Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
Metric-Based Few-Shot Learning for Video Action Recognition
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gone into few-shot learning for image class…
Action RecognitionFew-Shot LearningGeneral Classificationimage-classification+3Identifying and Resisting Adversarial Videos Using Temporal Consistency
Video classification is a challenging task in computer vision. Although Deep Neural Networks (DNNs) have achieved excellent performance in video classification, recent research shows adding imperceptible perturbations to…
Video ClassificationDistributed Deep Convolutional Neural Networks for the Internet-of-Things
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load…
Decision MakingDistributed ComputingVideo ClassificationTwo-Stream Video Classification with Cross-Modality Attention
Fusing multi-modality information is known to be able to effectively bring significant improvement in video classification. However, the most popular method up to now is still simply fusing each stream's prediction score…
Action ClassificationAction RecognitionClassificationGeneral Classification+3Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition
Video Recognition has drawn great research interest and great progress has been made. A suitable frame sampling strategy can improve the accuracy and efficiency of recognition. However, mainstream solutions generally ado…
Action RecognitionGeneral ClassificationMulti-agent Reinforcement Learningreinforcement-learning+4AVD: Adversarial Video Distillation
In this paper, we present a simple yet efficient approach for video representation, called Adversarial Video Distillation (AVD). The key idea is to represent videos by compressing them in the form of realistic images, wh…
Activity RecognitionDecoderGeneral ClassificationVideo ClassificationLoss Switching Fusion with Similarity Search for Video Classification
From video streaming to security and surveillance applications, video data play an important role in our daily living today. However, managing a large amount of video data and retrieving the most useful information for t…
ClassificationClusteringGeneral ClassificationScene Understanding+1Few-Shot Video Classification via Temporal Alignment
There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that ca…
Action RecognitionClassificationFew Shot Action RecognitionFew-Shot Learning+2Spatio-Temporal Fusion Networks for Action Recognition
The video based CNN works have focused on effective ways to fuse appearance and motion networks, but they typically lack utilizing temporal information over video frames. In this work, we present a novel spatio-temporal …
Action RecognitionVideo ClassificationLearning Video Representations using Contrastive Bidirectional Transformer
This paper proposes a self-supervised learning approach for video features that results in significantly improved performance on downstream tasks (such as video classification, captioning and segmentation) compared to ex…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Representation LearningSelf-Supervised Learning+4Learning Spatio-Temporal Representation with Local and Global Diffusion
Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the larg…
Action ClassificationAction DetectionAction RecognitionRepresentation Learning+3FASTER Recurrent Networks for Efficient Video Classification
Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visu…
Action ClassificationAction RecognitionClassificationGeneral Classification+1Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the effi…
General ClassificationGraph Neural NetworkVideo ClassificationVideo UnderstandingAssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures
Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to…
Action ClassificationAction RecognitionMultimodal Activity RecognitionOptical Flow Estimation+2Hallucinating Optical Flow Features for Video Classification
Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion …
ClassificationGeneral ClassificationHallucinationOptical Flow Estimation+1EEG-based Emotional Video Classification via Learning Connectivity Structure
Electroencephalography (EEG) is a useful way to implicitly monitor the users perceptual state during multimedia consumption. One of the primary challenges for the practical use of EEG-based monitoring is to achieve a sat…
ClassificationEEGElectroencephalogram (EEG)General Classification+1Exploring Temporal Information for Improved Video Understanding
In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and semantic segmentation. For action recogni…
Action RecognitionOptical Flow EstimationSegmentationSemantic Segmentation+3VideoGraph: Recognizing Minutes-Long Human Activities in Videos
Many human activities take minutes to unfold. To represent them, related works opt for statistical pooling, which neglects the temporal structure. Others opt for convolutional methods, as CNN and Non-Local. While success…
Long-video Activity RecognitionVideo ClassificationBudgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed…
General Classificationimage-classificationImage ClassificationInstance Segmentation+5On Flow Profile Image for Video Representation
Video representation is a key challenge in many computer vision applications such as video classification, video captioning, and video surveillance. In this paper, we propose a novel approach for video representation tha…
Activity RecognitionOptical Flow EstimationVideo CaptioningVideo Classification