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f7aaa3c0-262d-4894-a0d2-1a4d50f0117c | 2302.06555v2.pdf | page_header | arXiv:2302.06555v2 [cs.CL] 6 Jul 2024 | null | 39 | 660 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 17.23870086669922, "t": 566.97998046875, "r": 36.33979415893555, "b": 236.99996948242188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 37]}], "orig": "arXiv:2302.06555v2 [cs.CL] 6 Jul ... | null | |
1efbff73-8fa6-4fe1-9456-df4f893eb0cf | 2302.06555v2.pdf | section_header | Do Vision and Language Models Share Concepts? A Vector Space Alignment Study | null | 613 | 60 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 145.64881896972656, "t": 772.0592651367188, "r": 451.7751159667969, "b": 741.8055419921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 76]}], "orig": "Do Vision and Language Model... | null | |
2f6fc597-e04a-4529-8017-f9e746c2bf7c | 2302.06555v2.pdf | section_header | Jiaang Li † Yova Kementchedjhieva ‡ Constanza Fierro † Anders Søgaard † | null | 757 | 26 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/2", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 110.50749206542969, "t": 728.587646484375, "r": 488.74749755859375, "b": 715.568359375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 71]}], "orig": "Jiaang Li \u2020 Yova Kementched... | null | |
835d05b7-a176-4446-8c03-5cfd00098e08 | 2302.06555v2.pdf | text | † University of Copenhagen | null | 267 | 28 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/3", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 233.31304931640625, "t": 700.5826416015625, "r": 366.739990234375, "b": 686.6187744140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 26]}], "orig": "\u2020 University of Copenhagen", "text... | null | |
9960238e-0bda-414a-b41a-5179b70ccfad | 2302.06555v2.pdf | text | ‡ Mohamed bin Zayed University of Artificial Intelligence | null | 558 | 27 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/4", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 160.5359344482422, "t": 686.524658203125, "r": 439.458984375, "b": 672.8939208984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 57]}], "orig": "\u2021 Mohamed bin Zayed University of Artif... | null | |
d223e870-906b-4539-969c-496215626791 | 2302.06555v2.pdf | text | {jili,c.fierro,soegaard}@di.ku.dk, yova.kementchedjhieva@mbzuai.ac.ae | null | 990 | 25 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/5", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 52.79401397705078, "t": 671.6949462890625, "r": 547.7392578125, "b": 659.25048828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 69]}], "orig": "{jili,c.fierro,soegaard}@di.ku.dk, yova.keme... | null | |
7e2361f3-7c96-4be2-ad2b-02bb14073d09 | 2302.06555v2.pdf | section_header | Abstract | null | 91 | 23 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/6", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 158.09361267089844, "t": 635.4051513671875, "r": 203.5260009765625, "b": 623.8914184570312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "Abstract", "text": "Abstract"... | null | |
b4c1466b-92d1-411f-8b3e-c23c96a2cd1c | 2302.06555v2.pdf | text | Large-scale pretrained language models (LMs) are said to "lack the ability to connect utterances to the world" (Bender and Koller, 2020), because they do not have "mental models of the world" (Mitchell and Krakauer, 2023). If so, one would expect LM representations to be unrelated to representations induced by vision m... | null | 355 | 449 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/7", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 92.48450469970703, "t": 607.5615844726562, "r": 270.1062316894531, "b": 382.78509521484375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 774]}], "orig": "Large-scale pretrained language model... | null | |
2aa84d33-241d-454f-aff6-090baeb93711 | 2302.06555v2.pdf | section_header | 1 Introduction | null | 166 | 22 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/8", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 1, "bbox": {"l": 71.96525573730469, "t": 357.04547119140625, "r": 154.81365966796875, "b": 345.7883605957031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "1 Introduction", "text": "1... | null | |
43ebcc8a-6b10-4dc7-b1bb-1d1df53ce5e0 | 2302.06555v2.pdf | text | The debate around whether LMs can be said to understand is often portrayed as a back-and-forth between two opposing sides (Mitchell and Krakauer, 2023), but in reality, there are many positions. Some researchers have argued that LMs are 'all syntax, no semantics', i.e., that they learn form, but not meaning (Searle, 19... | null | 442 | 213 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/9", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 70.90017700195312, "t": 334.8507995605469, "r": 292.1755065917969, "b": 228.49066162109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 400]}], "orig": "The debate around whether LMs can be ... | null | |
15bed161-2aec-4ca4-ab02-b65cd1cab586 | 2302.06555v2.pdf | text | dataset: | null | 57 | 17 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/10", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 141.05947875976562, "t": 217.11761474609375, "r": 169.70974731445312, "b": 208.6852569580078, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 8]}], "orig": "dataset:", "text": "dataset:"} | null | |
7e34928e-b3ee-434d-94db-81cb08d3c4d6 | 2302.06555v2.pdf | text | https://github.com/ | null | 206 | 19 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/11", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 188.3842010498047, "t": 217.300048828125, "r": 291.3439636230469, "b": 208.0350341796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 19]}], "orig": "https://github.com/", "text": "https://... | null | |
9bf2f166-4937-4ace-bb94-dfb344f88b46 | 2302.06555v2.pdf | text | $^{1}$Code and jiaangli/VLCA . | null | 144 | 37 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/12", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 72.0, "t": 216.7012176513672, "r": 144.17959594726562, "b": 197.72625732421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 30]}], "orig": "$^{1}$Code and jiaangli/VLCA .", "text": "$^{1}$C... | null | |
6321b136-a71f-471e-a05c-035bebe99d9a | 2302.06555v2.pdf | text | $^{2}$The idea that computers are 'all syntax, no semantics' can be traced back to German 17th century philosopher Leibniz's Mill Argument (Lodge and Bobro, 1998). The Mill Argument states that mental states cannot be reduced to physical states, so if the capacity to understand language requires mental states, this cap... | null | 442 | 238 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/13", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 71.13861846923828, "t": 195.3333740234375, "r": 291.759765625, "b": 76.2840576171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 651]}], "orig": "$^{2}$The idea that computers are 'all syn... | null | |
8bb27c97-a19f-443c-b6c3-10c0c3fea2fa | 2302.06555v2.pdf | text | have inferential semantics, but not referential semantics (Rapaport, 2002; Sahlgren and Carlsson, 2021; Piantadosi and Hill, 2022), 3 whereas some have posited that a form of externalist referential semantics is possible, at least for chatbots engaged in direct conversation (Cappelen and Dever, 2021; Butlin, 2021; Moll... | null | 443 | 320 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/14", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 306.2083740234375, "t": 634.934326171875, "r": 527.3598022460938, "b": 474.9033508300781, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 579]}], "orig": "have inferential semantics, but not re... | null | |
78879112-8454-4241-9808-cdc6670cb00a | 2302.06555v2.pdf | text | This study provides evidence to the contrary: Language models and computer vision models (VMs) are trained on independent data sources (at least for unsupervised computer vision models). The only common source of bias is the world. If LMs and VMs exhibit similarities, it must be because they both model the world. We ex... | null | 443 | 457 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/15", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 305.9925231933594, "t": 471.6637268066406, "r": 527.4515380859375, "b": 242.90240478515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 809]}], "orig": "This study provides evidence to the ... | null | |
13524540-4a6d-47b7-b3ad-05ded451e35c | 2302.06555v2.pdf | text | Contributions. We present a series of evaluations of the vector spaces induced by three families of VMs and four families of LMs, i.e., a total of fourteen VMs and fourteen LMs. We show that within each family, the larger the LMs, the more their vector spaces become structurally similar to | null | 442 | 158 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/16", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 306.3404541015625, "t": 230.98822021484375, "r": 527.3585815429688, "b": 151.7589111328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 290]}], "orig": "Contributions. We present a series o... | null | |
35f66de4-2a68-4b7f-99ab-fe619540be51 | 2302.06555v2.pdf | text | receives text messages in this language and follows a rule book to reply to the messages. The interlocutor is Searle's caricature of artificial intelligence, and is obviously, Searle claims, not endowed with meaning or understanding, but merely symbol manipulation. | null | 439 | 106 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/17", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 306.3285217285156, "t": 140.958984375, "r": 525.7665405273438, "b": 88.13725280761719, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 265]}], "orig": "receives text messages in this language a... | null | |
cbc18cec-292c-43a7-a222-f02d37ed6869 | 2302.06555v2.pdf | text | $^{3}$See Marconi (1997) for this distinction. | null | 291 | 16 | 72 | image/png | 2 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/18", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 319.9280090332031, "t": 85.00321197509766, "r": 465.3711242675781, "b": 76.98725128173828, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 46]}], "orig": "$^{3}$See Marconi (1997) for this dist... | null | |
a4008690-b978-45e3-ab18-4930e3416f4c | 2302.06555v2.pdf | text | those of computer vision models. This enables retrieval of language representations of images (referential semantics) with minimal supervision. Retrieval precision depends on dispersion of image and language, polysemy, and frequency, but consistently improves with language model size. We discuss the implications of the... | null | 442 | 238 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/19", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 71.13309478759766, "t": 776.2091064453125, "r": 292.083251953125, "b": 657.3726196289062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 416]}], "orig": "those of computer vision models. This ... | null | |
7c58bc30-6e15-40ce-b04a-380f6e1ab0e2 | 2302.06555v2.pdf | section_header | 2 Related Work | null | 180 | 23 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/20", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 71.47272491455078, "t": 645.0098876953125, "r": 161.3809814453125, "b": 633.350341796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "2 Related Work", "text": "2 R... | null | |
553eacd2-92df-4108-be99-f90ab08aac40 | 2302.06555v2.pdf | text | Inspiration from cognitive science. Computational modeling is a cornerstone of cognitive science in the pursuit for a better understanding of how representations in the brain come about. As such, the field has shown a growing interest in computational representations induced with self-supervised learning (Orhan et al.,... | null | 442 | 373 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/21", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 71.06283569335938, "t": 622.7850952148438, "r": 292.0802001953125, "b": 436.3576354980469, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 659]}], "orig": "Inspiration from cognitive science. C... | null | |
e92fdcac-17c2-416a-a570-6a4387d1f6b8 | 2302.06555v2.pdf | text | Studies have looked at the alignability of neural language representations and human brain activations, with more promising results as language models grow better at modeling language (Sassenhagen and Fiebach, 2020; Schrimpf et al., 2021). In these studies, the partial alignability of brain and model representations is... | null | 441 | 240 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/22", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 71.25102233886719, "t": 433.5199279785156, "r": 292.1755065917969, "b": 313.6751708984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 434]}], "orig": "Studies have looked at the alignabili... | null | |
3da74941-87ce-468b-8e8a-cd69a132d125 | 2302.06555v2.pdf | text | Cross-modal alignment. The idea of crossmodal retrieval is not new (Lazaridou et al., 2014), but previously it has mostly been studied with practical considerations in mind. Recently, Merullo et al. (2023) showed that language representations in LMs are functionally similar to image representations in VMs, in that a li... | null | 442 | 457 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/23", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 71.18010711669922, "t": 304.51641845703125, "r": 292.0832214355469, "b": 76.014892578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 828]}], "orig": "Cross-modal alignment. The idea of cro... | null | |
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6413aaf0-41b3-4717-8799-d013d7a0fea6 | 2302.06555v2.pdf | caption | Figure 1: Mapping from MAE$_{Huge}$ (images) to OPT$_{30B}$ (text). Gold labels are in green. | null | 438 | 48 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/24", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 2, "bbox": {"l": 306.5188903808594, "t": 511.5455017089844, "r": 525.5476684570312, "b": 487.3186340332031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "orig": "Figure 1: Mapping from MAE$_{Huge}$... | null | |
ab01c853-c183-4c88-968b-f0ded2001a60 | 2302.06555v2.pdf | picture | null | Figure 1: Mapping from MAE$_{Huge}$ (images) to OPT$_{30B}$ (text). Gold labels are in green. | 124 | 103 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/pictures/7", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 2, "bbox": {"l": 377.6405029296875, "t": 580.822265625, "r": 439.61199951171875, "b": 529.2573852539062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 93]}], "captions": [{"cref": "#/texts/24"}], "refere... | null | |
005ca179-8bd9-467e-a2cb-95f325429644 | 2302.06555v2.pdf | text | Huh et al. (2024) proposes a similar hypothesis, although studying it from a different perspective, and our findings corroborate theirs. | null | 441 | 76 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/25", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 306.4500427246094, "t": 463.1133728027344, "r": 526.9063720703125, "b": 424.90692138671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "Huh et al. (2024) proposes a similar... | null | |
727ec5f5-36bf-4d02-b9c4-e4782346cbcf | 2302.06555v2.pdf | section_header | 3 Methodology | null | 172 | 24 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/26", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 2, "bbox": {"l": 306.342529296875, "t": 412.8549499511719, "r": 392.2894287109375, "b": 400.87078857421875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "3 Methodology", "text": "3 M... | null | |
be759c2b-2025-4928-8f5d-db721824f5ac | 2302.06555v2.pdf | text | Our primary objective is to compare the representations derived from VMs and LMs and assess their alignability, i.e. the extent to which LMs converge toward VMs' geometries. In the following sections, we introduce the procedures for obtaining the representations and aligning them, with an illustration of our methodolog... | null | 442 | 186 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/27", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 306.3543395996094, "t": 390.9617919921875, "r": 527.3591918945312, "b": 297.9390869140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 343]}], "orig": "Our primary objective is to compare t... | null | |
7a40986a-6ccd-463e-88ae-91d4adc8cb14 | 2302.06555v2.pdf | text | Vision models. We include fourteen VMs in our experiments, representing three model families: SegFormer (Xie et al., 2021), MAE (He et al., 2022), and ResNet (He et al., 2016). For all three types of VMs, we only employ the encoder component as a visual feature extractor. 4 | null | 442 | 157 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/28", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 306.2715148925781, "t": 288.335693359375, "r": 527.359375, "b": 210.1456298828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 274]}], "orig": "Vision models. We include fourteen VMs in our... | null | |
2d6f6757-f889-4c4f-84e5-e1b6fca83920 | 2302.06555v2.pdf | text | SegFormer models consist of a Transformerbased encoder and a light-weight feed-forward decoder. They are pretrained on object classification data and finetuned on scene parsing data for scene segmentation and object classification. We hypothesize that the reasoning necessary to | null | 443 | 158 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/29", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 2, "bbox": {"l": 306.04150390625, "t": 207.15533447265625, "r": 527.4514770507812, "b": 127.76580810546875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 278]}], "orig": "SegFormer models consist of a Transfo... | null | |
8572076e-cbfc-4318-8d1d-4c83905882ef | 2302.06555v2.pdf | footnote | $^{4}$We ran experiments with CLIP (Radford et al., 2021), but report on these separately, since CLIP does not meet the criteria of our study, being trained on a mixture of text and images. CLIP results are presented in Appendix C. | null | 440 | 86 | 72 | image/png | 3 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/30", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 2, "bbox": {"l": 306.3114013671875, "t": 119.45751953125, "r": 526.66015625, "b": 76.37322998046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 231]}], "orig": "$^{4}$We ran experiments with CLIP (Radf... | null | |
2ac5a30c-715e-483c-9d3d-113e3844e6a6 | 2302.06555v2.pdf | caption | Figure 2: Experiments stages: During our experiments, words, sentences, and images are selected from the aliases list (wordlist and ImageNet-21K aliases), Wikipedia and ImageNet-21K, respectively. The source and target spaces are constructed utilizing image and word embeddings which are extracted by specialized vision ... | null | 909 | 103 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/31", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 3, "bbox": {"l": 71.41864013671875, "t": 595.5836181640625, "r": 525.9263305664062, "b": 543.7525024414062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "orig": "Figure 2: Experiments stages: Duri... | null | |
7d1defea-4674-4d83-93b4-7dc5fd44cef9 | 2302.06555v2.pdf | picture | null | Figure 2: Experiments stages: During our experiments, words, sentences, and images are selected from the aliases list (wordlist and ImageNet-21K aliases), Wikipedia and ImageNet-21K, respectively. The source and target spaces are constructed utilizing image and word embeddings which are extracted by specialized vision ... | 909 | 332 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/pictures/8", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 3, "bbox": {"l": 70.01079559326172, "t": 777.8876953125, "r": 524.3985595703125, "b": 611.6873168945312, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 340]}], "captions": [{"cref": "#/texts/31"}], "refer... | null | |
00423384-00ba-411c-87d9-5681643567ad | 2302.06555v2.pdf | text | perform segmentation in context promotes representations that are more similar to those of LMs, which also operate in a discrete space (a vocabulary). The SegFormer models we use are pretrained with ImageNet-1K (Russakovsky et al., 2015) and finetuned with ADE20K (Zhou et al., 2017). | null | 442 | 157 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/32", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 71.2052993774414, "t": 519.85791015625, "r": 292.0829162597656, "b": 441.4917297363281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 284]}], "orig": "perform segmentation in context promotes... | null | |
d85c634d-dbb3-48ac-9c94-342db0c51fb3 | 2302.06555v2.pdf | text | MAE models relies on a Transformer-based encoder-decoder architecture, with the VisionTransformer (ViT) (Dosovitskiy et al., 2021) as the encoder backbone. MAE models are trained to reconstruct masked patches in images, i.e., a fully unsupervised training objective, similar to masked language modeling. The encoder take... | null | 442 | 320 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/33", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 71.08782196044922, "t": 437.7105407714844, "r": 292.07537841796875, "b": 277.903564453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 552]}], "orig": "MAE models relies on a Transformer-ba... | null | |
a5381d8b-f763-484f-9b97-4bb4c9d89391 | 2302.06555v2.pdf | text | ResNet models for object classification consist of a bottleneck convolutional neural network with residual blocks as an encoder, with a classification head. They are pretrained on the ImageNet-1K. | null | 438 | 104 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/34", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 71.25859069824219, "t": 274.5047607421875, "r": 290.271728515625, "b": 222.460205078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 196]}], "orig": "ResNet models for object classification... | null | |
5adb76e8-32a6-481f-924e-e903b49075e4 | 2302.06555v2.pdf | text | Language models. We include fourteen Transformer-based LMs in our experiments, representing four model families: BERT (Devlin et al., 2019), GPT-2 (Radford et al., 2019), OPT (Zhang et al., 2022) and LLaMA-2 (Touvron et al., 2023). We use six different sizes of BERT (all uncased): BERT$_{Base}$ and BERT$_{Large}$, whic... | null | 443 | 266 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/35", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 70.57910919189453, "t": 208.846435546875, "r": 292.1816101074219, "b": 76.1505126953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 490]}], "orig": "Language models. We include fourteen Tr... | null | |
35c1f1fe-1b43-4ff4-8be7-b962ec994878 | 2302.06555v2.pdf | text | GPT-2, an auto-regressive decoder-only LM, comes in three sizes, pretrained on the WebText dataset (Radford et al., 2019). OPT also comes in three sizes, pretrained on the union of five datasets (Zhang et al., 2022). LLaMA-2 was pretrained on two trillion tokens. | null | 441 | 158 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/36", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 306.2762145996094, "t": 520.4324340820312, "r": 526.9061889648438, "b": 441.523681640625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 263]}], "orig": "GPT-2, an auto-regressive decoder-only... | null | |
1d1c5a43-a827-4da9-af56-3b13c0e76332 | 2302.06555v2.pdf | text | Vision representations. The visual representation of a concept is obtained by embedding the images available for the concept with a given VM encoder and then averaging these representations. When applying SegFormer, we average the patches' representations from the last hidden state as the basis for every image, whereas... | null | 442 | 266 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/37", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 306.2260437011719, "t": 429.16314697265625, "r": 527.4528198242188, "b": 296.0677490234375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 461]}], "orig": "Vision representations. The visual r... | null | |
a561ca2b-6f29-40bb-88cd-8a7475f2c7d2 | 2302.06555v2.pdf | text | Language representations. The LMs included here were trained on text segments, so applying them to words in isolation could result in unpredictable behavior. We therefore represent words by embedding English Wikipedia sentences, using the token representations that form the concept, decontextualizing these representati... | null | 442 | 239 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/38", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 3, "bbox": {"l": 306.23394775390625, "t": 283.43695068359375, "r": 527.3591918945312, "b": 164.11749267578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 434]}], "orig": "Language representations. The LMs ... | null | |
58b5272c-7ffa-4e31-a994-75ac7501824f | 2302.06555v2.pdf | footnote | $^{5}$We also experimented with utilizing the representations from the last hidden state; however, the results were not as promising as those obtained from the penultimate hidden state. Caron et al. (2021) demonstrate the penultimate-layer features in ViTs trained with DINO exhibit strong correlations with saliency inf... | null | 441 | 151 | 72 | image/png | 4 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/39", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 3, "bbox": {"l": 306.2687072753906, "t": 152.629638671875, "r": 527.111083984375, "b": 76.98725891113281, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 386]}], "orig": "$^{5}$We also experimented with uti... | null | |
1bb9027b-a593-4c15-a20d-54e53fbb922d | 2302.06555v2.pdf | text | an averaging approach on the token representations forming the concept; otherwise, we choose for the last token within the concept (Zou et al., 2023). | null | 438 | 75 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/40", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 71.54341125488281, "t": 776.0916748046875, "r": 290.26824951171875, "b": 738.40966796875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 150]}], "orig": "an averaging approach on the token rep... | null | |
4e3bb844-dfc1-4d69-8906-303f9098040b | 2302.06555v2.pdf | text | Linear projection. Since we are interested in the extent to which vision and language representations are isomorphic, we focus on linear projections. 6 Following Conneau et al. (2018), we use Procrustes analysis (Schönemann, 1966) to align the representations of VMs to those of LMs, given a bimodal dictionary (§ 4.1). ... | null | 442 | 618 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/41", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 70.89579772949219, "t": 726.5772705078125, "r": 292.08294677734375, "b": 417.6516418457031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 1121]}], "orig": "Linear projection. Since we are int... | null | |
db03dd4e-1bdf-433d-b4bd-05021749e207 | 2302.06555v2.pdf | section_header | 4 Experimental Setup | null | 242 | 24 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/42", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 71.08319091796875, "t": 403.1234130859375, "r": 191.88674926757812, "b": 390.90045166015625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 20]}], "orig": "4 Experimental Setup", "te... | null | |
33c633e8-b91c-4fde-90cf-a16b59f21946 | 2302.06555v2.pdf | text | In this section, we discuss details around bimodal dictionary compilation (§ 4.1), evaluation metrics, as well as our baselines (§ 4.2). | null | 441 | 76 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/43", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 71.15853881835938, "t": 380.3851623535156, "r": 291.63519287109375, "b": 342.40765380859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 136]}], "orig": "In this section, we discuss details... | null | |
288e8dfc-c707-4f66-b71e-f47ce763588a | 2302.06555v2.pdf | section_header | 4.1 Bimodal Dictionary Compilation | null | 357 | 22 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/44", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 71.00336456298828, "t": 329.13519287109375, "r": 249.28378295898438, "b": 317.93157958984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 34]}], "orig": "4.1 Bimodal Dictionary Co... | null | |
1afe2f60-0a37-4fee-b2e2-ebdd5f6eb3c1 | 2302.06555v2.pdf | text | We build bimodal dictionaries of image-text pairs based on the ImageNet21K dataset (Russakovsky et al., 2015) and the CLDI (cross-lingual dictionary induction) dataset (Hartmann and Søgaard, 2018). In ImageNet, a concept class has a unique ID and is represented by multiple images and one or more names (which we refer t... | null | 442 | 295 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/45", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 70.98979187011719, "t": 310.3228759765625, "r": 292.07537841796875, "b": 163.00567626953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 537]}], "orig": "We build bimodal dictionaries of im... | null | |
c20d0945-d3c3-440c-9645-e23e6f264b44 | 2302.06555v2.pdf | footnote | $^{6}$For work on non-linear projection between representation spaces, see Nakashole (2018); Zhao and Gilman (2020); Glavaš and Vuli´c (2020). | null | 441 | 63 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/46", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 71.50354766845703, "t": 152.3046875, "r": 291.7594909667969, "b": 120.85226440429688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 142]}], "orig": "$^{6}$For work on non-linear projectio... | null | |
8242f6ac-1f27-4b3f-9a93-f2fdc5fe0d78 | 2302.06555v2.pdf | footnote | $^{7}$The variance is retained for most models after dimensionality reduction, except for a few cases where there is some loss of information. The cumulative of explained variance ratios for different models are presented in Table 8. | null | 441 | 85 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/47", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 71.3525619506836, "t": 118.89080810546875, "r": 291.7541809082031, "b": 76.62451171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 233]}], "orig": "$^{7}$The variance is retained for m... | null | |
65f280ac-2ac5-43b8-903c-d0c2b8f47d32 | 2302.06555v2.pdf | caption | Table 1: Statistics of the bimodal dictionaries. | null | 408 | 21 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/48", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 4, "bbox": {"l": 313.8568420410156, "t": 716.146240234375, "r": 517.947021484375, "b": 705.5986328125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 48]}], "orig": "Table 1: Statistics of the bimodal dicti... | null | |
958fcef6-7205-483c-a140-0bc9acb055f1 | 2302.06555v2.pdf | table | <table><tbody><tr><th>Set</th><th>Num. of classes</th><th>Num. of aliases</th><th>Num. of pairs</th></tr><tr><td>Only-1K</td><td>491</td><td>655</td><td>655</td></tr><tr><td>Exclude-1K</td><td>5,942</td><td>7,194</td><td>7,194</td></tr><tr><td>EN-CLDI</td><td>1,690</td><td>1,690</td><td>1,690</td></tr></tbody></table> | Table 1: Statistics of the bimodal dictionaries. | 435 | 103 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/tables/0", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 4, "bbox": {"l": 308.0361328125, "t": 778.875, "r": 525.6458740234375, "b": 727.481201171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/48"}], "references": [], "foot... | null | |
27d964d3-7aea-42d2-aa69-cddcfe5b13ef | 2302.06555v2.pdf | text | least one alias. As a result, 11,338 classes and 13,460 aliases meet the criteria. We further filter aliases that are shared by two different class IDs, and aliases for which their hyponyms are already in the aliases set. 8 To avoid any form of bias, given that the VMs we experiment with have been pretrained on ImageNe... | null | 442 | 237 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/49", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 306.3482360839844, "t": 682.125, "r": 527.3558349609375, "b": 563.3076171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 411]}], "orig": "least one alias. As a result, 11,338 classes and 1... | null | |
6a58186a-e7f7-4af5-bed5-9cd344e844e7 | 2302.06555v2.pdf | text | One important limitation of the Exclude-1K bimodal dictionary is that all concepts are nouns. Therefore, to investigate how our results generalize to other parts of speech (POS), we also use the English subset of CLDI dataset (EN-CLDI), which contains images paired with verbs and adjectives. Each word within this set i... | null | 443 | 238 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/50", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 306.14984130859375, "t": 560.3670654296875, "r": 527.4514770507812, "b": 441.3656311035156, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 432]}], "orig": "One important limitation of the Excl... | null | |
4486b15d-794a-4f20-87c0-89454067deb3 | 2302.06555v2.pdf | text | The pairs in these bimodal dictionaries are split 70-30 for training and testing based on the class IDs to avoid train-test leakage. 9 We compute five such splits at random and report averaged results. See § 6 for the impact of training set size variations. | null | 442 | 131 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/51", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 306.2119140625, "t": 438.3727722167969, "r": 527.4554443359375, "b": 373.1012878417969, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 257]}], "orig": "The pairs in these bimodal dictionaries ... | null | |
57508efd-4361-4518-8d6f-0a141a1b30a1 | 2302.06555v2.pdf | section_header | 4.2 Evaluation | null | 152 | 22 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/52", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 4, "bbox": {"l": 306.46258544921875, "t": 362.63116455078125, "r": 382.63604736328125, "b": 351.5690002441406, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 14]}], "orig": "4.2 Evaluation", "text": ... | null | |
fb9d2a8c-5170-4f2d-99f2-e9ede95d7298 | 2302.06555v2.pdf | text | We induce a linear mapping Ω based on training image-text pairs sampled from A and B , respectively. We then evaluate how close A Ω is to B by computing retrieval precision on held-out imagetext pairs. To make the retrieval task as challenging as possible, the target space B is expanded with 65,599 words from an Englis... | null | 442 | 240 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/53", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 306.2489013671875, "t": 345.0776672363281, "r": 527.352783203125, "b": 225.149169921875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 427]}], "orig": "We induce a linear mapping \u2126 based... | null | |
e4e28a87-f60f-436a-b661-d73190c39776 | 2302.06555v2.pdf | text | Metrics. We evaluate alignment in terms of precision-atk (P@ k ), a well-established metric employed in the evaluation of multilingual word embeddings (Conneau et al., 2018), with k ∈ { 1 , 10 , 100 } . 10 Note that this performance metric | null | 438 | 134 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/54", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 4, "bbox": {"l": 306.3006286621094, "t": 215.8372802734375, "r": 525.5789184570312, "b": 148.8270263671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 239]}], "orig": "Metrics. We evaluate alignment in ter... | null | |
510f1161-7607-48cd-8001-42c1c446291e | 2302.06555v2.pdf | footnote | $^{8}$We obtain the aliases hypernyms and hyponyms from the Princeton WordNet (Fellbaum, 2010). | null | 438 | 41 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/55", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 306.5758361816406, "t": 141.65576171875, "r": 525.547119140625, "b": 120.88531494140625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 95]}], "orig": "$^{8}$We obtain the aliases hypernym... | null | |
62693f5e-2a68-4dc6-9215-cfcc0d77a24e | 2302.06555v2.pdf | footnote | $^{9}$In the EN-CLDI set, we simply use words to mitigate the risk of train-test leakage. | null | 437 | 40 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/56", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 306.7635803222656, "t": 119.1673583984375, "r": 525.5420532226562, "b": 98.96624755859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 89]}], "orig": "$^{9}$In the EN-CLDI set, we simpl... | null | |
817261b3-6267-4805-89fa-7af4c9287a4f | 2302.06555v2.pdf | footnote | $^{10}$For example, we could use the mapping of the image of an apple into the word ‘apple’, and the mapping of the image | null | 439 | 42 | 72 | image/png | 5 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/57", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 4, "bbox": {"l": 306.67706298828125, "t": 96.8956298828125, "r": 525.7845458984375, "b": 75.97491455078125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 121]}], "orig": "$^{10}$For example, we could use ... | null | |
e038a730-a293-4950-a8fd-df077cd03f23 | 2302.06555v2.pdf | caption | Table 2: Alignment results for our baselines. All the Precision@ k scores are reported in percentage. | null | 439 | 49 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/58", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 71.00687408447266, "t": 713.8568115234375, "r": 290.2685546875, "b": 689.1964721679688, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 101]}], "orig": "Table 2: Alignment results for our ba... | null | |
908f9f51-c9ec-454a-8c0e-c609f8146b96 | 2302.06555v2.pdf | table | <table><tbody><tr><th>Baseline</th><th>P@1</th><th>P@10</th><th>P@100</th></tr><tr><td>Random retrieval</td><td>0.0015</td><td>0.0153</td><td>0.1531</td></tr><tr><td>Length-frequency alignment</td><td>0.0032</td><td>0.0127</td><td>0.6053</td></tr><tr><td>Non-isomorphic alignment</td><td>0.0000</td><td>0.0121</td><td>0.... | Table 2: Alignment results for our baselines. All the Precision@ k scores are reported in percentage. | 437 | 111 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/tables/1", "parent": {"cref": "#/body"}, "children": [], "label": "table", "prov": [{"page_no": 5, "bbox": {"l": 72.87548065185547, "t": 779.512451171875, "r": 291.2668762207031, "b": 723.8392333984375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 0]}], "captions": [{"cref": "#/texts/58"}], "reference... | null | |
9cffc2a5-950c-425e-848f-99d35b459371 | 2302.06555v2.pdf | text | is much more conservative than other metrics used for similar problems, including pairwise matching accuracy, percentile rank, and Pearson correlation (Minnema and Herbelot, 2019). Pairwise matching accuracy and percentile rank have random baseline scores of 0.5, and they converge in the limit. If a has a percentile ra... | null | 441 | 485 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/59", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 71.28717041015625, "t": 664.7708740234375, "r": 292.0834045410156, "b": 422.3381652832031, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 887]}], "orig": "is much more conservative than other ... | null | |
5c634071-8d05-4683-9f05-c402eec59de6 | 2302.06555v2.pdf | text | Random retrieval baseline. Our target space of 79,059 words makes the random retrieval baseline: | null | 442 | 49 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/60", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 71.1332015991211, "t": 412.76654052734375, "r": 291.7831726074219, "b": 388.6746520996094, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 96]}], "orig": "Random retrieval baseline. Our target ... | null | |
1278ce02-1320-42a5-9cfb-d30565291a10 | 2302.06555v2.pdf | formula | P@ 1 = 1 N N ∑ i =1 n$_{i}$ U (1) | null | 301 | 70 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/61", "parent": {"cref": "#/body"}, "children": [], "label": "formula", "prov": [{"page_no": 5, "bbox": {"l": 140.55914306640625, "t": 377.66351318359375, "r": 290.9989929199219, "b": 342.2218017578125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 33]}], "orig": "P@ 1 = 1 N N \u2211 i =1 n$_{i}$ ... | null | |
89af5241-b077-4675-971d-19f36ea358fa | 2302.06555v2.pdf | text | where N represents the total number of image classes; i iterates over each image class; n$_{i}$ denotes the number of labels for image class i ; U refers to the total number of unique aliases. From Equation 1, we get P@1 ≈ 0 . 0015% . | null | 441 | 130 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/62", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 71.25822448730469, "t": 331.0116271972656, "r": 292.0785217285156, "b": 265.8031005859375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 234]}], "orig": "where N represents the total number o... | null | |
55e1d195-297b-4a19-9e09-b0abb40ec9f9 | 2302.06555v2.pdf | text | Length-frequency alignment baseline. The random retrieval baseline tells us how well we can align representations across the two modalities in the absence of any signal (by chance). However, the fact that we can do better than a random baseline, does not, strictly speaking, prove that our models partially converge towa... | null | 442 | 213 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/63", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 71.22360229492188, "t": 255.29852294921875, "r": 292.08270263671875, "b": 149.0504150390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 386]}], "orig": "Length-frequency alignment baseline... | null | |
315395b8-2614-46b1-9ef5-e562835a112b | 2302.06555v2.pdf | footnote | of a banana into the word ‘banana’, as training pairs to induce a mapping Ω . If Ω then maps the image of a lemon onto the word ‘lemon’ as its nearest neighbor, we say that the precisionat-one for this mapping is 100%. If two target aliases were listed in the bimodal dictionary for the source image, mapping the image o... | null | 442 | 130 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/64", "parent": {"cref": "#/body"}, "children": [], "label": "footnote", "prov": [{"page_no": 5, "bbox": {"l": 70.99034881591797, "t": 141.355224609375, "r": 291.7580261230469, "b": 76.57489013671875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 368]}], "orig": "of a banana into the word \u2018ba... | null | |
801cd3c0-c84e-4cd1-9371-710f36591cc2 | 2302.06555v2.pdf | caption | Figure 3: t-SNE plot of 5 words mapped from MAE$_{Huge}$ (blue) to OPT$_{30B}$ (orange) using Procrustes analysis. The green represent the mapped MAE$_{Huge}$ embeddings. | null | 442 | 105 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/65", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 5, "bbox": {"l": 306.365478515625, "t": 638.8778076171875, "r": 527.3575439453125, "b": 586.4058837890625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "orig": "Figure 3: t-SNE plot of 5 words map... | null | |
00842dd0-5503-45db-93eb-e97de5e70515 | 2302.06555v2.pdf | picture | null | Figure 3: t-SNE plot of 5 words mapped from MAE$_{Huge}$ (blue) to OPT$_{30B}$ (orange) using Procrustes analysis. The green represent the mapped MAE$_{Huge}$ embeddings. | 432 | 255 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/pictures/9", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 5, "bbox": {"l": 308.20428466796875, "t": 778.9642944335938, "r": 524.2200927734375, "b": 651.4071655273438, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 170]}], "captions": [{"cref": "#/texts/65"}], "r... | null | |
a114e3d5-0d2c-4bc9-96a2-f3c4388fe4b6 | 2302.06555v2.pdf | text | pick up on shallow characteristics shared across the two spaces. One example is frequency: frequent words may refer to frequently depicted objects. Learning what is rare is learning about the world, but more is at stake in the debate around whether LMs understand. Or consider length: word length may correlate with the ... | null | 443 | 509 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/66", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 306.16961669921875, "t": 560.0623779296875, "r": 527.3591918945312, "b": 305.34613037109375, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 927]}], "orig": "pick up on shallow characteristics ... | null | |
e58f7f49-4941-4c85-a409-6e922bdc74fb | 2302.06555v2.pdf | text | Non-isomorphic alignment baseline. The former two baselines examine the possibility of aligning representations across two modalities based on chance or shallow signals. While informative, neither strictly demonstrates that a linear projection cannot effectively establish a connection between two non-isomorphic represe... | null | 443 | 428 | 72 | image/png | 6 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/67", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 5, "bbox": {"l": 306.20733642578125, "t": 289.99688720703125, "r": 527.4515380859375, "b": 76.0753173828125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 786]}], "orig": "Non-isomorphic alignment baseline. T... | null | |
46f07d41-0be5-4d86-9cf0-7991da5d9684 | 2302.06555v2.pdf | caption | Figure 4: LMs converge toward the geometry of visual models as they grow larger on Exclude-1K set. | null | 893 | 21 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/68", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 6, "bbox": {"l": 73.5337905883789, "t": 389.9848327636719, "r": 519.9508056640625, "b": 379.31463623046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 98]}], "orig": "Figure 4: LMs converge toward the g... | null | |
0ab8b3df-d493-4755-90ce-a662ff499418 | 2302.06555v2.pdf | picture | null | Figure 4: LMs converge toward the geometry of visual models as they grow larger on Exclude-1K set. | 853 | 689 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/pictures/10", "parent": {"cref": "#/body"}, "children": [], "label": "picture", "prov": [{"page_no": 6, "bbox": {"l": 77.1706314086914, "t": 752.3418579101562, "r": 503.3846435546875, "b": 408.00994873046875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 98]}], "captions": [{"cref": "#/texts/68"}], "re... | null | |
1a77051c-a165-494b-ba02-f228783e0409 | 2302.06555v2.pdf | text | computing the alignment. Table 2 presents a comparison of the three different baselines. All baselines have P@100 well below 1%. Our mappings between VMs and LMs score much higher (up to 64%), showing the strength of the correlation between the geometries induced by these models with respect to a conservative performan... | null | 441 | 185 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/69", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 71.34822082519531, "t": 354.8316955566406, "r": 292.0813903808594, "b": 262.27301025390625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 330]}], "orig": "computing the alignment. Table 2 pre... | null | |
84a88245-492a-4513-8107-e1c0ba51c59d | 2302.06555v2.pdf | section_header | 5 Results | null | 112 | 23 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/70", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 6, "bbox": {"l": 71.40553283691406, "t": 246.7689208984375, "r": 127.26032257080078, "b": 235.4713592529297, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 9]}], "orig": "5 Results", "text": "5 Resul... | null | |
8442b477-b8e0-451c-83fe-54f5c4bac58c | 2302.06555v2.pdf | text | Similarities between visual and textual representations and how they are recovered through Procrustes Analysis are visualized through t-SNE in Figure 3. Our main results for nine VMs and all LMs are presented in Figure 4. The best P@100 scores are around 64%, with baseline scores lower than 1% (Table 2). In general, ev... | null | 442 | 295 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/71", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 71.2156753540039, "t": 222.7291259765625, "r": 292.0754089355469, "b": 75.686279296875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 527]}], "orig": "Similarities between visual and textual ... | null | |
e4b5caf4-460f-4b2f-af09-49e178d47915 | 2302.06555v2.pdf | text | an artifact such as a vehicle may be denoted by many lexemes (car, automobile, SUV, etc.), each of which may have multiple inflections and derivations (car, cars, car's, etc.). Figure 5 shows examples where the top predictions seem 'as good' as the gold standard. We find that a region of 10 neighbours corresponds rough... | null | 442 | 536 | 72 | image/png | 7 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/72", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 6, "bbox": {"l": 306.32073974609375, "t": 354.6774597167969, "r": 527.4515380859375, "b": 86.65802001953125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 970]}], "orig": "an artifact such as a vehicle may be... | null | |
2d965b10-92ed-40d9-bbed-0865ffec6183 | 2302.06555v2.pdf | paragraph | Image Classes | null | 118 | 20 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/73", "parent": {"cref": "#/body"}, "children": [], "label": "paragraph", "prov": [{"page_no": 7, "bbox": {"l": 71.53941345214844, "t": 774.43701171875, "r": 130.51449584960938, "b": 764.5503540039062, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 13]}], "orig": "Image Classes", "text": "Image Cla... | null | |
50c5d387-e5f5-4af4-b690-5fd28eb9a8ae | 2302.06555v2.pdf | section_header | Nearest Neighbors (Top 100) | null | 233 | 20 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/74", "parent": {"cref": "#/body"}, "children": [], "label": "section_header", "prov": [{"page_no": 7, "bbox": {"l": 195.791259765625, "t": 774.43701171875, "r": 312.44464111328125, "b": 764.481201171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 27]}], "orig": "Nearest Neighbors (Top 100)", "... | null | |
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985b2396-eabc-4d66-9946-e641632253c2 | 2302.06555v2.pdf | text | palmyra, palmyra palm, palm, palais, palatines, royal palm , palazzi, palazzo, palisades, palatinate, regency, palatial, palas, palatinates, palms, palimony, caribe, palmier, paladins, banyan tree, bermudas, bruneian, palazzos, bahamian, palmers, malacca, madeira, ceiba tree, palmettos, palmtop, oil palm, pal, royal, r... | null | 639 | 95 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/75", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 195.43907165527344, "t": 760.7074584960938, "r": 514.689697265625, "b": 713.5289916992188, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 647]}], "orig": "palmyra, palmyra palm, palm, palais, ... | null | |
402ad2ae-7330-49bf-b722-cf3f2833f75b | 2302.06555v2.pdf | text | drinking fountain , water fountain , cesspools, water cooler, manhole cover, bird feeder, birdbath, water jug, drainage system, fountain, water tap, watering can, garbage disposal, cesspit, recycling bin, water tank, garbage can, water pipe, manhole, toilet bowl, water closet, cement mixer, trash bin, soda fountain, bu... | null | 636 | 95 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/76", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 195.55520629882812, "t": 702.2905883789062, "r": 513.342041015625, "b": 654.7896728515625, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 717]}], "orig": "drinking fountain , water fountain , ... | null | |
5635b7b6-31c2-46b3-bd21-153192905bcc | 2302.06555v2.pdf | text | clamp, wrench, screwdriver, socket wrench, carabiner , torque wrench, screwdrivers, fastener, elastic bandage, pliers, retractor, screw thread, carabiners, plunger, spanner, corer, screw, aspirator, clamps, adjustable spanner, applicator, center punch, latch, extractor, lever, adaptor, hose, gripper, compensator, pipe ... | null | 634 | 95 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/77", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 195.4573974609375, "t": 643.7977905273438, "r": 512.2069091796875, "b": 596.356689453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 665]}], "orig": "clamp, wrench, screwdriver, socket wre... | null | |
02fdcde5-29ec-4075-beb5-e487b6f1d1af | 2302.06555v2.pdf | text | community center, training school, school, youth hostel, service department, conference center, music school, day school, student union , academy, life office, hall, orphanage, school system, meeting, college, ministry, school principal, government building, house, council, clinic, business office, schoolmaster, worksh... | null | 643 | 94 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/78", "parent": {"cref": "#/body"}, "children": [], "label": "text", "prov": [{"page_no": 7, "bbox": {"l": 195.45948791503906, "t": 585.3809204101562, "r": 516.7018432617188, "b": 538.2024536132812, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 631]}], "orig": "community center, training school, s... | null | |
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80a9e68e-cf48-473c-9b83-3729fc5b71ac | 2302.06555v2.pdf | caption | Figure 5: Examples featuring the 100 nearest neighbors in the mapping of image classes into the language representation space (from MAE$_{Huge}$ to OPT$_{30B}$). The golden labels are highlighted in green. | null | 908 | 49 | 72 | image/png | 8 | application/pdf | 1.0.0 | [] | null | null | {"self_ref": "#/texts/79", "parent": {"cref": "#/body"}, "children": [], "label": "caption", "prov": [{"page_no": 7, "bbox": {"l": 71.26412200927734, "t": 521.2467041015625, "r": 525.5408325195312, "b": 497.1179504394531, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 205]}], "orig": "Figure 5: Examples featuring the 1... | null |
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