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https://arxiv.org/abs/2406.02383
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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
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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/2408.03326
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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/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-
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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
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https://arxiv.org/abs/2311.17061
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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/2507.06187
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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/2001.05027
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https://arxiv.org/abs/2309.03453
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https://arxiv.org/abs/2407.21121
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https://arxiv.org/abs/2405.12979
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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/1912.04421
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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
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[2305.02301] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data a
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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
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[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
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[2406.05184] The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Be
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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
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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/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
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