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20152026
most citedWhen Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges

7 citations · 11 across the 18 of their papers we have counts for

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16 papers · 1 filter

cs.CV2026

0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation

Xiaoqiang Lu, Licheng Jiao, Lingling Li +5

Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs to referring image segmentation (…

cs.CV2026

DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

Xiaoqiang Lu, Licheng Jiao, Long Sun +5

Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal…

cs.CV2026

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

Zehua Hao, Fang Liu, Qinliang Wang +3

Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fail in complementary ways.When…

cs.CV2026

SoccerNet 2026 Challenges Results

Anthony Cioppa, Silvio Giancola, Håkan Ardö +102

The SoccerNet 2026 Challenges constitute the sixth annual edition of the SoccerNet open benchmarking effort, dedicated to advancing computer vision research in sports video underst…

cs.CV2025

Edit-Your-Interest: Efficient Video Editing via Feature Most-Similar Propagation

Yi Zuo, Zitao Wang, Lingling Li +3

Text-to-image (T2I) diffusion models have recently demonstrated significant progress in video editing. However, existing video editing methods are severely limited by their high co…

cs.CV2025

Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds

Pei He, Lingling Li, Licheng Jiao +5

Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distri…