive fields. Specifically, the former has a global field while the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 457, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 105, + 457, + 505, + 470 + ], + "score": 1.0, + "content": "latter can only see a local area. Hence, they are supposed to work similarly well if we enlarge the", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 466, + 505, + 482 + ], + "spans": [ + { + "bbox": [ + 104, + 466, + 505, + 482 + ], + "score": 1.0, + "content": "convolution’s kernel size. To verify our hypothesis, we use a quite large kernel size 27 with a padding", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 479, + 506, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 479, + 506, + 491 + ], + "score": 1.0, + "content": "size 13 at position -1, whose result is reported in Table 6 (right). It achieves similar performance to", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 490, + 400, + 502 + ], + "spans": [ + { + "bbox": [ + 106, + 490, + 239, + 502 + ], + "score": 1.0, + "content": "the one positioning the PEG at 0", + "type": "text" + }, + { + "bbox": [ + 240, + 490, + 271, + 501 + ], + "score": 0.8, + "content": "( 7 2 . 5 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 490, + 400, + 502 + ], + "score": 1.0, + "content": ", which verifies our assumption.", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 38, + "bbox_fs": [ + 104, + 423, + 506, + 502 + ] + }, + { + "type": "table", + "bbox": [ + 115, + 540, + 262, + 619 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 123, + 513, + 487, + 526 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 122, + 512, + 487, + 528 + ], + "spans": [ + { + "bbox": [ + 122, + 512, + 487, + 528 + ], + "score": 1.0, + "content": "Table 6. Comparison of different plugin positions (left) and kernels (right) using DeiT-tiny", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 42 + }, + { + "type": "table_body", + "bbox": [ + 115, + 540, + 262, + 619 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 115, + 540, + 262, + 619 + ], + "spans": [ + { + "bbox": [ + 115, + 540, + 262, + 619 + ], + "score": 0.958, + "html": "
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PosIdxkernelParamsTop-1 (%)Top-5 (%)
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RPE here does not encode any", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 245, + 352, + 257 + ], + "spans": [ + { + "bbox": [ + 106, + 245, + 352, + 257 + ], + "score": 1.0, + "content": "absolute position information, see discussion in D.1 and B.3.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 9.5 + }, + { + "type": "text", + "bbox": [ + 107, + 262, + 504, + 295 + ], + "lines": [ + { + "bbox": [ + 106, + 263, + 505, + 273 + ], + "spans": [ + { + "bbox": [ + 106, + 263, + 505, + 273 + ], + "score": 1.0, + "content": "Moreover, we combine the learnable absolute PE with a single-layer PEG. This boosts the baseline", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 273, + 506, + 285 + ], + "spans": [ + { + "bbox": [ + 106, + 273, + 181, + 285 + ], + "score": 1.0, + "content": "CPVT-Ti (0-1) by", + "type": "text" + }, + { + "bbox": [ + 181, + 273, + 203, + 284 + ], + "score": 0.87, + "content": "0 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 203, + 273, + 360, + 285 + ], + "score": 1.0, + "content": ". If we use 4-layer PEG, it can achieve", + "type": "text" + }, + { + "bbox": [ + 361, + 273, + 388, + 284 + ], + "score": 0.88, + "content": "7 2 . 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 388, + 273, + 506, + 285 + ], + "score": 1.0, + "content": ". If we add a PEG to each of", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 284, + 486, + 297 + ], + "spans": [ + { + "bbox": [ + 105, + 284, + 245, + 297 + ], + "score": 1.0, + "content": "the first five blocks, we can obtain", + "type": "text" + }, + { + "bbox": [ + 245, + 284, + 272, + 295 + ], + "score": 0.86, + "content": "7 3 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 284, + 486, + 297 + ], + "score": 1.0, + "content": ", which is better than stacking them within one block.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 12 + }, + { + "type": "text", + "bbox": [ + 106, + 300, + 505, + 389 + ], + "lines": [ + { + "bbox": [ + 106, + 301, + 506, + 313 + ], + "spans": [ + { + "bbox": [ + 106, + 301, + 506, + 313 + ], + "score": 1.0, + "content": "CPE is not a simple combination of APE and RPE. We further compare our method with a", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 104, + 310, + 506, + 326 + ], + "spans": [ + { + "bbox": [ + 104, + 310, + 506, + 326 + ], + "score": 1.0, + "content": "baseline with combination of APE and RPE. Specifically, we use learnable positional encoding", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 505, + 336 + ], + "score": 1.0, + "content": "(LE) as DeiT at the beginning of the model and supply 2D RPE for every transformer block. This", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 334, + 505, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 172, + 347 + ], + "score": 1.0, + "content": "setting achieves", + "type": "text" + }, + { + "bbox": [ + 172, + 334, + 199, + 345 + ], + "score": 0.87, + "content": "7 2 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 199, + 334, + 468, + 347 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet, which is comparable to a single PEG", + "type": "text" + }, + { + "bbox": [ + 469, + 334, + 501, + 345 + ], + "score": 0.87, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 501, + 334, + 505, + 347 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 345, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 505, + 357 + ], + "score": 1.0, + "content": "Nevertheless, this experiment does not necessarily indicate that our CPE is a simple combination", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 356, + 505, + 368 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 505, + 368 + ], + "score": 1.0, + "content": "of APE and RPE. When tested on different resolutions, this baseline cannot scale well compared to", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 104, + 367, + 505, + 379 + ], + "spans": [ + { + "bbox": [ + 104, + 367, + 505, + 379 + ], + "score": 1.0, + "content": "ours (Table 8). RPE is not able to adequately mitigate the performance degradation on top of LE.", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 377, + 269, + 390 + ], + "spans": [ + { + "bbox": [ + 105, + 377, + 269, + 390 + ], + "score": 1.0, + "content": "This shall be seen as a major difference.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 17.5 + }, + { + "type": "table", + "bbox": [ + 123, + 437, + 488, + 473 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 107, + 399, + 504, + 423 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 106, + 398, + 504, + 412 + ], + "spans": [ + { + "bbox": [ + 106, + 398, + 504, + 412 + ], + "score": 1.0, + "content": "Table 8. Direct evaluation on other resolutions without fine-tuning. The models are trained on", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 108, + 411, + 414, + 423 + ], + "spans": [ + { + "bbox": [ + 108, + 411, + 146, + 421 + ], + "score": 0.87, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 147, + 411, + 222, + 423 + ], + "score": 1.0, + "content": ". CPE outperforms", + "type": "text" + }, + { + "bbox": [ + 223, + 411, + 260, + 421 + ], + "score": 0.28, + "content": "\\mathrm { L E + R P E }", + "type": "inline_equation" + }, + { + "bbox": [ + 261, + 411, + 414, + 423 + ], + "score": 1.0, + "content": "combination on untrained resolutions.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 22.5 + }, + { + "type": "table_body", + "bbox": [ + 123, + 437, + 488, + 473 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 123, + 437, + 488, + 473 + ], + "spans": [ + { + "bbox": [ + 123, + 437, + 488, + 473 + ], + "score": 0.971, + "html": "
ModelPositionalParams160(%)224(%)384(%)448(%)512(%)
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This setting", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 522, + 506, + 536 + ], + "spans": [ + { + "bbox": [ + 105, + 522, + 506, + 536 + ], + "score": 1.0, + "content": "suggests that it is also beneficial to have more of LEs, but not as good as ours. It is expected since", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 533, + 342, + 545 + ], + "spans": [ + { + "bbox": [ + 106, + 533, + 342, + 545 + ], + "score": 1.0, + "content": "we exploit relative information via PEGs at the same time.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 29 + }, + { + "type": "title", + "bbox": [ + 108, + 560, + 195, + 573 + ], + "lines": [ + { + "bbox": [ + 105, + 559, + 197, + 576 + ], + "spans": [ + { + "bbox": [ + 105, + 559, + 197, + 576 + ], + "score": 1.0, + "content": "6 CONCLUSION", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 32 + }, + { + "type": "text", + "bbox": [ + 107, + 585, + 505, + 674 + ], + "lines": [ + { + "bbox": [ + 105, + 585, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 505, + 599 + ], + "score": 1.0, + "content": "We introduced CPVT, a novel method to provide the position information in vision transformers,", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 597, + 505, + 609 + ], + "spans": [ + { + "bbox": [ + 106, + 597, + 505, + 609 + ], + "score": 1.0, + "content": "which dynamically generates the position encodings based on the local neighbors of each input", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 608, + 505, + 621 + ], + "spans": [ + { + "bbox": [ + 105, + 608, + 505, + 621 + ], + "score": 1.0, + "content": "token. Through extensive experimental studies, we demonstrate that our proposed positional en-", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 619, + 505, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 619, + 505, + 631 + ], + "score": 1.0, + "content": "codings can achieve stronger performance than the previous positional encodings. The transformer", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 630, + 505, + 643 + ], + "spans": [ + { + "bbox": [ + 105, + 630, + 505, + 643 + ], + "score": 1.0, + "content": "models with our positional encodings can naturally process longer input sequences and keep the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 641, + 505, + 654 + ], + "spans": [ + { + "bbox": [ + 105, + 641, + 505, + 654 + ], + "score": 1.0, + "content": "desired translation equivalence in vision tasks. Moreover, our positional encodings are easy to im-", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 652, + 505, + 664 + ], + "spans": [ + { + "bbox": [ + 106, + 652, + 505, + 664 + ], + "score": 1.0, + "content": "plement and come with negligible cost. We look forward to a broader application of our method in", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 662, + 395, + 676 + ], + "spans": [ + { + "bbox": [ + 105, + 662, + 395, + 676 + ], + "score": 1.0, + "content": "transformer-driven vision tasks like segmentation and video processing.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 36.5 + }, + { + "type": "title", + "bbox": [ + 108, + 691, + 175, + 703 + ], + "lines": [ + { + "bbox": [ + 106, + 690, + 176, + 704 + ], + "spans": [ + { + "bbox": [ + 106, + 690, + 176, + 704 + ], + "score": 1.0, + "content": "REFERENCES", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 41 + }, + { + "type": "text", + "bbox": [ + 109, + 709, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 709, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 106, + 709, + 505, + 722 + ], + "score": 1.0, + "content": "Mart´ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 115, + 719, + 505, + 734 + ], + "spans": [ + { + "bbox": [ + 115, + 719, + 505, + 734 + ], + "score": 1.0, + "content": "Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. Tensorflow: A system for large-", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42.5 + } + ], + "page_idx": 8, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 107, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2023", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 302, + 751, + 308, + 759 + ], + "lines": [ + { + "bbox": [ + 302, + 751, + 309, + 762 + ], + "spans": [ + { + "bbox": [ + 302, + 751, + 309, + 762 + ], + "score": 1.0, + "content": "9", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 192, + 107, + 419, + 209 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 107, + 70, + 502, + 93 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 68, + 504, + 84 + ], + "spans": [ + { + "bbox": [ + 106, + 68, + 504, + 84 + ], + "score": 1.0, + "content": "Table 7. Comparison of various positional encoding strategies. LE: learnable positional encoding.", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 79, + 244, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 79, + 244, + 95 + ], + "score": 1.0, + "content": "RPE: relative positional encoding", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "table_body", + "bbox": [ + 192, + 107, + 419, + 209 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 192, + 107, + 419, + 209 + ], + "spans": [ + { + "bbox": [ + 192, + 107, + 419, + 209 + ], + "score": 0.98, + "html": "
ModelPEG PosEncodingTop-1 (%)Top-5 (%）
DeiT-tiny (2020)LE72.2 72.391.0 91.0
DeiT-tiny DeiT-tiny2D sin-cos 2DRPE70.590.0
CPVT-Ti= 0-1PEG72.491.2
CPVT-Ti0-1PEG+LE72.991.4
CPVT-Ti0-14×PEG+LE72.9
0-591.4
CPVT-TiPEG73.491.8
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This boosts the baseline", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 273, + 506, + 285 + ], + "spans": [ + { + "bbox": [ + 106, + 273, + 181, + 285 + ], + "score": 1.0, + "content": "CPVT-Ti (0-1) by", + "type": "text" + }, + { + "bbox": [ + 181, + 273, + 203, + 284 + ], + "score": 0.87, + "content": "0 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 203, + 273, + 360, + 285 + ], + "score": 1.0, + "content": ". If we use 4-layer PEG, it can achieve", + "type": "text" + }, + { + "bbox": [ + 361, + 273, + 388, + 284 + ], + "score": 0.88, + "content": "7 2 . 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 388, + 273, + 506, + 285 + ], + "score": 1.0, + "content": ". If we add a PEG to each of", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 284, + 486, + 297 + ], + "spans": [ + { + "bbox": [ + 105, + 284, + 245, + 297 + ], + "score": 1.0, + "content": "the first five blocks, we can obtain", + "type": "text" + }, + { + "bbox": [ + 245, + 284, + 272, + 295 + ], + "score": 0.86, + "content": "7 3 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 284, + 486, + 297 + ], + "score": 1.0, + "content": ", which is better than stacking them within one block.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 12, + "bbox_fs": [ + 105, + 263, + 506, + 297 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 300, + 505, + 389 + ], + "lines": [ + { + "bbox": [ + 106, + 301, + 506, + 313 + ], + "spans": [ + { + "bbox": [ + 106, + 301, + 506, + 313 + ], + "score": 1.0, + "content": "CPE is not a simple combination of APE and RPE. 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This", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 334, + 505, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 172, + 347 + ], + "score": 1.0, + "content": "setting achieves", + "type": "text" + }, + { + "bbox": [ + 172, + 334, + 199, + 345 + ], + "score": 0.87, + "content": "7 2 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 199, + 334, + 468, + 347 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet, which is comparable to a single PEG", + "type": "text" + }, + { + "bbox": [ + 469, + 334, + 501, + 345 + ], + "score": 0.87, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 501, + 334, + 505, + 347 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 345, + 505, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 505, + 357 + ], + "score": 1.0, + "content": "Nevertheless, this experiment does not necessarily indicate that our CPE is a simple combination", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 356, + 505, + 368 + ], + "spans": [ + { + "bbox": [ + 105, + 356, + 505, + 368 + ], + "score": 1.0, + "content": "of APE and RPE. When tested on different resolutions, this baseline cannot scale well compared to", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 104, + 367, + 505, + 379 + ], + "spans": [ + { + "bbox": [ + 104, + 367, + 505, + 379 + ], + "score": 1.0, + "content": "ours (Table 8). RPE is not able to adequately mitigate the performance degradation on top of LE.", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 377, + 269, + 390 + ], + "spans": [ + { + "bbox": [ + 105, + 377, + 269, + 390 + ], + "score": 1.0, + "content": "This shall be seen as a major difference.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 17.5, + "bbox_fs": [ + 104, + 301, + 506, + 390 + ] + }, + { + "type": "table", + "bbox": [ + 123, + 437, + 488, + 473 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 107, + 399, + 504, + 423 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 106, + 398, + 504, + 412 + ], + "spans": [ + { + "bbox": [ + 106, + 398, + 504, + 412 + ], + "score": 1.0, + "content": "Table 8. Direct evaluation on other resolutions without fine-tuning. The models are trained on", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 108, + 411, + 414, + 423 + ], + "spans": [ + { + "bbox": [ + 108, + 411, + 146, + 421 + ], + "score": 0.87, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 147, + 411, + 222, + 423 + ], + "score": 1.0, + "content": ". CPE outperforms", + "type": "text" + }, + { + "bbox": [ + 223, + 411, + 260, + 421 + ], + "score": 0.28, + "content": "\\mathrm { L E + R P E }", + "type": "inline_equation" + }, + { + "bbox": [ + 261, + 411, + 414, + 423 + ], + "score": 1.0, + "content": "combination on untrained resolutions.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 22.5 + }, + { + "type": "table_body", + "bbox": [ + 123, + 437, + 488, + 473 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 123, + 437, + 488, + 473 + ], + "spans": [ + { + "bbox": [ + 123, + 437, + 488, + 473 + ], + "score": 0.971, + "html": "
ModelPositionalParams160(%)224(%)384(%)448(%)512(%)
DeiT-tiny (LE+RPE)DeiT-tiny (PEG at Pos 0)40011192065.666.872.472.470.873.268.471.865.670.3
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Model#channels#heads#layers#params
CPVT-Ti1923126M
CPVT-S38461222M
CPVT-B768121286M
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MethodsViTDeiTCPVT
Epochs Batch size300 4096300 1024300 1024
OptimizerAdamWAdamWLAMB
Learning rate decaycosinecosinecosine
Weight decay0.30.050.05
Warmup epochs3.455
Label smoothing ε (Szegedy et al., 2016)X0.1 X0.1
Dropout (Srivastava et al.,2014)0.1X
Stoch.Depth (Huang et al., 2016)X0.1 √0.1
Repeated Aug (Hoffer et al., 2020)XX√
Gradient Clip.√9/0.5X
Rand Augment (Cubuk et al., 2020)X9/0.5
Mixup prob. (Zhang et al.,2018)X0.80.8
Cutmix prob. (Yun et al., 2019)X1.01.0
Erasing prob. (Zhong et al.,2020)X0.250.25
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It’s nontrivial to make", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 161, + 504, + 173 + ], + "spans": [ + { + "bbox": [ + 106, + 161, + 504, + 173 + ], + "score": 1.0, + "content": "absolute positional encodings like DeiT (using learnable positional encoding) translation-equivariant", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 172, + 506, + 185 + ], + "spans": [ + { + "bbox": [ + 105, + 172, + 506, + 185 + ], + "score": 1.0, + "content": "since different absolute positions will be added if the input signal is translated. Note that our method", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 183, + 506, + 195 + ], + "spans": [ + { + "bbox": [ + 105, + 183, + 506, + 195 + ], + "score": 1.0, + "content": "is not strictly translation-equivariant because of the zero padding. 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CPVT architecture variants. The larger model, CPVT-B, has the same architecture as ViT-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 107, + 281, + 504, + 294 + ], + "spans": [ + { + "bbox": [ + 107, + 281, + 504, + 294 + ], + "score": 1.0, + "content": "B (Dosovitskiy et al., 2021) and DeiT-B (Touvron et al., 2020). CPVT-S and CPVT-Ti have the", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 292, + 345, + 306 + ], + "spans": [ + { + "bbox": [ + 106, + 292, + 345, + 306 + ], + "score": 1.0, + "content": "same architecture as DeiT-small and DeiT-tiny respectively", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13 + }, + { + "type": "table_body", + "bbox": [ + 214, + 318, + 396, + 361 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 214, + 318, + 396, + 361 + ], + "spans": [ + { + "bbox": [ + 214, + 318, + 396, + 361 + ], + "score": 0.974, + "html": "
Model#channels#heads#layers#params
CPVT-Ti1923126M
CPVT-S38461222M
CPVT-B768121286M
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MethodsViTDeiTCPVT
Epochs Batch size300 4096300 1024300 1024
OptimizerAdamWAdamWLAMB
Learning rate decaycosinecosinecosine
Weight decay0.30.050.05
Warmup epochs3.455
Label smoothing ε (Szegedy et al., 2016)X0.1 X0.1
Dropout (Srivastava et al.,2014)0.1X
Stoch.Depth (Huang et al., 2016)X0.1 √0.1
Repeated Aug (Hoffer et al., 2020)XX√
Gradient Clip.√9/0.5X
Rand Augment (Cubuk et al., 2020)X9/0.5
Mixup prob. (Zhang et al.,2018)X0.80.8
Cutmix prob. (Yun et al., 2019)X1.01.0
Erasing prob. (Zhong et al.,2020)X0.250.25
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ModelPaddingTop-1 Acc(%)Top-5 Acc(%)
CPVT-Ti√72.491.2
X70.589.8
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PositionsModelParams (M)Top-1 Acc (%）Top-5 Acc (%)
0-1tiny5.772.491.2
0-5tiny5.973.491.8
0-11tiny6.173.491.8
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Under carefully", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 661, + 367, + 673 + ], + "spans": [ + { + "bbox": [ + 105, + 661, + 315, + 673 + ], + "score": 1.0, + "content": "controlled settings, PEG further boosts PVT-tiny by", + "type": "text" + }, + { + "bbox": [ + 315, + 661, + 337, + 672 + ], + "score": 0.84, + "content": "3 . 1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 338, + 661, + 367, + 673 + ], + "score": 1.0, + "content": "mIoU.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30.5, + "bbox_fs": [ + 105, + 628, + 506, + 673 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 687, + 505, + 731 + ], + "lines": [ + { + "bbox": [ + 106, + 687, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 106, + 687, + 505, + 700 + ], + "score": 1.0, + "content": "Object detection on COCO. We also perform controlled experiments with the RetinaNet (Lin", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "et al., 2017) framework on the COCO detection task. The results are shown in Table 13. In the", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 710, + 505, + 722 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 142, + 722 + ], + "score": 1.0, + "content": "standard", + "type": "text" + }, + { + "bbox": [ + 143, + 710, + 157, + 720 + ], + "score": 0.86, + "content": "1 \\times", + "type": "inline_equation" + }, + { + "bbox": [ + 157, + 710, + 309, + 722 + ], + "score": 1.0, + "content": "schedule, PEG improves PVT-tiny by", + "type": "text" + }, + { + "bbox": [ + 309, + 710, + 332, + 721 + ], + "score": 0.9, + "content": "2 . 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 332, + 710, + 405, + 722 + ], + "score": 1.0, + "content": "mAP. PEG brings", + "type": "text" + }, + { + "bbox": [ + 406, + 710, + 428, + 720 + ], + "score": 0.84, + "content": "2 . 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 428, + 710, + 505, + 722 + ], + "score": 1.0, + "content": "higher mAP under", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 721, + 178, + 731 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 121, + 731 + ], + "score": 1.0, + "content": "the", + "type": "text" + }, + { + "bbox": [ + 121, + 721, + 138, + 731 + ], + "score": 0.84, + "content": "3 \\times", + "type": "inline_equation" + }, + { + "bbox": [ + 139, + 721, + 178, + 731 + ], + "score": 1.0, + "content": "schedule.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 34.5, + "bbox_fs": [ + 105, + 687, + 505, + 731 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 110, + 117, + 502, + 241 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 108, + 80, + 502, + 102 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 77, + 504, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 77, + 504, + 95 + ], + "score": 1.0, + "content": "Table 13. Our method boosts the performance of PVT on ImageNet classification, ADE20K seg-", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 91, + 236, + 104 + ], + "spans": [ + { + "bbox": [ + 106, + 91, + 236, + 104 + ], + "score": 1.0, + "content": "mentation and COCO detection", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "table_body", + "bbox": [ + 110, + 117, + 502, + 241 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 117, + 502, + 241 + ], + "spans": [ + { + "bbox": [ + 110, + 117, + 502, + 241 + ], + "score": 0.983, + "html": "
BackboneImageNetSemantic FPN on ADE20KRetinaNet on COCO
Params (M)Top-1 (%)Params (M)mIoU (%）Params (M)mAP (%,1x)mAP (%,3×,+MS)
ResNet-18 (He et al.,2016)1269.81632.92131.835.4
PVT-tiny (Wang et al., 2021)1375.01735.72336.739.4
PVT-tiny+PEG1377.31738.02338.041.8
PVT-tiny+GAP1375.91736.02336.939.7
PVT-tiny+PEG+GAP1378.11738.82338.741.8
PVT-small (Wang et al., 2021)2579.82839.83440.442.2
PVT-small+PEG+GAP2581.22844.33443.045.2
PVT-Medium (Wang et al.,2021)4481.24841.65441.943.2
PVT-Medium+PEG+GAP4482.74844.95444.346.4
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VariantsModelTop-1 Acc (%)
1 Depthwise Conv 3×3tiny72.4
1 Depthwise Conv 7×7tiny72.5
4 *(Depthwise Conv 3×3+BN+ReLU)tiny72.4
1 Dense Conv 3×3tiny72.3
4 * (Dense Conv 3×3+BN+ReLU)tiny72.5
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Through experiments, we find that such a simple design (i.e., depth-", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 543, + 505, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 544, + 127, + 555 + ], + "score": 1.0, + "content": "wise", + "type": "text" + }, + { + "bbox": [ + 127, + 543, + 147, + 554 + ], + "score": 0.87, + "content": "3 \\times 3", + "type": "inline_equation" + }, + { + "bbox": [ + 147, + 544, + 505, + 555 + ], + "score": 1.0, + "content": ") readily achieves on par or even better performance than the recent SOTAs. We give the", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 555, + 271, + 567 + ], + "spans": [ + { + "bbox": [ + 106, + 555, + 271, + 567 + ], + "score": 1.0, + "content": "torch implementation example in Alg. 1.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 20.5 + }, + { + "type": "title", + "bbox": [ + 108, + 581, + 232, + 594 + ], + "lines": [ + { + "bbox": [ + 105, + 581, + 234, + 596 + ], + "spans": [ + { + "bbox": [ + 105, + 581, + 234, + 596 + ], + "score": 1.0, + "content": "D MORE DISCUSSIONS", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 23 + }, + { + "type": "title", + "bbox": [ + 107, + 606, + 357, + 617 + ], + "lines": [ + { + "bbox": [ + 106, + 606, + 358, + 619 + ], + "spans": [ + { + "bbox": [ + 106, + 606, + 358, + 619 + ], + "score": 1.0, + "content": "D.1 WHY RPE WORKS LESS WELL THAN ABSOLUTE PE?", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 24 + }, + { + "type": "text", + "bbox": [ + 107, + 626, + 505, + 682 + ], + "lines": [ + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 505, + 639 + ], + "score": 1.0, + "content": "As mentioned in Section 5.3 (main text), RPE is inferior to the absolute positional encoding. It", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 637, + 505, + 651 + ], + "spans": [ + { + "bbox": [ + 105, + 637, + 505, + 651 + ], + "score": 1.0, + "content": "is because RPE does not encode any absolute position information. Also discussed in Section B.3", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 649, + 506, + 662 + ], + "spans": [ + { + "bbox": [ + 106, + 649, + 506, + 662 + ], + "score": 1.0, + "content": "(main text), absolute position information is also important even for ImageNet classification as it is", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 659, + 505, + 673 + ], + "spans": [ + { + "bbox": [ + 105, + 659, + 505, + 673 + ], + "score": 1.0, + "content": "needed to determine which object is at the center of the image. Note that there might be multiple", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 671, + 457, + 684 + ], + "spans": [ + { + "bbox": [ + 105, + 671, + 457, + 684 + ], + "score": 1.0, + "content": "objects in an image, and the label of an image is the category of the object at the center.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27 + }, + { + "type": "text", + "bbox": [ + 108, + 687, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 687, + 505, + 700 + ], + "spans": [ + { + "bbox": [ + 106, + 687, + 505, + 700 + ], + "score": 1.0, + "content": "Additionally, although RPE becomes popular recently, it is often jointly used with absolute posi-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 505, + 711 + ], + "score": 1.0, + "content": "tional encodings (e.g., in ConViT (d’Ascoli et al., 2021)), or the absolute position information is", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 104, + 708, + 506, + 724 + ], + "spans": [ + { + "bbox": [ + 104, + 708, + 506, + 724 + ], + "score": 1.0, + "content": "leaked in other ways (e.g., convolution paddings in CoAtNet (Dai et al., 2021)). This further sug-", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 721, + 290, + 732 + ], + "spans": [ + { + "bbox": [ + 106, + 721, + 290, + 732 + ], + "score": 1.0, + "content": "gests absolute position information is crucial.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 31.5 + } + ], + "page_idx": 15, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 108, + 27, + 293, + 37 + ], + "lines": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "spans": [ + { + "bbox": [ + 106, + 26, + 293, + 38 + ], + "score": 1.0, + "content": "Published as a conference paper at ICLR 2023", + "type": "text" + } + ] + } + ] + }, + { + "type": "discarded", + "bbox": [ + 301, + 751, + 311, + 760 + ], + "lines": [ + { + "bbox": [ + 299, + 750, + 313, + 764 + ], + "spans": [ + { + "bbox": [ + 299, + 750, + 313, + 764 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 14, + "width": 14 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 110, + 117, + 502, + 241 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 108, + 80, + 502, + 102 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 77, + 504, + 95 + ], + "spans": [ + { + "bbox": [ + 106, + 77, + 504, + 95 + ], + "score": 1.0, + "content": "Table 13. Our method boosts the performance of PVT on ImageNet classification, ADE20K seg-", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 91, + 236, + 104 + ], + "spans": [ + { + "bbox": [ + 106, + 91, + 236, + 104 + ], + "score": 1.0, + "content": "mentation and COCO detection", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "table_body", + "bbox": [ + 110, + 117, + 502, + 241 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 110, + 117, + 502, + 241 + ], + "spans": [ + { + "bbox": [ + 110, + 117, + 502, + 241 + ], + "score": 0.983, + "html": "
BackboneImageNetSemantic FPN on ADE20KRetinaNet on COCO
Params (M)Top-1 (%)Params (M)mIoU (%）Params (M)mAP (%,1x)mAP (%,3×,+MS)
ResNet-18 (He et al.,2016)1269.81632.92131.835.4
PVT-tiny (Wang et al., 2021)1375.01735.72336.739.4
PVT-tiny+PEG1377.31738.02338.041.8
PVT-tiny+GAP1375.91736.02336.939.7
PVT-tiny+PEG+GAP1378.11738.82338.741.8
PVT-small (Wang et al., 2021)2579.82839.83440.442.2
PVT-small+PEG+GAP2581.22844.33443.045.2
PVT-Medium (Wang et al.,2021)4481.24841.65441.943.2
PVT-Medium+PEG+GAP4482.74844.95444.346.4
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VariantsModelTop-1 Acc (%)
1 Depthwise Conv 3×3tiny72.4
1 Depthwise Conv 7×7tiny72.5
4 *(Depthwise Conv 3×3+BN+ReLU)tiny72.4
1 Dense Conv 3×3tiny72.3
4 * (Dense Conv 3×3+BN+ReLU)tiny72.5
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We evaluate its lambda module with an embedding size of 128, where we denote its encoding", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 409, + 505, + 423 + ], + "spans": [ + { + "bbox": [ + 105, + 409, + 505, + 423 + ], + "score": 1.0, + "content": "scheme as RPE2D-d128. Noticeably, this configuration has about 5.9M parameters (comparable to", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 420, + 505, + 435 + ], + "spans": [ + { + "bbox": [ + 105, + 420, + 221, + 435 + ], + "score": 1.0, + "content": "DeiT-tiny) but only obtains", + "type": "text" + }, + { + "bbox": [ + 221, + 421, + 248, + 432 + ], + "score": 0.86, + "content": "6 8 . 7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 248, + 420, + 505, + 435 + ], + "score": 1.0, + "content": ". We attribute its failure to the limited ability in capturing the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 432, + 505, + 444 + ], + "spans": [ + { + "bbox": [ + 105, + 432, + 505, + 444 + ], + "score": 1.0, + "content": "correct positional information. After all, lambda layers are designed with the help of many CNN", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 443, + 504, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 443, + 504, + 455 + ], + "score": 1.0, + "content": "backbones components such as down-sampling to form various stages, to replace ordinary convolu-", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 455, + 402, + 466 + ], + "spans": [ + { + "bbox": [ + 106, + 455, + 402, + 466 + ], + "score": 1.0, + "content": "tions in ResNet (He et al., 2016). 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However, because PEG provides the position in an implicit way, it is interesting to see if PEG", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "score": 1.0, + "content": "can indeed provide the position information as the original positional encodings. Here we inves-", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 538, + 504, + 551 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 464, + 551 + ], + "score": 1.0, + "content": "tigate this by visualizing the attention weights of the transformers. Specifically, given a", + "type": "text" + }, + { + "bbox": [ + 465, + 538, + 504, + 549 + ], + "score": 0.88, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 549, + 505, + 561 + ], + "spans": [ + { + "bbox": [ + 105, + 549, + 155, + 561 + ], + "score": 1.0, + "content": "image (i.e.", + "type": "text" + }, + { + "bbox": [ + 155, + 549, + 184, + 560 + ], + "score": 0.89, + "content": "1 4 \\times 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 185, + 549, + 390, + 561 + ], + "score": 1.0, + "content": "patches), the score matrix within a single head is", + "type": "text" + }, + { + "bbox": [ + 390, + 549, + 429, + 560 + ], + "score": 0.89, + "content": "1 9 6 \\times 1 9 6", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 549, + 505, + 561 + ], + "score": 1.0, + "content": ". We visualize the", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 560, + 377, + 572 + ], + "spans": [ + { + "bbox": [ + 106, + 560, + 377, + 572 + ], + "score": 1.0, + "content": "normalized self-attention score matrix of the second encoder block.", + "type": "text" + } + ], + "index": 45 + } + ], + "index": 42.5 + }, + { + "type": "text", + "bbox": [ + 107, + 577, + 505, + 644 + ], + "lines": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "score": 1.0, + "content": "We first visualize the attention weights of DeiT with the original positional encodings. As shown in", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 588, + 505, + 601 + ], + "spans": [ + { + "bbox": [ + 106, + 588, + 505, + 601 + ], + "score": 1.0, + "content": "Figure 5 (middle), the diagonal element interacts strongly with its local neighbors but weakly with", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 599, + 506, + 612 + ], + "spans": [ + { + "bbox": [ + 105, + 599, + 506, + 612 + ], + "score": 1.0, + "content": "those far-away elements, which suggests that DeiT with the original positional encodings learn to", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 610, + 505, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 610, + 505, + 623 + ], + "score": 1.0, + "content": "attend the local neighbors of each patch. 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As shown in Figure 5 (right),", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 659, + 506, + 673 + ], + "spans": [ + { + "bbox": [ + 105, + 659, + 506, + 673 + ], + "score": 1.0, + "content": "like the original positional encodings, the model with PEG can also learn a similar attention pattern,", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 106, + 671, + 444, + 683 + ], + "spans": [ + { + "bbox": [ + 106, + 671, + 444, + 683 + ], + "score": 1.0, + "content": "which indicates that the proposed PEG can provide the position information as well.", + "type": "text" + } + ], + "index": 54 + } + ], + "index": 53 + }, + { + "type": "text", + "bbox": [ + 107, + 687, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 687, + 506, + 699 + ], + "spans": [ + { + "bbox": [ + 106, + 687, + 506, + 699 + ], + "score": 1.0, + "content": "We illustrate the attention scores in several encoder blocks of DeiT (Touvron et al., 2020) and CPVT", + "type": "text" + } + ], + "index": 55 + }, + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "score": 1.0, + "content": "in the Fig. 6. 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work is also related to Lambda Networks (Bello, 2021) which uses 2D relative positional encod-", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 398, + 505, + 413 + ], + "spans": [ + { + "bbox": [ + 105, + 398, + 505, + 413 + ], + "score": 1.0, + "content": "ings. We evaluate its lambda module with an embedding size of 128, where we denote its encoding", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 409, + 505, + 423 + ], + "spans": [ + { + "bbox": [ + 105, + 409, + 505, + 423 + ], + "score": 1.0, + "content": "scheme as RPE2D-d128. Noticeably, this configuration has about 5.9M parameters (comparable to", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 420, + 505, + 435 + ], + "spans": [ + { + "bbox": [ + 105, + 420, + 221, + 435 + ], + "score": 1.0, + "content": "DeiT-tiny) but only obtains", + "type": "text" + }, + { + "bbox": [ + 221, + 421, + 248, + 432 + ], + "score": 0.86, + "content": "6 8 . 7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 248, + 420, + 505, + 435 + ], + "score": 1.0, + "content": ". We attribute its failure to the limited ability in capturing the", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 432, + 505, + 444 + ], + "spans": [ + { + "bbox": [ + 105, + 432, + 505, + 444 + ], + "score": 1.0, + "content": "correct positional information. After all, lambda layers are designed with the help of many CNN", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 443, + 504, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 443, + 504, + 455 + ], + "score": 1.0, + "content": "backbones components such as down-sampling to form various stages, to replace ordinary convolu-", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 455, + 402, + 466 + ], + "spans": [ + { + "bbox": [ + 106, + 455, + 402, + 466 + ], + "score": 1.0, + "content": "tions in ResNet (He et al., 2016). In contrast, CPVT is transformer-based.", + "type": "text" + } + ], + "index": 38 + } + ], + "index": 35, + "bbox_fs": [ + 105, + 388, + 505, + 466 + ] + }, + { + "type": "title", + "bbox": [ + 109, + 483, + 282, + 494 + ], + "lines": [ + { + "bbox": [ + 106, + 482, + 284, + 496 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 284, + 496 + ], + "score": 1.0, + "content": "D.3 QUALITATIVE ANALYSIS OF CPVT", + "type": "text" + } + ], + "index": 39 + } + ], + "index": 39 + }, + { + "type": "text", + "bbox": [ + 107, + 505, + 505, + 571 + ], + "lines": [ + { + "bbox": [ + 106, + 505, + 505, + 518 + ], + "spans": [ + { + "bbox": [ + 106, + 505, + 505, + 518 + ], + "score": 1.0, + "content": "Thus far, we have shown that PEG can have better performance than the original positional encod-", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 516, + 505, + 528 + ], + "spans": [ + { + "bbox": [ + 105, + 516, + 505, + 528 + ], + "score": 1.0, + "content": "ings. However, because PEG provides the position in an implicit way, it is interesting to see if PEG", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 505, + 540 + ], + "score": 1.0, + "content": "can indeed provide the position information as the original positional encodings. Here we inves-", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 538, + 504, + 551 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 464, + 551 + ], + "score": 1.0, + "content": "tigate this by visualizing the attention weights of the transformers. Specifically, given a", + "type": "text" + }, + { + "bbox": [ + 465, + 538, + 504, + 549 + ], + "score": 0.88, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 549, + 505, + 561 + ], + "spans": [ + { + "bbox": [ + 105, + 549, + 155, + 561 + ], + "score": 1.0, + "content": "image (i.e.", + "type": "text" + }, + { + "bbox": [ + 155, + 549, + 184, + 560 + ], + "score": 0.89, + "content": "1 4 \\times 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 185, + 549, + 390, + 561 + ], + "score": 1.0, + "content": "patches), the score matrix within a single head is", + "type": "text" + }, + { + "bbox": [ + 390, + 549, + 429, + 560 + ], + "score": 0.89, + "content": "1 9 6 \\times 1 9 6", + "type": "inline_equation" + }, + { + "bbox": [ + 429, + 549, + 505, + 561 + ], + "score": 1.0, + "content": ". We visualize the", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 560, + 377, + 572 + ], + "spans": [ + { + "bbox": [ + 106, + 560, + 377, + 572 + ], + "score": 1.0, + "content": "normalized self-attention score matrix of the second encoder block.", + "type": "text" + } + ], + "index": 45 + } + ], + "index": 42.5, + "bbox_fs": [ + 105, + 505, + 505, + 572 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 577, + 505, + 644 + ], + "lines": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "score": 1.0, + "content": "We first visualize the attention weights of DeiT with the original positional encodings. As shown in", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 588, + 505, + 601 + ], + "spans": [ + { + "bbox": [ + 106, + 588, + 505, + 601 + ], + "score": 1.0, + "content": "Figure 5 (middle), the diagonal element interacts strongly with its local neighbors but weakly with", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 599, + 506, + 612 + ], + "spans": [ + { + "bbox": [ + 105, + 599, + 506, + 612 + ], + "score": 1.0, + "content": "those far-away elements, which suggests that DeiT with the original positional encodings learn to", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 610, + 505, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 610, + 505, + 623 + ], + "score": 1.0, + "content": "attend the local neighbors of each patch. After the positional encodings are removed (denoted by", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 620, + 506, + 634 + ], + "spans": [ + { + "bbox": [ + 105, + 620, + 506, + 634 + ], + "score": 1.0, + "content": "DeiT w/o PE), all the patches produce similar attention weights and fail to attend to the patches near", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 632, + 231, + 645 + ], + "spans": [ + { + "bbox": [ + 106, + 632, + 231, + 645 + ], + "score": 1.0, + "content": "themselves, see Figure 5 (left).", + "type": "text" + } + ], + "index": 51 + } + ], + "index": 48.5, + "bbox_fs": [ + 105, + 576, + 506, + 645 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 649, + 504, + 682 + ], + "lines": [ + { + "bbox": [ + 105, + 648, + 505, + 662 + ], + "spans": [ + { + "bbox": [ + 105, + 648, + 505, + 662 + ], + "score": 1.0, + "content": "Finally, we show the attention weights of our CPVT model with PEG. As shown in Figure 5 (right),", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 659, + 506, + 673 + ], + "spans": [ + { + "bbox": [ + 105, + 659, + 506, + 673 + ], + "score": 1.0, + "content": "like the original positional encodings, the model with PEG can also learn a similar attention pattern,", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 106, + 671, + 444, + 683 + ], + "spans": [ + { + "bbox": [ + 106, + 671, + 444, + 683 + ], + "score": 1.0, + "content": "which indicates that the proposed PEG can provide the position information as well.", + "type": "text" + } + ], + "index": 54 + } + ], + "index": 53, + "bbox_fs": [ + 105, + 648, + 506, + 683 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 687, + 504, + 732 + ], + "lines": [ + { + "bbox": [ + 106, + 687, + 506, + 699 + ], + "spans": [ + { + "bbox": [ + 106, + 687, + 506, + 699 + ], + "score": 1.0, + "content": "We illustrate the attention scores in several encoder blocks of DeiT (Touvron et al., 2020) and CPVT", + "type": "text" + } + ], + "index": 55 + }, + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 711 + ], + "score": 1.0, + "content": "in the Fig. 6. It shows both methods learn similar locality patterns. As attention scores are computed", + "type": "text" + } + ], + "index": 56 + }, + { + "bbox": [ + 105, + 710, + 506, + 721 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 506, + 721 + ], + "score": 1.0, + "content": "over the tokens projected in different subspaces (Q and K), they do not necessarily show a strict", + "type": "text" + } + ], + "index": 57 + }, + { + "bbox": [ + 105, + 720, + 498, + 734 + ], + "spans": [ + { + "bbox": [ + 105, + 720, + 498, + 734 + ], + "score": 1.0, + "content": "diagonal pattern, where some may have slight shift, see DeiT in Fig. 6c and CPVT of Fig. 5 right.", + "type": "text" + } + ], + "index": 58 + } + ], + "index": 56.5, + "bbox_fs": [ + 105, + 687, + 506, + 734 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 155, + 81, + 455, + 167 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 155, + 81, + 455, + 167 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 155, + 81, + 455, + 167 + ], + "spans": [ + { + "bbox": [ + 155, + 81, + 455, + 167 + ], + "score": 0.96, + "type": "image", + "image_path": "19c1477edf28cf26db81ecd53fd8947ea0edbe4a9868e6368aab289ef778d7ce.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 155, + 81, + 455, + 109.66666666666667 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 155, + 109.66666666666667, + 455, + 138.33333333333334 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 155, + 138.33333333333334, + 455, + 167.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 107, + 178, + 504, + 223 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 177, + 504, + 191 + ], + "spans": [ + { + "bbox": [ + 106, + 177, + 504, + 191 + ], + "score": 1.0, + "content": "Figure 5. Normalized attention scores (first head) of the second encoder block of DeiT without po-", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 189, + 504, + 201 + ], + "spans": [ + { + "bbox": [ + 106, + 189, + 504, + 201 + ], + "score": 1.0, + "content": "sition encoding (DeiT w/o PE), DeiT (Touvron et al., 2020), and CPVT on the same input sequence.", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 200, + 504, + 213 + ], + "spans": [ + { + "bbox": [ + 105, + 200, + 504, + 213 + ], + "score": 1.0, + "content": "Position encodings are key to developing a schema of locality in lower layers of DeiT. Meantime,", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 107, + 212, + 426, + 223 + ], + "spans": [ + { + "bbox": [ + 107, + 212, + 426, + 223 + ], + "score": 1.0, + "content": "CPVT profits from conditional encodings and follows a similar locality pattern.", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 4.5 + } + ], + "index": 2.75 + }, + { + "type": "image", + "bbox": [ + 115, + 235, + 482, + 419 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 115, + 235, + 482, + 419 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 115, + 235, + 482, + 419 + ], + "spans": [ + { + "bbox": [ + 115, + 235, + 482, + 419 + ], + "score": 0.974, + "type": "image", + "image_path": "0a11531d79480ac414e128993ffaedb8cbf0d456688b90d3ed26f02be66c7b97.jpg" + } + ] + } + ], + "index": 8, + "virtual_lines": [ + { + "bbox": [ + 115, + 235, + 482, + 296.3333333333333 + ], + "spans": [], + "index": 7 + }, + { + "bbox": [ + 115, + 296.3333333333333, + 482, + 357.66666666666663 + ], + "spans": [], + "index": 8 + }, + { + "bbox": [ + 115, + 357.66666666666663, + 482, + 418.99999999999994 + ], + "spans": [], + "index": 9 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 108, + 428, + 504, + 463 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 106, + 428, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 106, + 428, + 505, + 441 + ], + "score": 1.0, + "content": "Figure 6. Normalized attention scores (the second and third head) of the second and third encoder", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 439, + 504, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 439, + 504, + 452 + ], + "score": 1.0, + "content": "block of DeiT (Touvron et al., 2020), and CPVT on the same input sequence. DeiT and CPVT share", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 450, + 391, + 464 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 391, + 464 + ], + "score": 1.0, + "content": "similar locality patterns that are aligned diagonally (some might shift).", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 11 + } + ], + "index": 9.5 + }, + { + "type": "title", + "bbox": [ + 107, + 484, + 309, + 495 + ], + "lines": [ + { + "bbox": [ + 106, + 483, + 311, + 496 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 311, + 496 + ], + "score": 1.0, + "content": "D.4 COMPARISON WITH OTHER APPROACHES", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 13 + }, + { + "type": "text", + "bbox": [ + 107, + 504, + 505, + 581 + ], + "lines": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "spans": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "score": 1.0, + "content": "We further compare our method with other approaches such as CvT (Wu et al., 2021), ConViT", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 515, + 506, + 528 + ], + "spans": [ + { + "bbox": [ + 105, + 515, + 506, + 528 + ], + "score": 1.0, + "content": "(d’Ascoli et al., 2021) and CoAtNet (Dai et al., 2021) on ImageNet validation set in Table 15. To", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 526, + 505, + 539 + ], + "spans": [ + { + "bbox": [ + 105, + 526, + 505, + 539 + ], + "score": 1.0, + "content": "make fair comparisons, we categorize these methods into two groups: plain and pyramid models.", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "score": 1.0, + "content": "Since our models are primarily for plain models, we adapt our methods on two popular pyramid", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 547, + 505, + 561 + ], + "spans": [ + { + "bbox": [ + 105, + 547, + 443, + 561 + ], + "score": 1.0, + "content": "frameworks PVT and Swin. Our CPVT-S-GAP slightly outperforms ConViT-S by", + "type": "text" + }, + { + "bbox": [ + 444, + 549, + 466, + 559 + ], + "score": 0.85, + "content": "0 . 2 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 466, + 547, + 505, + 561 + ], + "score": 1.0, + "content": "with 4M", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 558, + 506, + 572 + ], + "spans": [ + { + "bbox": [ + 105, + 558, + 506, + 572 + ], + "score": 1.0, + "content": "fewer parameters and 0.8G fewer FLOPs. When equipped with pyramid designs, our methods are", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 571, + 261, + 582 + ], + "spans": [ + { + "bbox": [ + 106, + 571, + 261, + 582 + ], + "score": 1.0, + "content": "still comparable to CvT and CoAtNet.", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17 + }, + { + "type": "text", + "bbox": [ + 106, + 594, + 505, + 693 + ], + "lines": [ + { + "bbox": [ + 106, + 593, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 106, + 593, + 505, + 606 + ], + "score": 1.0, + "content": "Comparison with DeiT w/ Convolutional Projection. 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This", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 637, + 506, + 650 + ], + "spans": [ + { + "bbox": [ + 106, + 637, + 221, + 650 + ], + "score": 1.0, + "content": "CvT-flavored DeiT achieves", + "type": "text" + }, + { + "bbox": [ + 221, + 638, + 248, + 648 + ], + "score": 0.86, + "content": "7 0 . 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 249, + 637, + 506, + 650 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet validation set, which is lower than", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 648, + 505, + 662 + ], + "spans": [ + { + "bbox": [ + 105, + 648, + 127, + 662 + ], + "score": 1.0, + "content": "ours", + "type": "text" + }, + { + "bbox": [ + 127, + 649, + 160, + 660 + ], + "score": 0.88, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 160, + 648, + 207, + 662 + ], + "score": 1.0, + "content": ". Note that", + "type": "text" + }, + { + "bbox": [ + 208, + 650, + 214, + 660 + ], + "score": 0.59, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 214, + 648, + 505, + 662 + ], + "score": 1.0, + "content": "-k-v projections in CvT utilize three depthwise convolutions, therefore,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 657, + 506, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 657, + 506, + 675 + ], + "score": 1.0, + "content": "this setting has more parameters than ours. 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Normalized attention scores (first head) of the second encoder block of DeiT without po-", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 189, + 504, + 201 + ], + "spans": [ + { + "bbox": [ + 106, + 189, + 504, + 201 + ], + "score": 1.0, + "content": "sition encoding (DeiT w/o PE), DeiT (Touvron et al., 2020), and CPVT on the same input sequence.", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 200, + 504, + 213 + ], + "spans": [ + { + "bbox": [ + 105, + 200, + 504, + 213 + ], + "score": 1.0, + "content": "Position encodings are key to developing a schema of locality in lower layers of DeiT. 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Normalized attention scores (the second and third head) of the second and third encoder", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 439, + 504, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 439, + 504, + 452 + ], + "score": 1.0, + "content": "block of DeiT (Touvron et al., 2020), and CPVT on the same input sequence. DeiT and CPVT share", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 450, + 391, + 464 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 391, + 464 + ], + "score": 1.0, + "content": "similar locality patterns that are aligned diagonally (some might shift).", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 11 + } + ], + "index": 9.5 + }, + { + "type": "title", + "bbox": [ + 107, + 484, + 309, + 495 + ], + "lines": [ + { + "bbox": [ + 106, + 483, + 311, + 496 + ], + "spans": [ + { + "bbox": [ + 106, + 483, + 311, + 496 + ], + "score": 1.0, + "content": "D.4 COMPARISON WITH OTHER APPROACHES", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 13 + }, + { + "type": "text", + "bbox": [ + 107, + 504, + 505, + 581 + ], + "lines": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "spans": [ + { + "bbox": [ + 105, + 504, + 506, + 517 + ], + "score": 1.0, + "content": "We further compare our method with other approaches such as CvT (Wu et al., 2021), ConViT", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 515, + 506, + 528 + ], + "spans": [ + { + "bbox": [ + 105, + 515, + 506, + 528 + ], + "score": 1.0, + "content": "(d’Ascoli et al., 2021) and CoAtNet (Dai et al., 2021) on ImageNet validation set in Table 15. To", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 526, + 505, + 539 + ], + "spans": [ + { + "bbox": [ + 105, + 526, + 505, + 539 + ], + "score": 1.0, + "content": "make fair comparisons, we categorize these methods into two groups: plain and pyramid models.", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "score": 1.0, + "content": "Since our models are primarily for plain models, we adapt our methods on two popular pyramid", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 547, + 505, + 561 + ], + "spans": [ + { + "bbox": [ + 105, + 547, + 443, + 561 + ], + "score": 1.0, + "content": "frameworks PVT and Swin. 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When equipped with pyramid designs, our methods are", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 571, + 261, + 582 + ], + "spans": [ + { + "bbox": [ + 106, + 571, + 261, + 582 + ], + "score": 1.0, + "content": "still comparable to CvT and CoAtNet.", + "type": "text" + } + ], + "index": 20 + } + ], + "index": 17, + "bbox_fs": [ + 105, + 504, + 506, + 582 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 594, + 505, + 693 + ], + "lines": [ + { + "bbox": [ + 106, + 593, + 505, + 606 + ], + "spans": [ + { + "bbox": [ + 106, + 593, + 505, + 606 + ], + "score": 1.0, + "content": "Comparison with DeiT w/ Convolutional Projection. Note CvT uses a depth-wise convolution", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 604, + 505, + 618 + ], + "spans": [ + { + "bbox": [ + 106, + 604, + 117, + 618 + ], + "score": 1.0, + "content": "in", + "type": "text" + }, + { + "bbox": [ + 117, + 605, + 141, + 617 + ], + "score": 0.33, + "content": "\\scriptstyle q - k - v", + "type": "inline_equation" + }, + { + "bbox": [ + 141, + 604, + 505, + 618 + ], + "score": 1.0, + "content": "projection which they call it Convolutional Projection. Instead of using it in all layers, we", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 615, + 506, + 629 + ], + "spans": [ + { + "bbox": [ + 105, + 615, + 506, + 629 + ], + "score": 1.0, + "content": "put only one of such design into DeiT-tiny and train such a model from scratch under strictly con-", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 627, + 504, + 639 + ], + "spans": [ + { + "bbox": [ + 106, + 627, + 504, + 639 + ], + "score": 1.0, + "content": "trolled settings. We insert it in the position 0 as in our method. The result is shown in Table 16. This", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 637, + 506, + 650 + ], + "spans": [ + { + "bbox": [ + 106, + 637, + 221, + 650 + ], + "score": 1.0, + "content": "CvT-flavored DeiT achieves", + "type": "text" + }, + { + "bbox": [ + 221, + 638, + 248, + 648 + ], + "score": 0.86, + "content": "7 0 . 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 249, + 637, + 506, + 650 + ], + "score": 1.0, + "content": "top-1 accuracy on ImageNet validation set, which is lower than", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 648, + 505, + 662 + ], + "spans": [ + { + "bbox": [ + 105, + 648, + 127, + 662 + ], + "score": 1.0, + "content": "ours", + "type": "text" + }, + { + "bbox": [ + 127, + 649, + 160, + 660 + ], + "score": 0.88, + "content": "( 7 2 . 4 \\% )", + "type": "inline_equation" + }, + { + "bbox": [ + 160, + 648, + 207, + 662 + ], + "score": 1.0, + "content": ". Note that", + "type": "text" + }, + { + "bbox": [ + 208, + 650, + 214, + 660 + ], + "score": 0.59, + "content": "q", + "type": "inline_equation" + }, + { + "bbox": [ + 214, + 648, + 505, + 662 + ], + "score": 1.0, + "content": "-k-v projections in CvT utilize three depthwise convolutions, therefore,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 657, + 506, + 675 + ], + "spans": [ + { + "bbox": [ + 105, + 657, + 506, + 675 + ], + "score": 1.0, + "content": "this setting has more parameters than ours. This attests the difference of CvT and CPVT, verifying", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 671, + 505, + 683 + ], + "spans": [ + { + "bbox": [ + 106, + 671, + 505, + 683 + ], + "score": 1.0, + "content": "our advantage by learning better position encodings other than inserting them in all layers to have", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 682, + 397, + 694 + ], + "spans": [ + { + "bbox": [ + 106, + 682, + 397, + 694 + ], + "score": 1.0, + "content": "the ability to capture local context and to remove ambiguity in attention.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 25, + "bbox_fs": [ + 105, + 593, + 506, + 694 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 169, + 223, + 442, + 337 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 107, + 175, + 505, + 209 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 175, + 505, + 189 + ], + "spans": [ + { + "bbox": [ + 106, + 175, + 505, + 189 + ], + "score": 1.0, + "content": "Table 15. Performance comparison with other approaches such as CvT (Wu et al., 2021), ConViT", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 107, + 187, + 504, + 199 + ], + "spans": [ + { + "bbox": [ + 107, + 187, + 504, + 199 + ], + "score": 1.0, + "content": "(d’Ascoli et al., 2021) and CoAtNet (Dai et al., 2021) on ImageNet validation set. All the models", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 198, + 484, + 211 + ], + "spans": [ + { + "bbox": [ + 106, + 198, + 399, + 211 + ], + "score": 1.0, + "content": "are trained on ImageNet-1k dataset and tested on the validation set using", + "type": "text" + }, + { + "bbox": [ + 399, + 198, + 439, + 208 + ], + "score": 0.89, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + }, + { + "bbox": [ + 439, + 198, + 484, + 211 + ], + "score": 1.0, + "content": "resolution.", + "type": "text" + } + ], + "index": 2 + } + ], + "index": 1 + }, + { + "type": "table_body", + "bbox": [ + 169, + 223, + 442, + 337 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 169, + 223, + 442, + 337 + ], + "spans": [ + { + "bbox": [ + 169, + 223, + 442, + 337 + ], + "score": 0.981, + "html": "
ModelTypeParamsFLOPsTop-1 Acc(%)
DeiT-small (Touvron et al., 2020)ConViT-S (d'Ascoli et al.,2021)CPVT-S-GAP (ours)PlainPlainPlain22M27M23M4.6G5.4G4.6G79.981.381.5
CoAtNet-0 (Dai et al., 2021)CvT-13 (Wu et al., 2021)PVT-small (Wang et al., 2021)PVT-small+PEG+GAPSwin-tiny (Liu et al.,2021)Swin-tiny+PEG+GAPPyramidPyramidPyramidPyramidPyramidPyramid25M20M25M25M29M4.2G4.5G3.8G3.8G4.5G81.681.679.881.281.382.3
29M4.5G
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ModelParamsInsert PositionTop-1 Acc (%)
CPVT-Ti DeiT+ Convolutional Projection5681320 56853520 072.4 70.6
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ModelTypeParamsFLOPsTop-1 Acc(%)
DeiT-small (Touvron et al., 2020)ConViT-S (d'Ascoli et al.,2021)CPVT-S-GAP (ours)PlainPlainPlain22M27M23M4.6G5.4G4.6G79.981.381.5
CoAtNet-0 (Dai et al., 2021)CvT-13 (Wu et al., 2021)PVT-small (Wang et al., 2021)PVT-small+PEG+GAPSwin-tiny (Liu et al.,2021)Swin-tiny+PEG+GAPPyramidPyramidPyramidPyramidPyramidPyramid25M20M25M25M29M4.2G4.5G3.8G3.8G4.5G81.681.679.881.281.382.3
29M4.5G
", + "type": "table", + "image_path": "92a0883388c61f8609d4cd2d321d19b1837a9453c336019225ca6e91ff9d7de4.jpg" + } + ] + } + ], + "index": 4, + "virtual_lines": [ + { + "bbox": [ + 169, + 223, + 442, + 261.0 + ], + "spans": [], + "index": 3 + }, + { + "bbox": [ + 169, + 261.0, + 442, + 299.0 + ], + "spans": [], + "index": 4 + }, + { + "bbox": [ + 169, + 299.0, + 442, + 337.0 + ], + "spans": [], + "index": 5 + } + ] + } + ], + "index": 2.5 + }, + { + "type": "table", + "bbox": [ + 174, + 587, + 437, + 630 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 107, + 538, + 504, + 572 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 107, + 539, + 503, + 551 + ], + "spans": [ + { + "bbox": [ + 107, + 539, + 503, + 551 + ], + "score": 1.0, + "content": "Table 16. Comparison with positional encoding in CvT (Wu et al., 2021) on ImageNet validation set.", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 549, + 503, + 562 + ], + "spans": [ + { + "bbox": [ + 106, + 549, + 464, + 562 + ], + "score": 1.0, + "content": "All the models are trained on ImageNet-1k dataset and tested on the validation set using", + "type": "text" + }, + { + "bbox": [ + 464, + 550, + 503, + 561 + ], + "score": 0.87, + "content": "2 2 4 \\times 2 2 4", + "type": "inline_equation" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 560, + 153, + 573 + ], + "spans": [ + { + "bbox": [ + 105, + 560, + 153, + 573 + ], + "score": 1.0, + "content": "resolution.", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 7 + }, + { + "type": "table_body", + "bbox": [ + 174, + 587, + 437, + 630 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 174, + 587, + 437, + 630 + ], + "spans": [ + { + "bbox": [ + 174, + 587, + 437, + 630 + ], + "score": 0.972, + "html": "
ModelParamsInsert PositionTop-1 Acc (%)
CPVT-Ti DeiT+ Convolutional Projection5681320 56853520 072.4 70.6
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