activity
20242026
most citedBias in Large Language Models: Origin, Evaluation, and Mitigation

24 citations · 24 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

Shuang Cui, Fan Ji, Guanglong Sun +4

Real-world image restoration (IR) remains challenging due to complex and coupled degradations. While recent agentic IR frameworks leverage Large Language Models for flexible tool p…

cs.IR2026

Text-Guided Visual Representation Learning for Robust Multimodal E-Commerce Recommendation

Yufei Guo, Jing Ma, Tianlu Zhang +5

Multimodal item embeddings are crucial for e-commerce item-to-item (I2I) retrieval, yet real-world product images often contain promotional overlays and background clutter that inj…

cs.CL202624 cited

Bias in Large Language Models: Origin, Evaluation, and Mitigation

Yufei Guo, Muzhe Guo, Juntao Su +5

Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines th…

cs.CV2026

Towards Artwork Explanation in Large-scale Vision Language Models

Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2

Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…

cs.CV2025

Synergistic Prompting for Robust Visual Recognition with Missing Modalities

Zhihui Zhang, Luanyuan Dai, Qika Lin +5

Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, i…

cs.CV2025

Toward Realistic Camouflaged Object Detection: Benchmarks and Method

Zhimeng Xin, Tianxu Wu, Shiming Chen +5

Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours…