In this paper, we propose a weakly supervised deep temporal encoding-decoding solution for anomaly detection in surveillance videos using multiple instance learning. The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we treat video as a bag and video clips as instances in the bag. Our main contribution lies in the proposed novel approach to consider temporal relations between video instances. We deal with video instances (clips) as a sequential visual data rather than independent instances. We employ a deep temporal and encoder network that is designed to capture spatial-temporal evolution of video instances over time. We also propose a new loss function that is smoother than similar loss functions recently presented in the computer vision literature, and therefore; enjoys faster convergence and improved tolerance to local minima during the training phase. The proposed temporal encoding-decoding approach with modified loss is benchmarked against the state-of-the-art in simulation studies. The results show that the proposed method performs similar to or better than the state-of-the-art solutions for anomaly detection in video surveillance applications.
University of Chinese Academy of Sciences（中国科学院大学）；CASIA（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
The task of Dense Video Captioning (DVC) aims to generate captions with timestamps for multiple events in one video. Semantic information plays an important role for both localization and description of DVC. We present a semantic-assisted dense video captioning model based on the encoding-decoding framework. In the encoding stage, we design a concept detector to extract semantic information, which is then fused with multi-modal visual features to sufficiently represent the input video. In the decoding stage, we design a classification head, paralleled with the localization and captioning heads…
Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）；Alibaba Group（阿里巴巴集团）
In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data. The proposed SA-CNN introduces convolution and transposed convolution stacks to capture and generate contextual information among unordered 3D points. Following conventional hierarchical pipeline, the encoding process extracts feature in local-to-global manner, while the decoding process generates feature and point cloud in coarse-to-fine, multi-resolution stages. We demonstrate that SA-CNN is capable of a wide range of applica…
Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE.MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning. For anomaly detection, we acco…
Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks
Department of Advanced Computing Sciences (DACS), University of Maastricht (UM)（先进计算科学系（DACS）、马斯特里赫特大学（UM））；Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH)（信息科技研究所（ITI）、希腊研究中心与技术中心（CERTH））
Sparse-View Interpretable 3D Animal Behavior Representations for Neural Encoding and Decoding
Harvard University（哈佛大学）；University of Cambridge（剑桥大学）；Northwestern University（西北大学）；Cold Spring Harbor Laboratory（冷泉港实验室）；Columbia University（哥伦比亚大学）；University of Science and Technology of China（中国科学技术大学）
FPT Software AI Center（FPT软件人工智能中心）；John von Neumann Institute, Vietnam National University（越南国家大学冯·诺依曼研究院）；My Duc Phu Nhuan Hospital（美德富润医院）；Vinmec Central Park International Hospital（Vinmec中央公园国际医院）；Arkansas Economic Development Commission（阿肯色州经济发展委员会）；University of Science, VNU-HCM（越南国家大学胡志明市分校科学大学）；University of Arkansas（阿肯色大学）；My Duc Hospital（美德医院）
The timing of cell divisions in early embryos during the In-Vitro Fertilization (IVF) process is a key predictor of embryo viability. However, observing cell divisions in Time-Lapse Monitoring (TLM) is a time-consuming process and highly depends on experts. In this paper, we propose EmbryosFormer, a computational model to automatically detect and classify cell divisions from original time-lapse images. Our proposed network is designed as an encoder-decoder deformable transformer with collaborative heads. The transformer contracting path predicts per-image labels and is optimized by a classific…
The dispute of how the human brain represents conceptual knowledge has been argued in many scientific fields. Brain imaging studies have shown that the spatial patterns of neural activation in the brain are correlated with thinking about different semantic categories of words (for example, tools, animals, and buildings) or when viewing the related pictures. In this paper, we present a computational model that learns to predict the neural activation captured in functional magnetic resonance imaging (fMRI) data of test words. Unlike the models with hand-crafted features that have been used in th…
Centre for Research & Technology Hellas（色雷斯研究与技术中心）；Information Technologies Institute（信息技术研究所）
A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i.e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy. In this report, we aim to reproduce the findings of DARK that investigated the 2D heatmap representation by highlighting the importance of the encoding of the ground truth heatmap and the decoding of the predicted heatmap to keypoint coordinates. The authors claim that a) a more principled distribution-aware coordinate decod…
In many professional fields, such as medicine, remote sensing and sciences, users often demand image compression methods to be mathematically lossless. But lossless image coding has a rather low compression ratio (around 2:1 for natural images). The only known technique to achieve significant compression while meeting the stringent fidelity requirements is the methodology of $\ell_\infty$-constrained coding that was developed and standardized in nineties. We make a major progress in $\ell_\infty$-constrained image coding after two decades, by developing a novel CNN-based soft $\ell_\infty$-con…
Learning Cortical Anomaly through Masked Encoding for Unsupervised Heterogeneity Mapping
Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health (TÜCMH)（图宾根心理健康中心精神病学与心理治疗系）；Norwegian Centre for Mental Disorders Research（挪威精神障碍研究中心）；Donders Institute for Brain, Cognition, and Behaviour, Radboud University（拉德堡德大学唐德斯脑、认知与行为研究所）
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Cixi Institute of Biomedical Engineering, Chinese Academy of Sciences（中国科学院慈溪生物医学工程研究所）；UBTech Research（优必选研究院）；Southern University of Science and Technology（南方科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与影像工程技术研究中心）
Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions, by only leveraging normal images in training phase. Normal images from healthy subjects often have regular structures (e.g., the structured blood vessels in the fundus image, or structured anatomy in optical coherence tomography image). On the contrary, the diseases and lesions often destroy these structures. Motivated by this, we propose to leverage the relation between the image texture and structure to design a deep neural network for anomaly detection. Specifically, we…
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)
Inria Bordeaux（法国国家信息与自动化研究所波尔多分所）；LaBRI, Université de Bordeaux（波尔多大学拉布里实验室）；Université de Bordeaux（波尔多大学）；TU Berlin（柏林工业大学）；National University of Singapore（新加坡国立大学）；Microsoft（微软公司）；International Institute of Information Technology Hyderabad（海得拉巴国际信息技术研究所）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）
