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20242026
most citedHypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding

1 citations · 1 across the 7 of their papers we have counts for

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cs.CV2026

Dive Into the Implicit Biases of Low-rank Vision-language Alignment

Mingjia Shi, Shuo Wang, Xiaobo Wang +7

Vision-language alignment, the stage that bridges pretrained vision encoders and large language models, is widely treated as a form of pretraining requiring full-parameter updates.…

cs.CV2026

WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation

Wei Dong, Tianyu Fu, Zhe Yu +9

As web agents increasingly demonstrate capabilities in automated task execution, the development of robust evaluation frameworks for assessing their navigation and task completion…

cs.CV2024

Salvaging the Overlooked: Leveraging Class-Aware Contrastive Learning for Multi-Class Anomaly Detection

Lei Fan, Junjie Huang, Donglin Di +4

For anomaly detection (AD), early approaches often train separate models for individual classes, yielding high performance but posing challenges in scalability and resource managem…

cs.CV20241 cited

Hypergraph Multi-modal Large Language Model: Exploiting EEG and Eye-tracking Modalities to Evaluate Heterogeneous Responses for Video Understanding

Minghui Wu, Chenxu Zhao, Anyang Su +8

Understanding of video creativity and content often varies among individuals, with differences in focal points and cognitive levels across different ages, experiences, and genders.…

cs.CV2024

ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection

Yun Liang, Zhiguang Hu, Junjie Huang +3

Current unsupervised anomaly detection approaches perform well on public datasets but struggle with specific anomaly types due to the domain gap between pre-trained feature extract…