Existing visual question reasoning methods usually fail to explicitly discover the inherent causal mechanism and ignore jointly modeling cross-modal event temporality and causality. In this paper, we propose a visual question reasoning framework named Cross-Modal Question Reasoning (CMQR), to discover temporal causal structure and mitigate visual spurious correlation by causal intervention. To explicitly discover visual causal structure, the Visual Causality Discovery (VCD) architecture is proposed to find question-critical scene temporally and disentangle the visual spurious correlations by attention-based front-door causal intervention module named Local-Global Causal Attention Module (LGCAM). To align the fine-grained interactions between linguistic semantics and spatial-temporal representations, we build an Interactive Visual-Linguistic Transformer (IVLT) that builds the multi-modal co-occurrence interactions between visual and linguistic content. Extensive experiments on four datasets demonstrate the superiority of CMQR for discovering visual causal structures and achieving robust question reasoning.
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）
CausalDisenSeg: A Causality-Guided Disentanglement Framework with Counterfactual Reasoning for Robust Brain Tumor Segmentation Under Missing Modalities
School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Mathematics and Computer Sciences, Nanchang University（南昌大学数学与计算机科学学院）
Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）
East China Normal University（华东师范大学）；Xiamen University（厦门大学）；Shanghai Jiao Tong University（上海交通大学）
The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimatin…
Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation
School of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Tongji Artificial Intelligence (Suzhou) Research Institute（同济大学苏州人工智能研究院）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）；Rutgers University（罗格斯大学）
Causality knowledge is vital to building robust AI systems. Deep learning models often perform poorly on tasks that require causal reasoning, which is often derived using some form of commonsense knowledge not immediately available in the input but implicitly inferred by humans. Prior work has unraveled spurious observational biases that models fall prey to in the absence of causality. While language representation models preserve contextual knowledge within learned embeddings, they do not factor in causal relationships during training. By blending causal relationships with the input features …
Seoul National University（首尔国立大学）；AI Institute for Seoul National University (AIIS)（首尔国立大学人工智能研究院）；Hanyang University（汉阳大学）
Video Question Answering is a task which requires an AI agent to answer questions grounded in video. This task entails three key challenges: (1) understand the intention of various questions, (2) capturing various elements of the input video (e.g., object, action, causality), and (3) cross-modal grounding between language and vision information. We propose Motion-Appearance Synergistic Networks (MASN), which embed two cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question's intentions. MASN consists of a motion module, an appea…
MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
Department of Computer Science, Chongqing University, China（重庆大学计算机科学系）；National Elite Institute of Engineering, Chongqing University, China（重庆大学工程精英研究院）；College of Computer Science and Technology, National University of Deffense Technology, China（国防科技大学计算机科学与技术学院）
Stanford University（斯坦福大学）；University of Waterloo（滑铁卢大学）；Harvard University（哈佛大学）
Most prior art in visual understanding relies solely on analyzing the "what" (e.g., event recognition) and "where" (e.g., event localization), which in some cases, fails to describe correct contextual relationships between events or leads to incorrect underlying visual attention. Part of what defines us as human and fundamentally different from machines is our instinct to seek causality behind any association, say an event Y that happened as a direct result of event X. To this end, we propose iPerceive, a framework capable of understanding the "why" between events in a video by building a comm…
Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
Shanghai AI Laboratory（上海人工智能实验室）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
