Recent advancements in Vision-Language Models (VLMs) have significantly pushed the boundaries of Visual Question Answering (VQA).However,high-resolution details can sometimes become noise that leads to hallucinations or reasoning errors. In this paper,we propose Degradation-Driven Prompting (DDP), a novel framework that improves VQA performance by strategically reducing image fidelity to force models to focus on essential structural information. We evaluate DDP across two distinct tasks. Physical attributes targets images prone to human misjudgment, where DDP employs a combination of 80p downsampling, structural visual aids (white background masks and orthometric lines), and In-Context Learning (ICL) to calibrate the model's focus. Perceptual phenomena addresses various machine-susceptible visual anomalies and illusions, including Visual Anomaly (VA), Color (CI), Motion(MI),Gestalt (GI), Geometric (GSI), and Visual Illusions (VI).For this task, DDP integrates a task-classification stage with specialized tools such as blur masks and contrast enhancement alongside downsampling. Our experimental results demonstrate that less is more: by intentionally degrading visual inputs and providing targeted structural prompts, DDP enables VLMs to bypass distracting textures and achieve superior reasoning accuracy on challenging visual benchmarks.
Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360{\textdegree} video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception t…
Arizona State University（亚利桑那州立大学）；Carnegie Mellon University（卡内基梅隆大学）
Visual Question Answering (VQA) is a complex semantic task requiring both natural language processing and visual recognition. In this paper, we explore whether VQA is solvable when images are captured in a sub-Nyquist compressive paradigm. We develop a series of deep-network architectures that exploit available compressive data to increasing degrees of accuracy, and show that VQA is indeed solvable in the compressed domain. Our results show that there is nominal degradation in VQA performance when using compressive measurements, but that accuracy can be recovered when VQA pipelines are used in…
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering
Key Laboratory of Complex Systems Modeling and Simulation, the School of Computer Science, Hangzhou Dianzi University（复杂系统建模与仿真重点实验室、计算机科学学院、杭州电子大学）；HDU-ITMO Joint Institute, Hangzhou Dianzi University（杭州电子大学-ITMO联合学院）；School of Computer Science and Information Engineering, Hefei University of Technology（计算机科学与信息工程学院、合肥工业大学）；School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen（智能科学与工程学院、哈尔滨工业大学深圳校区）
Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigation
Johns Hopkins University, School of Medicine（约翰霍普金斯大学医学院）；Massachusetts Institute of Technology（麻省理工学院）；Microsoft Healthcare & Life Sciences（微软医疗健康与生命科学）
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion
School of Artificial Intelligence, Tiangong University（天津工业大学人工智能学院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Linying Medical Technology (Shenzhen) Co., Ltd.（临影医疗科技（深圳）有限公司）
Arizona State University（亚利桑那州立大学）；Emory University（埃默里大学）
Multiple-choice VQA has drawn increasing attention from researchers and end-users recently. As the demand for automatically constructing large-scale multiple-choice VQA data grows, we introduce a novel task called textual Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet meaningful distractors given the context image, question, and correct answer. The DG-VQA task aims at generating distractors without ground-truth training samples since such resources are rarely available. To tackle the DG-VQA unsupervisedly, we propose Gobbet, a reinforcement learning(RL) based fr…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google LLC（谷歌有限责任公司）
Recent years have witnessed an explosion of user-generated content (UGC) videos shared and streamed over the Internet, thanks to the evolution of affordable and reliable consumer capture devices, and the tremendous popularity of social media platforms. Accordingly, there is a great need for accurate video quality assessment (VQA) models for UGC/consumer videos to monitor, control, and optimize this vast content. Blind quality prediction of in-the-wild videos is quite challenging, since the quality degradations of UGC content are unpredictable, complicated, and often commingled. Here we contrib…
Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, either artificial or authentically distorted. We introduce a new in-the-wild VQA dataset that is substantially larger and diverse: KonVid-150k. It consists of a coarsely annotated set of 153,841 videos having five quality ratings each, and 1,596 videos with a minimum of 89 ratings …
R^3-VQA: "Read the Room" by Video Social Reasoning
School of Intelligence Science and Technology, Peking University（北京理工大学智能科学与技术学院）；College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence（一般人工智能国家重点实验室）；Yuanpei College, Peking University（北京大学元培学院）；Tsinghua University（清华大学）；University of California, Los Angeles（加州大学洛杉矶分校）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）
Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allo…
SpaceDG: Benchmarking Spatial Intelligence under Visual Degradation
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Electronic Science and Technology of China（电子科技大学）；Chongqing University（重庆大学）；The University of Tokyo（东京大学）；Beihang University（北航）；Northwestern Polytechnical University（西北工业大学）
End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting
Beijing Institute of Technology（北京理工大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；University of Rochester（罗切斯特大学）
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
Beijing Normal University, China（北京师范大学）；University of Exeter, United Kingdom（埃克塞特大学）；University College London, United Kingdom（伦敦大学学院）；Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates（穆罕默德·本·扎耶德人工智能大学）；University of Surrey, United Kingdom（萨里大学）
ReCALL: Recalibrating Capability Degradation for MLLM-based Composed Image Retrieval
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Southeast University（东南大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；National University of Singapore（新加坡国立大学）；Wuhan AI Research（武汉人工智能研究院）；Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangdong Polytechnic Normal University（广东技术师范大学广东省知识产权大数据重点实验室）
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Inc.（谷歌公司）
Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Distortion type identification and degradation level determination is employed as an auxiliary task to train a deep learning model containing a deep Convolutional Neural Network (CNN) that extracts spatial features, as well as a recurrent unit that captures temporal information. The model is trained using a contrastive loss and we therefore refer to this trainin…
DocVAL: Validated Chain-of-Thought Distillation for Grounded Document VQA
Department of Computer Science（计算机科学系）；Engineering, Ohio State University, Ohio, US（工程系，俄亥俄州立大学，俄亥俄，美国）；Department of Computer Science, University of Central Florida, Florida, US（计算机科学系，中央佛罗里达大学，佛罗里达，美国）
