1 citations · 2 across the 9 of their papers we have counts for
10 papers
Forbid Your Attention: Fooling Multimodal Large Language Models by Selectively Removing Intrinsic Focus in Spectral Domain
Daizong Liu, Junhao Dong, Zhiyuan Ma +6
Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information, showing remarkable progress…
Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective
Xiang Fang, Zeyu Xiong, Wanlong Fang +7
This paper addresses the challenging task of weakly-supervised video temporal grounding. Existing approaches are generally based on the moment proposal selection framework that uti…
TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios
Qiucheng Yu, Ruijie Xu, Mingang Chen +2
Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment. However, existing benchmarks suffer from three fundamental…
Towards Efficient General Feature Prediction in Masked Skeleton Modeling
Shengkai Sun, Zefan Zhang, Jianfeng Dong +3
Recent advances in the masked autoencoder (MAE) paradigm have significantly propelled self-supervised skeleton-based action recognition. However, most existing approaches limit rec…
UW-3DGS: Underwater 3D Reconstruction with Physics-Aware Gaussian Splatting
Wenpeng Xing, Jie Chen, Zaifeng Yang +5
Underwater 3D scene reconstruction faces severe challenges from light absorption, scattering, and turbidity, which degrade geometry and color fidelity in traditional methods like N…
Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval
Junan Lin, Daizong Liu, Xianke Chen +5
Video Moment Retrieval (VMR) aims to retrieve a specific moment semantically related to the given query. To tackle this task, most existing VMR methods solely focus on the visual a…
Multimodal Language Models See Better When They Look Shallower
Haoran Chen, Junyan Lin, Xinghao Chen +6
Multimodal large language models (MLLMs) typically extract visual features from the final layers of a pretrained Vision Transformer (ViT). This widespread deep-layer bias, however,…
Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model
Chen Rao, Guangyuan Li, Zehua Lan +7
Current video deblurring methods have limitations in recovering high-frequency information since the regression losses are conservative with high-frequency details. Since Diffusion…
Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
Xiaoye Qu, Qiyuan Chen, Wei Wei +2
Despite the remarkable ability of large vision-language models (LVLMs) in image comprehension, these models frequently generate plausible yet factually incorrect responses, a pheno…
Mitigating Multilingual Hallucination in Large Vision-Language Models
Xiaoye Qu, Mingyang Song, Wei Wei +2
While Large Vision-Language Models (LVLMs) have exhibited remarkable capabilities across a wide range of tasks, they suffer from hallucination problems, where models generate plaus…