1 citations · 1 across the 8 of their papers we have counts for
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SAGE: Spuriousness-Aware Guided Prompt Exploration for Mitigating Multimodal Bias
Wenqian Ye, Di Wang, Guangtao Zheng +2
Large vision-language models, such as CLIP, have shown strong zero-shot classification performance by aligning images and text in a shared embedding space. However, CLIP models oft…
Collaborative Text-to-Image Generation via Multi-Agent Reinforcement Learning and Semantic Fusion
Jiabao Shi, Minfeng Qi, Lefeng Zhang +5
Multimodal text-to-image generation remains constrained by the difficulty of maintaining semantic alignment and professional-level detail across diverse visual domains. We propose…
ARC-Hunyuan-Video-7B: Structured Video Comprehension of Real-World Shorts
Yuying Ge, Yixiao Ge, Chen Li +15
Real-world user-generated short videos, especially those distributed on platforms such as WeChat Channel and TikTok, dominate the mobile internet. However, current large multimodal…
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Dingzriui Wang, Xuanliang Zhang, Keyan Xu +3
In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across mo…
What Should Feature Distillation Transfer in LLMs? A Task-Tangent Geometry View
Khouloud Saadi, Di Wang
Feature-based knowledge distillation aims to transfer intermediate representations from a teacher LLM model to a student. Existing approaches typically rely on direct feature match…