Recently, finetuning pretrained Vision-Language Models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in Visual Question Answering (VQA). However, as VLMs scale, finetuning full model parameters for a given task in low-resource settings becomes computationally expensive, storage inefficient, and prone to overfitting. Current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, but there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, MixPHM reparameterizes expert weights in a low-rank subspace and shares part of the weights inside and across experts. Moreover, based on a quantitative redundancy analysis for adapters, we propose Redundancy Regularization to reduce task-irrelevant redundancy while promoting task-relevant correlation in MixPHM representations. Experiments conducted on VQA v2, GQA, and OK-VQA demonstrate that MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning.
A Knowledge Noise Mitigation Framework for Knowledge-based Visual Question Answering
School of Computer, Electronics and Information（计算机、电子与信息学院）；Guangxi University（广西大学）；Guangxi Key Laboratory of Multimedia Communications and Network Technology（广西多媒体通信与网络技术重点实验室）
AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering
Great Bay University（大湾区大学）；Dongguan Key Laboratory for Intelligence and Information Technology（东莞智能与信息科技重点实验室）；Nankai University（南开大学）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；The Chinese University of Hong Kong（香港中文大学）
Redundancy-aware Transformer for Video Question Answering
National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；Institute of Dataspace, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）
Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decoding
Department of computer science, University of Central Florida, Orlando, USA（计算机科学系，中央佛罗里达大学）；Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA（生物工程系，宾夕法尼亚大学）；Department of electrical engineering, Columbia university, New York, NY, USA（电气工程系，哥伦比亚大学）；Technical University of Applied Sciences Regensburg, Regensburg, Germany（应用科学技术大学（雷根斯堡））；Department of Surgery, University of Calgary, Calgary, Alberta, Canada（外科系，卡尔加里大学）；University College of Nabi Akram, Tabriz, Iran（纳比阿克兰大学）；School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran（电气工程学院，伊朗科学技术大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Baichuan Inc.（百川智能）；Renmin University of China（中国人民大学）
SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Hupan Lab（湖畔实验室）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound
ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC学院）；University of Washington（华盛顿大学）；Zhejiang University School of Medicine Sir Run Run Shaw Hospital（浙江大学医学院附属邵逸夫医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Southern California（南加州大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；University of Freiburg（弗莱堡大学）
