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 326350 of 455 papers

TitleStatusHype
Evaluating Two-Stream CNN for Video Classification0
Evolution of active categorical image classification via saccadic eye movement0
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification0
Factor Analysis in Fault Diagnostics Using Random Forest0
Factorized Topic Models0
FASTER Recurrent Networks for Efficient Video Classification0
Fast Video Classification via Adaptive Cascading of Deep Models0
Few-Shot Video Classification via Representation Fusion and Promotion Learning0
Few-Shot Video Classification via Temporal Alignment0
Fine-Grained AutoAugmentation for Multi-Label Classification0
Fine-grained Video Categorization with Redundancy Reduction Attention0
FSD-10: A Dataset for Competitive Sports Content Analysis0
Fusing Multi-Stream Deep Networks for Video Classification0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification0
Generating Natural Language Summaries for Multimedia0
Generating Video Description using Sequence-to-sequence Model with Temporal Attention0
GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video0
Goal-driven text descriptions for images0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition0
Graph-based Isometry Invariant Representation Learning0
Handcrafted Local Features are Convolutional Neural Networks0
Hand Hygiene Video Classification Based on Deep Learning0
Hand Pose Classification Based on Neural Networks0
Harnessing Object and Scene Semantics for Large-Scale Video Understanding0
Hierarchical Label Inference for Video Classification0
Show:102550
← PrevPage 14 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