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This paper presents CylinderDepth, a self-supervised surround depth estimation method leveraging cylindrical spatial attention for multi-view consistency across camera rigs.

Existing online video segmentation models typically combine a per-frame segmentation module with complex, specialized tracking modules. This work shows that a plain Vision Transformer encoder with a lightweight temporal module can match that performance, resulting in VidEoMT — up to 5–10x faster, running at up to 160 FPS with a ViT-L encoder.

This CVPR 2026 work links structured world knowledge representations out of Video LLMs for next-generation video recommendation, covering how large vision-language models can provide rich semantic priors for video understanding while addressing efficiency and deployment challenges in real recommendation systems.