SOTAVerified

Video Classification

Video Classification is the task of producing a label that is relevant to the video given its frames. A good video level classifier is one that not only provides accurate frame labels, but also best describes the entire video given the features and the annotations of the various frames in the video. For example, a video might contain a tree in some frame, but the label that is central to the video might be something else (e.g., “hiking”). The granularity of the labels that are needed to describe the frames and the video depends on the task. Typical tasks include assigning one or more global labels to the video, and assigning one or more labels for each frame inside the video.

Source: Efficient Large Scale Video Classification

Papers

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Showing 301–350 of 455 papers

TitleStatusHype
ToxVidLM: A Multimodal Framework for Toxicity Detection in Code-Mixed Videos—0
Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach—0
Transfer-learning for video classification: Video Swin Transformer on multiple domains—0
Transformers Meet Visual Learning Understanding: A Comprehensive Review—0
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos—0
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification—0
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing—0
Two-Stream Transformer Architecture for Long Video Understanding—0
Two-Stream Video Classification with Cross-Modality Attention—0
UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV—0
Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks—0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling—0
UniForensics: Face Forgery Detection via General Facial Representation—0
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS—0
Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos—0
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks—0
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories—0
Video Classification using Semantic Concept Co-occurrences—0
Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor—0
Video Contents Understanding using Deep Neural Networks—0
VideoGraph: Recognizing Minutes-Long Human Activities in Videos—0
VideoSSL: Semi-Supervised Learning for Video Classification—0
VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners—0
Video Token Merging for Long-form Video Understanding—0
Video Understanding as Machine Translation—0
VidTr: Video Transformer Without Convolutions—0
Visual Data Synthesis via GAN for Zero-Shot Video Classification—0
Walk-Steered Convolution for Graph Classification—0
When Video Classification Meets Incremental Classes—0
Where and when to look? Spatial-temporal attention for action recognition in videos—0
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network—0
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization—0
MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA—0
Metric-Based Few-Shot Learning for Video Action Recognition—0
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification—0
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification—0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Multi-label Video Classification for Underwater Ship Inspection—0
Multi-modal Aggregation for Video Classification—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models—0
Loss Switching Fusion with Similarity Search for Video ClassificationCode0
Differentiable Resolution Compression and Alignment for Efficient Video Classification and RetrievalCode0
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All datasetsBreakfastCOINMoBYouTube-8MHockey Fight Detection DatasetCharadesHome Action GenomeKineticsMultimodal PISASomething-Something V1Something-Something V2SRI-APPROVE Fine-Grained Video Classification

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)95.2—Unverified
2MA-LMMAccuracy (%)93—Unverified
3S5Accuracy (%)90.7—Unverified
4TranS4merAccuracy (%)90.27—Unverified
5D-Sprv.Accuracy (%)89.9—Unverified
6ViS4merAccuracy (%)88.2—Unverified
7GHRMAccuracy (%)75.5—Unverified
8TimeceptionAccuracy (%)71.3—Unverified
9VideoGraphAccuracy (%)69.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)93.5—Unverified
2MA-LMMAccuracy (%)93.2—Unverified
3S5Accuracy (%)90.8—Unverified
4D-Sprv.Accuracy (%)90—Unverified
5TranS4merAccuracy (%)89.3—Unverified
6ViS4merAccuracy (%)88.4—Unverified
7TSNAccuracy (%)73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1VTNAccuracy77.85—Unverified
2I3DAccuracy72.11—Unverified
3ConvLSTMAccuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1DCGN (self-attention graph pooling)Hit@187.7—Unverified
2Hierarchical LSTM with MoEHit@186.8—Unverified
3Mixture-of-2-ExpertsHit@170.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy99.5—Unverified
2CNN+LSTM1:1 Accuracy98—Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridmAP38.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Cooperative Ours (3rd-person)Accuracy (%)24.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridTop-177.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VideoAccuracy (%)73.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy84—Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy91—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Label Prototypes Contrastive LearningAUPR88.4—Unverified