Papers Video Classification
“Video Classification” 태그가 달린 논문 495편 · 필터 해제
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL) proposes one solution to this problem.…
BenchmarkingGeneral ClassificationVideo ClassificationZero-Shot Action Recognition+1VideoSSL: Semi-Supervised Learning for Video Classification
We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised video classification methods demand a la…
ClassificationGeneral ClassificationVideo ClassificationOver-the-Air Adversarial Flickering Attacks against Video Recognition Networks
Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter…
Action ClassificationClassificationGeneral Classificationimage-classification+4Learning spatio-temporal representations with temporal squeeze pooling
In this paper, we propose a new video representation learning method, named Temporal Squeeze (TS) pooling, which can extract the essential movement information from a long sequence of video frames and map it into a set o…
Action RecognitionClassificationGeneral ClassificationRepresentation Learning+1FSD-10: A Dataset for Competitive Sports Content Analysis
Action recognition is an important and challenging problem in video analysis. Although the past decade has witnessed progress in action recognition with the development of deep learning, such process has been slow in com…
Action RecognitionBenchmarkingVideo Classificationiqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention
In this report, the method for the iqiyi submission to the task of ActivityNet 2019 Kinetics-700 challenge is described. Three models are involved in the model ensemble stage: TSN, HG-NL and StNet. We propose the hierarc…
General ClassificationVideo ClassificationCross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification
Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of d…
ClassificationGeneral ClassificationGraph Embeddingimage-classification+5Appending Adversarial Frames for Universal Video Attack
There have been many efforts in attacking image classification models with adversarial perturbations, but the same topic on video classification has not yet been thoroughly studied. This paper presents a novel idea of vi…
ClassificationGeneral Classificationimage-classificationImage Classification+1Video action detection by learning graph-based spatio-temporal interactions
Action Detection is a complex task that aims to detect and classify human actions in video clips. Typically, it has been addressed by processing fine-grained features extracted from a video classification backbone. Recen…
Action DetectionAction LocalizationGraph LearningSpatio-Temporal Action Localization+4VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
This paper introduces video domain generalization where most video classification networks degenerate due to the lack of exposure to the target domains of divergent distributions. We observe that the global temporal feat…
Action RecognitionData AugmentationDiversityDomain Generalization+4DASZL: Dynamic Action Signatures for Zero-shot Learning
There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no…
Action DetectionActivity DetectionActivity RecognitionVideo Classification+1A Multigrid Method for Efficiently Training Video Models
Training competitive deep video models is an order of magnitude slower than training their counterpart image models. Slow training causes long research cycles, which hinders progress in video understanding research. Foll…
Action DetectionAction RecognitionGPUVideo Classification+1A Spectral Nonlocal Block for Neural Networks
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performances, they lack the mechanism to encode the rich, structured…
Action RecognitionFine-Grained Image ClassificationGeneral Classificationimage-classification+3Towards Train-Test Consistency for Semi-supervised Temporal Action Localization
Recently, Weakly-supervised Temporal Action Localization (WTAL) has been densely studied but there is still a large gap between weakly-supervised models and fully-supervised models. It is practical and intuitive to annot…
Action LocalizationMultiple Instance LearningTemporal Action LocalizationVideo Classification+1Fast Non-Local Neural Networks with Spectral Residual Learning
Effectively modeling long-range spatial correlation is crucial in context-sensitive visual computing tasks, such as human pose estimation and video classification. Enlarging receptive field is popularly adopted in buildi…
Pose EstimationVideo ClassificationAWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation
We propose an Adaptive Weighted Spatiotemporal Distillation (AWSD) technique for video representation by encoding the appearance and dynamics of the videos into a single RGB image map. This is obtained by adaptively divi…
General ClassificationVideo ClassificationGated Channel Transformation for Visual Recognition
In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. This transformation explicitly models channel relationships with explainable control var…
General Classificationimage-classificationImage ClassificationInstance Segmentation+4Spectral Nonlocal Block for Neural Network
The nonlocal network is designed for capturing long-range spatial-temporal dependencies in several computer vision tasks. Although having shown excellent performances, it needs an elaborate preparation for both the numbe…
Video ClassificationUNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS
Extracting underlying dynamics of objects in image sequences is one of the challenging problems in computer vision. On the other hand, dynamic mode decomposition (DMD) has recently attracted attention as a way of obtaini…
DecoderTime SeriesTime Series AnalysisVideo ClassificationSelf-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks
There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating samples through Generative Adversarial N…
Data AugmentationGeneral ClassificationVideo Classification