1-100 of about 1,614 matches for site:arxiv.org site:arxiv.org site:arxiv.org site:arxiv.org �adhere 
  https://arxiv.org/abs/2406.02383
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 https://arxiv.org/abs/2405.15217
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 https://arxiv.org/abs/2207.00026
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 https://arxiv.org/abs/2412.15689
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 https://arxiv.org/abs/2412.12463
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  [2412.15211] Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variat 
 https://arxiv.org/abs/2412.15211
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 https://arxiv.org/abs/2312.10540
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  [2409.17146] Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models 
 https://arxiv.org/abs/2409.17146
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 https://arxiv.org/abs/2412.13196
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 https://arxiv.org/abs/2112.03857
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 https://arxiv.org/abs/2408.03326
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 https://arxiv.org/abs/2207.10662
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 https://arxiv.org/abs/2104.02244
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 https://arxiv.org/abs/2406.09417
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 https://arxiv.org/abs/2404.13026
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 https://arxiv.org/abs/1906.07927
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  [2503.15586] How to Train Your Dragon: Automatic Diffusion-Based Rigging for Characters with Diverse 
 https://arxiv.org/abs/2503.15586
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 https://arxiv.org/abs/2503.14445
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 https://arxiv.org/abs/2408.00771
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 https://arxiv.org/abs/2503.04036
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 https://arxiv.org/abs/2505.05473
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 https://arxiv.org/abs/2303.04803
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 https://arxiv.org/abs/2112.09106
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 https://arxiv.org/abs/2410.13832
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  [1910.04760] A cost-effective method for improving and re-purposing large, pre-trained GANs by fine- 
 https://arxiv.org/abs/1910.04760
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 https://arxiv.org/abs/2001.07685
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 https://arxiv.org/abs/2303.15951
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 https://arxiv.org/abs/2407.01878
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  [2209.06293] Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New York 
 https://arxiv.org/abs/2209.06293
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 https://arxiv.org/abs/2311.17061
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 https://arxiv.org/abs/1907.09273
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 https://arxiv.org/abs/1910.07969
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 https://arxiv.org/abs/2402.00838
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 https://arxiv.org/abs/2408.15239
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 https://arxiv.org/abs/2502.15989
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 https://arxiv.org/abs/2507.06187
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 https://arxiv.org/abs/2506.07643
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 https://arxiv.org/abs/2403.02543
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 https://arxiv.org/abs/2407.00369
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 https://arxiv.org/abs/2311.02542
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 https://arxiv.org/abs/2401.09416
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 https://arxiv.org/abs/2502.15242
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 https://arxiv.org/abs/2504.08591
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  [2406.16008] Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utiliz 
 https://arxiv.org/abs/2406.16008
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  [2306.09224] Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories 
 https://arxiv.org/abs/2306.09224
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 https://arxiv.org/abs/2406.08332
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 https://arxiv.org/abs/2412.03937
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 https://arxiv.org/abs/1612.06321
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 https://arxiv.org/abs/2502.10377
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 https://arxiv.org/abs/2001.05027
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 https://arxiv.org/abs/2309.03453
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 https://arxiv.org/abs/2405.11881
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 https://arxiv.org/abs/2407.21121
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 https://arxiv.org/abs/2412.00905
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 https://arxiv.org/abs/2405.12979
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 https://arxiv.org/abs/2506.05282
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 https://arxiv.org/abs/2412.21079
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 https://arxiv.org/abs/2111.11432
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 https://arxiv.org/abs/2406.09408
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 https://arxiv.org/abs/2304.04848
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 https://arxiv.org/abs/2308.16891
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 https://arxiv.org/abs/2306.09012
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 https://arxiv.org/abs/1912.04421
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 https://arxiv.org/abs/2304.09479
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 https://arxiv.org/abs/2308.06954
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 https://arxiv.org/abs/1804.03281
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 https://arxiv.org/abs/2412.13185
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 https://arxiv.org/abs/2308.07926
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 https://arxiv.org/abs/2405.17414
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  [1403.2805] The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects 
 https://arxiv.org/abs/1403.2805
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 https://arxiv.org/abs/2404.19174
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 https://arxiv.org/abs/2404.12333
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  [2304.00583] Enhancing Deformable Local Features by Jointly Learning to Detect and Describe Keypoint 
 https://arxiv.org/abs/2304.00583
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  [2305.02301] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data a 
 https://arxiv.org/abs/2305.02301
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 https://arxiv.org/abs/2405.14871
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 https://arxiv.org/abs/2501.10021
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 https://arxiv.org/abs/1812.01584
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 https://arxiv.org/abs/2310.17994
 arXiv is committed to these values and only works with partners that adhere to them
  [2004.01804] Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition a 
 https://arxiv.org/abs/2004.01804
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 https://arxiv.org/abs/2309.08250
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 https://arxiv.org/abs/2104.05279
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 https://arxiv.org/abs/2404.05465
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 https://arxiv.org/abs/2302.06833
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 https://arxiv.org/abs/2503.21581
 arXiv is committed to these values and only works with partners that adhere to them
  [2406.05184] The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Be 
 https://arxiv.org/abs/2406.05184
 arXiv is committed to these values and only works with partners that adhere to them
 https://arxiv.org/abs/2305.16321
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 https://arxiv.org/abs/2012.05116
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 https://arxiv.org/abs/2111.15121
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  [2309.01858] Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Ima 
 https://arxiv.org/abs/2309.01858
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 https://arxiv.org/abs/2204.09222
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 https://arxiv.org/abs/2404.00126
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 https://arxiv.org/abs/2305.13409
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 https://arxiv.org/abs/2304.13915
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 https://arxiv.org/abs/2102.10458
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 https://arxiv.org/abs/2404.03813
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 https://arxiv.org/abs/2209.14530
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 https://arxiv.org/abs/1609.05507
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 https://arxiv.org/abs/2209.11302
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 https://arxiv.org/abs/2004.09095
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 https://arxiv.org/abs/2109.04683
 arXiv is committed to these values and only works with partners that adhere to them