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20192026
most citedA Bayesian Approach to OOD Robustness in Image Classification

6 citations · 16 across the 39 of their papers we have counts for

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36 papers · 1 filter

cs.CV2026

Technical Report on the CVPR 2026@AdvML Workshop Challenge

Tianyuan Zhang, Zonglei Jing, Jiangfan Liu +47

Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning. This report presents the CVPR 2026@AdvML Workshop Cha…

cs.CV2026

RATS! Patches Talk Through Registers: Emergent Parts in Register Attention Transformers

Timing Yang, Predrag Neskovic, Jansen Seheult +4

When humans see a bird, they recognize far more than just "bird" -- they see a head, wings, and talons, a structured assembly of reusable parts that can be identified across every…

cs.CV2026

SEMAGIC: Learning Semantically Consistent Deformable 3D Representations from In-the-Wild Images

Sky Cen, Wufei Ma, Guofeng Zhang +2

Learning deformable 3D object models from single-view in-the-wild images has enabled impressive 3D shape reconstruction without supervision. However, it remains unclear whether the…

cs.CV2026

Can These Views Be One Scene? Evaluating Multiview 3D Consistency when 3D Foundation Models Hallucinate

Soumava Paul, Prakhar Kaushik, Alan Yuille

Multiview 3D evaluation assumes that the images being scored are observations of one static 3D scene. This assumption can fail in NVS and sparse-view reconstruction: inputs or gene…

cs.CV2026

HECTOR: Hybrid Editable Compositional Object References for Video Generation

Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang +4

Real-world videos naturally portray complex interactions among distinct physical objects, effectively forming dynamic compositions of visual elements. However, most current video g…

cs.CV2026

Thinking with Spatial Code for Physical-World Video Reasoning

Jieneng Chen, Wenxin Ma, Ruisheng Yuan +3

We introduce Thinking with Spatial Code, a framework that transforms RGB video into explicit, temporally coherent 3D representations for physical-world visual question answering. W…