1 citations · 1 across the 18 of their papers we have counts for
21 papers
BARQ: Balanced Codebook Refinement for Low-Bit LLM Quantization
Chenhang Cui, Xu Xie, Linrui Xu +4
As large language models (LLMs) grow in parameter count, model storage and parameter memory traffic have become major bottlenecks to efficient deployment. Codebook-based weight qua…
PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment
Jiawei Feng, Jiancan Wu, Xingyu Zhu +3
Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences. However, its adaptation to multimodal se…
Robustifying Vision-Language Models via Test-Time Prompt Adaptation
Xingyu Zhu, Huanshen Wu, Shuo Wang +4
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing tes…
Temporal Evidence Routing with Structured Visual Evidence for TimeLogicQA
Yuyang Sun, Yongliang Wu, Xingyu Zhu +8
TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, boundary conditions, and overlap.…
Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge
Yuyang Sun, Yongliang Wu, Xingyu Zhu +8
VRR-QA evaluates whether video-language systems can infer spatial, temporal, viewpoint, depth, and visibility relations that are not always resolved by a single frame. We present a…
Dual-Route Top-K Retrieval with 1v1 VLM Reranking for the CoVR-R
Yuyang Sun, Yongliang Wu, Xingyu Zhu +8
We describe \emph{Dual-Route Top-K Retrieval with 1v1 VLM Reranking} for the CoVR-R challenge. The method treats composed video retrieval as two coupled problems: finding a suffici…
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
Principled Steering via Null-space Projection for Jailbreak Defense in Vision-Language Models
Xingyu Zhu, Beier Zhu, Shuo Wang +4
As vision-language models (VLMs) are increasingly deployed in open-world scenarios, they can be easily induced by visual jailbreak attacks to generate harmful content, posing serio…
Adapting Point Cloud Analysis via Multimodal Bayesian Distribution Learning
Xingyu Zhu, Liang Yi, Shuo Wang +4
Multimodal 3D vision-language models show strong generalization across diverse 3D tasks, but their performance still degrades notably under domain shifts. This has motivated recent…
Multimodal OCR: Parse Anything from Documents
Handong Zheng, Yumeng Li, Kaile Zhang +22
We present Multimodal OCR (MOCR), a document parsing paradigm that jointly parses text and graphics into unified textual representations. Unlike conventional OCR systems that focus…
Look Carefully: Adaptive Visual Reinforcements in Multimodal Large Language Models for Hallucination Mitigation
Xingyu Zhu, Kesen Zhao, Liang Yi +4
Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language reasoning, yet they remain vulnerable to hallucination, where generated content deviat…
GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
Xingyu Zhu, Beier Zhu, Junfeng Fang +4
Large vision-language models (LVLMs) have achieved remarkable progress in vision-language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side…
Thinking with Images as Continuous Actions: Numerical Visual Chain-of-Thought
Kesen Zhao, Beier Zhu, Junbao Zhou +3
Recent MLLMs increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regions via either textified coordin…
Hierarchical Semantic Alignment for Image Clustering
Xingyu Zhu, Beier Zhu, Yunfan Li +4
Image clustering is a classic problem in computer vision, which categorizes images into different groups. Recent studies utilize nouns as external semantic knowledge to improve clu…
Res-Bench: Benchmarking the Robustness of Multimodal Large Language Models to Dynamic Resolution Input
Chenxu Li, Zhicai Wang, Yuan Sheng +3
Multimodal Large Language Models (MLLMs) increasingly support dynamic image resolutions. However, current evaluation paradigms primarily assess semantic performance, overlooking th…
Enhancing CLIP Robustness via Cross-Modality Alignment
Xingyu Zhu, Beier Zhu, Shuo Wang +2
Vision-language models (VLMs) such as CLIP demonstrate strong generalization in zero-shot classification but remain highly vulnerable to adversarial perturbations. Existing methods…
Dynamic Multimodal Prototype Learning in Vision-Language Models
Xingyu Zhu, Shuo Wang, Beier Zhu +6
With the increasing attention to pre-trained vision-language models (VLMs), \eg, CLIP, substantial efforts have been devoted to many downstream tasks, especially in test-time adapt…
Accelerating Diffusion Transformer via Error-Optimized Cache
Junxiang Qiu, Shuo Wang, Jinda Lu +4
Diffusion Transformer (DiT) is a crucial method for content generation. However, it needs a lot of time to sample. Many studies have attempted to use caching to reduce the time con…
Enhancing Zero-Shot Vision Models by Label-Free Prompt Distribution Learning and Bias Correcting
Xingyu Zhu, Beier Zhu, Yi Tan +3
Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. While optimizing prompts on downstream labeled data…
Selective Vision-Language Subspace Projection for Few-shot CLIP
Xingyu Zhu, Beier Zhu, Yi Tan +3
Vision-language models such as CLIP are capable of mapping the different modality data into a unified feature space, enabling zero/few-shot inference by measuring the similarity of…
Boosting Few-Shot Learning via Attentive Feature Regularization
Xingyu Zhu, Shuo Wang, Jinda Lu +3
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from differen…