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 401–425 of 455 papers

TitleStatusHype
UTS submission to Google YouTube-8M Challenge 2017Code0
Hierarchical Deep Recurrent Architecture for Video UnderstandingCode0
A spatiotemporal model with visual attention for video classification—0
Tensor-Train Recurrent Neural Networks for Video ClassificationCode0
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video ClassificationCode0
Aggregating Frame-level Features for Large-Scale Video Classification—0
The YouTube-8M Kaggle Competition: Challenges and MethodsCode0
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform—0
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M datasetCode0
Multiresolution Match Kernels for Gesture Video Classification—0
Learnable pooling with Context Gating for video classificationCode0
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and TextCode0
The Monkeytyping Solution to the YouTube-8M Video Understanding ChallengeCode0
Hierarchical Label Inference for Video Classification—0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks—0
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification—0
ActionVLAD: Learning spatio-temporal aggregation for action classification—0
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity RecognitionCode0
Saliency-guided video classification via adaptively weighted learning—0
Graph-based Isometry Invariant Representation Learning—0
Deep Motion Features for Visual Tracking—0
Generating Video Description using Sequence-to-sequence Model with Temporal Attention—0
Fast Video Classification via Adaptive Cascading of Deep Models—0
Deep Learning for Video Classification and CaptioningCode0
DAiSEE: Towards User Engagement Recognition in the Wild—0
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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