Weakly Aligned Feature Fusion for Multimodal Object Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Lu Zhang, Zhiyong Liu, Xiangyu Zhu, Zhan Song, Xu Yang, Zhen Lei, Hong Qiao
To achieve accurate and robust object detection in the real-world scenario, various forms of images are incorporated, such as color, thermal, and depth. However, multimodal data often suffer from the position shift problem, i.e., the image pair is not strictly aligned, making one object has different positions in different modalities. For the deep learning method, this problem makes it difficult to fuse multimodal features and puzzles the convolutional neural network (CNN) training. In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem. First, a region feature (RF) alignment module with adjacent similarity constraint is designed to consistently predict the position shift between two modalities and adaptively align the cross-modal RFs. Second, we propose a novel region of interest (RoI) jitter strategy to improve the robustness to unexpected shift patterns. Third, we present a new multimodal feature fusion method that selects the more reliable feature and suppresses the less useful one via feature reweighting. In addition, by locating bounding boxes in both modalities and building their relationships, we provide novel multimodal labeling named KAIST-Paired. Extensive experiments on 2-D and 3-D object detection, RGB-T, and RGB-D datasets demonstrate the effectiveness and robustness of our method.
UiT The Arctic University of Norway（北极挪威特罗姆瑟大学）；NORCE Norwegian Research Center（NORCE挪威研究中心）；University of Genoa（热那亚大学）
Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces…
Cross-modal Offset-guided Dynamic Alignment and Fusion for Weakly Aligned UAV Object Detection
State Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences（光学场操控科学与技术国家重点实验室，中国科学院）；Institute of Optics and Electronics, Chinese Academy of Sciences（中国科学院光电研究所）
Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery
Beijing University of Technology（北京工业大学）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beijing Institute of Technology（北京理工大学）；Fuzhou University（福州大学）；China University of Geosciences Wuhan（中国地质大学（武汉））；Ghent University（根特大学）
SSLFusion: Scale & Space Aligned Latent Fusion Model for Multimodal 3D Object Detection
School of Big Data and Software Engineering, Chongqing University（重庆大学大数据与软件学院）；School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）；School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Beijing Institute of Technology（北京理工大学）；Shanghai AI Laboratory（上海人工智能实验室）；Inceptio（嬴彻科技）；University of Macau（澳门大学）
Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations for training. In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i.e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models. Our LWSIS not only exploits the complementary information in multimodal data …
University of Toronto（多伦多大学）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of Maryland - Baltimore County（马里兰大学巴尔的摩县分校）
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that i…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Microsoft Research Asia（微软亚洲研究院）；Kuaishou Technology（快手科技）
Multimodal and multi-domain stylization are two important problems in the field of image style transfer. Currently, there are few methods that can perform both multimodal and multi-domain stylization simultaneously. In this paper, we propose a unified framework for multimodal and multi-domain style transfer with the support of both exemplar-based reference and randomly sampled guidance. The key component of our method is a novel style distribution alignment module that eliminates the explicit distribution gaps between various style domains and reduces the risk of mode collapse. The multimodal …
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Keio University（庆应义塾大学）；National Institute of Informatics（国立信息学研究所）；National Institute of Informatics Research and Development Center for Large Language Models（国立信息学研究所大型语言模型研发中心）
UrbanWorld2.0: A Multimodal Agentic Framework for Reality-Aligned 3D World Generation at City-Scale
College of AI, Tsinghua University（人工智能学院，清华大学）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；Department of Electronic Engineering, BNRist, Tsinghua University（电子工程系，北京研究院，清华大学）
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection Method
School of Software Engineering（软件工程学院）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Xi’an Jiaotong University（西安交通大学）；Chinese Academy of Sciences（中国科学院）
A Multimodal Deviation Perceiving Framework for Weakly-Supervised Temporal Forgery Localization
School of Computer Science and Engineering, Sun Yat-sen University（计算机科学与工程学院，中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；School of Cyber Science and Engineering, Wuhan University（网络科学与工程学院，武汉大学）
Weakly Supervised Multimodal Temporal Forgery Localization via Multitask Learning
School of Computer Science and Engineering, MoE Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-sen University（计算机科学与工程学院、信息科技教育部重点实验室、广东省信息安全技术重点实验室、中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究所）
For multimodal tasks, a good feature extraction network should extract information as much as possible and ensure that the extracted feature embedding and other modal feature embedding have an excellent mutual understanding. The latter is often more critical in feature fusion than the former. Therefore, selecting the optimal feature extraction network collocation is a very important subproblem in multimodal tasks. Most of the existing studies ignore this problem or adopt an ergodic approach. This problem is modeled as an optimization problem in this paper. A novel method is proposed to convert…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Renmin University of China（中国人民大学）；CEBSIT, Chinese Academy of Sciences（中国科学院CEBSIT）
Multispectral pedestrian detection has shown great advantages under poor illumination conditions, since the thermal modality provides complementary information for the color image. However, real multispectral data suffers from the position shift problem, i.e. the color-thermal image pairs are not strictly aligned, making one object has different positions in different modalities. In deep learning based methods, this problem makes it difficult to fuse the feature maps from both modalities and puzzles the CNN training. In this paper, we propose a novel Aligned Region CNN (AR-CNN) to handle the w…
CIEC: Coupling Implicit and Explicit Cues for Multimodal Weakly Supervised Manipulation Localization
School of Computer Science and Engineering（计算机科学与工程学院）；MoE Key Laboratory of Information Technology（信息技术关键实验室）；Key Laboratory of Information Security Technology（信息安全技术重点实验室）；Sun Yat-sen University（中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）；Alibaba Group（阿里巴巴集团）
Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing
Zhengzhou University（郑州大学）；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）；Peng Cheng Laboratory（鹏城实验室）
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy
College of Medicine and Biological Information Engineering（医学与生物信息工程学院）；Cancer Hospital of Dalian University of Technology（大连理工大学肿瘤医院）；Cancer Hospital of China Medical University（中国医科大学附属肿瘤医院）
Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models
Salim Habib University（萨利姆·哈比卜大学）；Institute of Business Administration Sukkur（苏库尔工商管理学院）；University of Central Florida（中佛罗里达大学）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Toronto Metropolitan University（多伦多都会大学）；Vector Institute（向量研究所）
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；DarkMatter AI Research（DarkMatter AI研究）；University of Oxford（牛津大学）；HKU（香港大学）；Cainiao Group（菜鸟集团）；NCUT（北方工业大学）；Sun Yat-sen University（中山大学）；Westlake University（西湖大学）
Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is challenging as ground truth for uncertainty estimates is not available. Ideally, in a well-calibrated model, uncertainty estimates should perfectly correlate with model error. We propose a novel error aligned uncertainty optimization method and introduce a trainable loss function to guide the models to yield good quality uncertainty estimates aligning with the model e…
Shanghai Jiao Tong University（上海交通大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Berkeley（加州大学伯克利分校）
Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance …
Enhancing Weakly Supervised Multimodal Video Anomaly Detection through Text Guidance
School of Computer Science and Technology, Hangzhou Dianzi University（杭州电子科技大学计算机科学与技术学院）；Alibaba Cloud（阿里云）；College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）
Human-Aligned Bench: Fine-Grained Assessment of Reasoning Ability in MLLMs vs. Humans
Wuhan University（武汉大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Innovation Institute（上海创新研究院）；Harbin Institute of Technology（哈尔滨工业大学）
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心）；Department of Electronic and Computer Engineering（电子与计算机工程系）；The Hong Kong University of Science and Technology（香港科技大学）
Multimodal affect recognition constitutes an important aspect for enhancing interpersonal relationships in human-computer interaction. However, relevant data is hard to come by and notably costly to annotate, which poses a challenging barrier to build robust multimodal affect recognition systems. Models trained on these relatively small datasets tend to overfit and the improvement gained by using complex state-of-the-art models is marginal compared to simple baselines. Meanwhile, there are many different multimodal affect recognition datasets, though each may be small. In this paper, we propos…
Memes are used for spreading ideas through social networks. Although most memes are created for humor, some memes become hateful under the combination of pictures and text. Automatically detecting the hateful memes can help reduce their harmful social impact. Unlike the conventional multimodal tasks, where the visual and textual information is semantically aligned, the challenge of hateful memes detection lies in its unique multimodal information. The image and text in memes are weakly aligned or even irrelevant, which requires the model to understand the content and perform reasoning over mul…
