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20242026
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cs.CV2026

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval

Guosheng Zhang, Linkai Liu, Keyao Wang +3

Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus on sample-level objectives…

cs.CV2026

KD-CVG: A Knowledge-Driven Approach for Creative Video Generation

Linkai Liu, Wei Feng, Xi Zhao +9

Creative Generation (CG) leverages generative models to automatically produce advertising content that highlights product features, and it has been a significant focus of recent re…

cs.CV2025

RePainter: Empowering E-commerce Object Removal via Spatial-matting Reinforcement Learning

Zipeng Guo, Lichen Ma, Xiaolong Fu +13

In web data, product images are central to boosting user engagement and advertising efficacy on e-commerce platforms, yet the intrusive elements such as watermarks and promotional…

cs.CV2025

Where, What, Why: Towards Explainable Driver Attention Prediction

Yuchen Zhou, Jiayu Tang, Xiaoyan Xiao +6

Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by ge…

cs.CV2024

Learning from Observer Gaze:Zero-Shot Attention Prediction Oriented by Human-Object Interaction Recognition

Yuchen Zhou, Linkai Liu, Chao Gou

Most existing attention prediction research focuses on salient instances like humans and objects. However, the more complex interaction-oriented attention, arising from the compreh…