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 176200 of 455 papers

TitleStatusHype
Learning To Recognize Procedural Activities with Distant SupervisionCode1
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action RecognitionCode0
UniFormer: Unifying Convolution and Self-attention for Visual RecognitionCode2
Progressive Video Summarization via Multimodal Self-supervised LearningCode1
Co-training Transformer with Videos and Images Improves Action Recognition0
Approaches Toward Physical and General Video Anomaly DetectionCode0
MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video ClassificationCode0
MViTv2: Improved Multiscale Vision Transformers for Classification and DetectionCode1
PreViTS: Contrastive Pretraining with Video Tracking Supervision0
Adaptive Token Sampling For Efficient Vision TransformersCode1
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets0
Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing0
MetaVD: A Meta Video Dataset for enhancing human action recognition datasetsCode0
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
A Closer Look at Few-Shot Video Classification: A New Baseline and BenchmarkCode1
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric TransformationsCode0
An Investigation on Hardware-Aware Vision Transformer Scaling0
Predicting Driver Self-Reported Stress by Analyzing the Road Scene0
Overview of Tencent Multi-modal Ads Video Understanding Challenge0
Goal-driven text descriptions for images0
A Unified Taxonomy and Multimodal Dataset for Events in Invasion GamesCode1
Hand Hygiene Video Classification Based on Deep Learning0
Hand Pose Classification Based on Neural Networks0
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing0
Show:102550
← PrevPage 8 of 19Next →

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