37 citations · 37 across the 9 of their papers we have counts for
9 papers
STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
Yichen Guo, Tinghao Wang, Qizhe Zhang +15
Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substantial computational overhead,…
FADE: Mitigating Hallucinations by Reducing Language-Prior Dominance in Large Vision-Language Models
Yichen Guo, Kai Tang, Fenglai Lin +6
Despite the impressive capabilities of Large Vision-Language Models (LVLMs), they remain susceptible to hallucination, generating content inconsistent with the input image. Recent…
TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference
Tinghao Wang, Yichen Guo, Rui Huang +13
Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introdu…
Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
Yigeng Zhou, Wu Li, Yifan Lu +6
Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts w…
Beyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition
Hui Liu, Kecheng Chen, Jialiang Wang +3
Vision-Language Models (VLMs), such as CLIP, have significantly advanced zero-shot image recognition. However, their performance remains limited by suboptimal prompt engineering an…
Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance
Hui Liu, Wenya Wang, Kecheng Chen +6
In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories by composing known simpler concepts. However, existing vision-langu…
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
Hui Liu, Wenya Wang, Hao Sun +4
Large Language Models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities from few-shot demonstration exemplars. While recent learning-based demonstration se…
TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
Hui Liu, Wenya Wang, Haoru Li +1
The proliferation of fake news has emerged as a severe societal problem, raising significant interest from industry and academia. While existing deep-learning based methods have ma…
Robust Domain Misinformation Detection via Multi-modal Feature Alignment
Hui Liu, Wenya Wang, Hao Sun +2
Social media misinformation harms individuals and societies and is potentialized by fast-growing multi-modal content (i.e., texts and images), which accounts for higher "credibilit…