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20242026
most citedFine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval

29 citations · 30 across the 12 of their papers we have counts for

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cs.CV2026

From Where to How: Continuous 4D Interaction Forecasting from Egocentric Video

Qiaohui Chu, Haoyu Zhang, Meng Liu +3

Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize them, providing an important ca…

cs.CV2026

Diverse-Intent Multi-Turn Fashion Image Retrieval

Mingqiang Tang, Haokun Wen, Meng Liu +3

Real-world fashion search involves interactive retrieval across multiple turns. However, existing multi-turn retrieval methods are built on a restrictive assumption that every inte…

cs.CV2026

SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion

Haoyu Zhang, Meng Liu, Qianlong Xiang +3

Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benchmarks often emphasize image-l…

cs.CV2026

JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026

Qiaohui Chu, Haoyu Zhang, Yisen Feng +4

We propose JFAA, a JEPA-based Future Action Anticipation method for the EPIC-KITCHENS-100 (EK-100) Action Anticipation task. Inspired by the representation learning and future pred…

cs.CV2026

VISTA: Technical Report for the Ego4D Short-Term Object Interaction Anticipation at EgoVis 2026

Qiaohui Chu, Haoyu Zhang, Yisen Feng +4

We propose VISTA, a V-JEPA Integrated StillFast Temporal Anticipator for the Ego4D Short-Term Object Interaction Anticipation (STA) Challenge at EgoVis 2026. Given an egocentric vi…

cs.CV2026

OSGNet with MLLM Reranking @ Ego4D Episodic Memory Challenge 2026

Yisen Feng, Leigang Qu, Haoyu Zhang +5

In this report, we present our champion solutions for the Natural Language Queries and GoalStep tracks of the Ego4D Episodic Memory Challenge at CVPR 2026. Both tracks require accu…