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

Showing 401425 of 455 papers

TitleStatusHype
Hallucinating Optical Flow Features for Video ClassificationCode0
Adversarial Framing for Image and Video ClassificationCode0
Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video ClassificationCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
A Multimodal Handover Failure Detection Dataset and BaselinesCode0
Group NormalizationCode0
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation LearningCode0
Tensor-Train Recurrent Neural Networks for Video ClassificationCode0
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video ClassificationCode0
Gated Channel Transformation for Visual RecognitionCode0
Fine-grained Activity Recognition in Baseball VideosCode0
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)Code0
Fast Non-Local Neural Networks with Spectral Residual LearningCode0
Attention Bottlenecks for Multimodal FusionCode0
FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas WarCode0
Text-to-feature diffusion for audio-visual few-shot learningCode0
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video ArchitecturesCode0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
VPAI_Lab at MedVidQA 2022: A Two-Stage Cross-modal Fusion Method for Medical Instructional Video ClassificationCode0
The Monkeytyping Solution to the YouTube-8M Video Understanding ChallengeCode0
Pushing the boundaries of event subsampling in event-based video classification using CNNsCode0
Extending Information Bottleneck Attribution to Video SequencesCode0
Exploring Temporal Information for Improved Video UnderstandingCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)95.2Unverified
2MA-LMMAccuracy (%)93Unverified
3S5Accuracy (%)90.7Unverified
4TranS4merAccuracy (%)90.27Unverified
5D-Sprv.Accuracy (%)89.9Unverified
6ViS4merAccuracy (%)88.2Unverified
7GHRMAccuracy (%)75.5Unverified
8TimeceptionAccuracy (%)71.3Unverified
9VideoGraphAccuracy (%)69.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)93.5Unverified
2MA-LMMAccuracy (%)93.2Unverified
3S5Accuracy (%)90.8Unverified
4D-Sprv.Accuracy (%)90Unverified
5TranS4merAccuracy (%)89.3Unverified
6ViS4merAccuracy (%)88.4Unverified
7TSNAccuracy (%)73.4Unverified
#ModelMetricClaimedVerifiedStatus
1VTNAccuracy77.85Unverified
2I3DAccuracy72.11Unverified
3ConvLSTMAccuracy69.71Unverified
#ModelMetricClaimedVerifiedStatus
1DCGN (self-attention graph pooling)Hit@187.7Unverified
2Hierarchical LSTM with MoEHit@186.8Unverified
3Mixture-of-2-ExpertsHit@170.1Unverified
#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy99.5Unverified
2CNN+LSTM1:1 Accuracy98Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridmAP38.2Unverified
#ModelMetricClaimedVerifiedStatus
1Cooperative Ours (3rd-person)Accuracy (%)24.7Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridTop-177.6Unverified
#ModelMetricClaimedVerifiedStatus
1VideoAccuracy (%)73.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy84Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy91Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Label Prototypes Contrastive LearningAUPR88.4Unverified