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 301325 of 455 papers

TitleStatusHype
Metric-Based Few-Shot Learning for Video Action Recognition0
Identifying and Resisting Adversarial Videos Using Temporal Consistency0
Distributed Deep Convolutional Neural Networks for the Internet-of-Things0
Two-Stream Video Classification with Cross-Modality Attention0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition0
AVD: Adversarial Video Distillation0
Loss Switching Fusion with Similarity Search for Video ClassificationCode0
Few-Shot Video Classification via Temporal Alignment0
Spatio-Temporal Fusion Networks for Action Recognition0
Learning Spatio-Temporal Representation with Local and Global DiffusionCode0
Learning Video Representations using Contrastive Bidirectional Transformer0
FASTER Recurrent Networks for Efficient Video Classification0
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network0
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video ArchitecturesCode0
EEG-based Emotional Video Classification via Learning Connectivity StructureCode1
Hallucinating Optical Flow Features for Video ClassificationCode0
Exploring Temporal Information for Improved Video UnderstandingCode0
VideoGraph: Recognizing Minutes-Long Human Activities in Videos0
On Flow Profile Image for Video Representation0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
Billion-scale semi-supervised learning for image classificationCode1
The Expressive Power of Deep Neural Networks with Circulant Matrices0
MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA0
Where and when to look? Spatial-temporal attention for action recognition in videos0
Factor Analysis in Fault Diagnostics Using Random Forest0
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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