In this work, we address the issues of missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performances of our method and a diverse possibility for future research.
We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged inform…
CCSD: Cross-Modal Compositional Self-Distillation for Robust Brain Tumor Segmentation with Missing Modalities
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（上海智能自主系统研究院，同济大学）；College of Biomedical Engineering, Fudan University（生物医学工程学院，复旦大学）；School of Medicine, Tongji University（医学院，同济大学）；College of Information Engineering, China Jiliang University（信息工程学院，中国嘉兴大学）
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Academy for Engineering and Technology, Fudan University（复旦大学工程技术学院）；Jilin Provincial Key Laboratory of Intelligence Science and Engineering（吉林省智能科学与工程重点实验室）；Engineering Research Center of AI and Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）
In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as a supervisory signal for training representations for a new unlabeled paired modality. Our method enables learning of rich representations for unlabeled modalities and can be used as a pre-training procedure for new modalities with limited labeled data. We show experimental results where we transfer supervision from labeled RGB images to unlabeled depth and optical flow images and demonstrate large improvements for both these c…
Omnimodal Dataset Distillation via High-order Proxy Alignment
University of Science and Technology of China（中国科学技术大学）；Xidian University（西安电子科技大学）；National University of Singapore（新加坡国立大学）；The University of Sydney（悉尼大学）
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities
University of Science and Technology of China（中国科学技术大学）；Tianjin University（天津大学）；Beijing Tiantan Hospital, Capital Medical University（首都医科大学附属北京天坛医院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）
Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation
School of Computer Science and Technology, China University of Mining and Technology（计算机科学与技术学院，中国矿业大学）；Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology（教育部矿山数字化工程研究中心，中国矿业大学）
University of Rochester（罗切斯特大学）；JD AI Research（京东人工智能研究院）
In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation. Unlike previous anticipation tasks that aim at action label prediction, our work targets at generating natural language outputs that provide interpretable and accurate descriptions of future action steps. It is a challenging task due to the lack of semantic information extracted from the instructional videos. To overcome this challenge, we propose a novel knowledge distillation framework to exploit the related external textual knowledge …
Shanghai Jiao Tong University（上海交通大学）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de Paris（巴黎理工学院）
The joint use of multiple imaging modalities for medical image segmentation has been widely studied in recent years. The fusion of information from different modalities has demonstrated to improve the segmentation accuracy, with respect to mono-modal segmentations, in several applications. However, acquiring multiple modalities is usually not possible in a clinical setting due to a limited number of physicians and scanners, and to limit costs and scan time. Most of the time, only one modality is acquired. In this paper, we propose KD-Net, a framework to transfer knowledge from a trained multi-…
Purify-then-Align: Towards Robust Human Sensing under Modality Missing with Knowledge Distillation from Noisy Multimodal Teacher
School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；Institute of Computer Science, Universität Bern（伯尔尼大学计算机科学研究所）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Decoupled Hierarchical Distillation for Multimodal Emotion Recognition
School of Computer Science and Engineering, and the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University（计算机科学与工程学院，新一代人工智能技术及跨学科应用重点实验室，东南大学）；School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；Institute of Intelligent Information Processing, Taizhou University（智能信息处理研究院，台州大学）；School of Engineering Science, University of Chinese Academy of Sciences（工程科学学院，中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization
National University of Singapore（新加坡国立大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；National Neuroscience Institute（国家神经科学研究所）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
Modality-Balanced Learning for Multimedia Recommendation
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）
Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labeled or pseudo-labeled datasets obtained from the active pixel sensor (APS) frames; however, such datasets' quality is far from rivaling those based on the canonical images. In this paper, we propose a novel approach, called \textbf{EvDistill}, to learn a student network on the unlabe…
With the rapid increase of multimedia data, a large body of literature has emerged to work on multimodal summarization, the majority of which target at refining salient information from textual and visual modalities to output a pictorial summary with the most relevant images. Existing methods mostly focus on either extractive or abstractive summarization and rely on qualified image captions to build image references. We are the first to propose a Unified framework for Multimodal Summarization grounding on BART, UniMS, that integrates extractive and abstractive objectives, as well as selecting …
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Data
University of Adelaide, Australia（澳大利亚阿德莱德大学）；New York University Abu Dhabi, UAE（阿联酋纽约大学阿布扎克分校）；University of Surrey, UK（英国萨里大学）；Boai hospital of Zhongshan, China（中国中山博爱医院）；University of Central Florida, USA（美国佛罗里达大学）
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
Rutgers University（罗格斯大学）；Sichuan University West China Hospital（四川大学华西医院）；Walmart Global Tech（沃尔玛全球科技）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Tongren Hospital（同仁医院）；Fudan University（复旦大学）；Zhongshan Hospital（中山医院）；SenseTime Research（商汤科技研究院）
Istituto Italiano di Tecnologia（意大利技术研究院）；University of Genova（热那亚大学）；University of Verona（维罗纳大学）
Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can be accurately collected to include a variety of sensory modalities, it is often the case that not all of them are available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities. This paper presents a …
University of Science and Technology of China（中国科学技术大学）；The University of Adelaide（阿德莱德大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Shanghai Artificial Intelligent Laboratory（上海人工智能实验室）
Recent progresses on self-supervised 3D human action representation learning are largely attributed to contrastive learning. However, in conventional contrastive frameworks, the rich complementarity between different skeleton modalities remains under-explored. Moreover, optimized with distinguishing self-augmented samples, models struggle with numerous similar positive instances in the case of limited action categories. In this work, we tackle the aforementioned problems by introducing a general Inter- and Intra-modal Mutual Distillation (I$^2$MD) framework. In I$^2$MD, we first re-formulate t…
We deal with the problem of information fusion driven satellite image/scene classification and propose a generic hallucination architecture considering that all the available sensor information are present during training while some of the image modalities may be absent while testing. It is well-known that different sensors are capable of capturing complementary information for a given geographical area and a classification module incorporating information from all the sources are expected to produce an improved performance as compared to considering only a subset of the modalities. However, t…
Univ. of Oxford（牛津大学）；Inst. of Mathematics of the Romanian Academy（罗马尼亚科学院数学研究所）；Univ. Politehnica of Bucharest（布加勒斯特理工大学）；Adobe Research（Adobe研究院）；Univ. of Cambridge（剑桥大学）；Peking Univ.（北京大学）
In recent years, considerable progress on the task of text-video retrieval has been achieved by leveraging large-scale pretraining on visual and audio datasets to construct powerful video encoders. By contrast, despite the natural symmetry, the design of effective algorithms for exploiting large-scale language pretraining remains under-explored. In this work, we are the first to investigate the design of such algorithms and propose a novel generalized distillation method, TeachText, which leverages complementary cues from multiple text encoders to provide an enhanced supervisory signal to the …
Imperial College London（伦敦帝国理工学院）；The Chinese University of Hong Kong（香港中文大学）
Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics. To effectively train such a highly compact model,…
Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective
Jiangsu University（江苏大学）；Griffith University（格里菲斯大学）；National University of Singapore（新加坡国立大学）；University of New South Wales（新南威尔士大学）；Fujian University of Technology（福建理工大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Tsinghua University（清华大学）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Queen Mary University of London（伦敦玛丽女王大学）
University of Tübingen（蒂宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；Tencent PCG（腾讯平台与内容事业群）；MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）
Having access to multi-modal cues (e.g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality. In this work, we propose to transfer knowledge across heterogeneous modalities, even though these data modalities may not be semantically correlated. Rather than directly aligning the representations of different modalities, we compose audio, image, and video representations across modalities to uncover richer multi-modal knowledge. Our main idea is to learn a compositional embedding that closes the cross-modal semantic gap and captures the task…
Nanjing University of Science and Technology（南京理工大学）
Human multimodal emotion recognition (MER) aims to perceive human emotions via language, visual and acoustic modalities. Despite the impressive performance of previous MER approaches, the inherent multimodal heterogeneities still haunt and the contribution of different modalities varies significantly. In this work, we mitigate this issue by proposing a decoupled multimodal distillation (DMD) approach that facilitates flexible and adaptive crossmodal knowledge distillation, aiming to enhance the discriminative features of each modality. Specially, the representation of each modality is decouple…
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；Key Laboratory of Intelligent Bionic Unmanned Systems, Ministry of Education（教育部智能仿生无人系统重点实验室）；School of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能学院）；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）
Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
State Key Laboratory of General Artificial Intelligence, Peking University（北京理工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）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；Key Laboratory of Target Cognition and Application Technology（目标认知与应用技术重点实验室）；Beijing Institute of Technology（北京理工大学）；Ucap Cloud（Ucap云）
Texas State University（德克萨斯州立大学）；Illinois Institute of Technology（伊利诺伊理工学院）
Wearable sensor-based Human Action Recognition (HAR) has achieved remarkable success recently. However, the accuracy performance of wearable sensor-based HAR is still far behind the ones from the visual modalities-based system (i.e., RGB video, skeleton, and depth). Diverse input modalities can provide complementary cues and thus improve the accuracy performance of HAR, but how to take advantage of multi-modal data on wearable sensor-based HAR has rarely been explored. Currently, wearable devices, i.e., smartwatches, can only capture limited kinds of non-visual modality data. This hinders the …
Lack of specialized data makes building a multi-domain neural machine translation tool challenging. Although emerging literature dealing with low resource languages starts to show promising results, most state-of-the-art models used millions of sentences. Today, the majority of multi-domain adaptation techniques are based on complex and sophisticated architectures that are not adapted for real-world applications. So far, no scalable method is performing better than the simple yet effective mixed-finetuning, i.e finetuning a generic model with a mix of all specialized data and generic data. In …
