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 351–375 of 455 papers

TitleStatusHype
Deep RNN Framework for Visual Sequential ApplicationsCode0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Higher-order Network for Action Recognition—0
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video ClassificationCode0
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization—0
Fine-grained Video Categorization with Redundancy Reduction Attention—0
Training compact deep learning models for video classification using circulant matricesCode0
Representation Flow for Action RecognitionCode0
Learnable Pooling Methods for Video ClassificationCode0
Non-local NetVLAD Encoding for Video Classification—0
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural NetworksCode0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling—0
Towards Good Practices for Multi-modal Fusion in Large-scale Video Classification—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Compound Memory Networks for Few-shot Video Classification—0
Approach for Video Classification with Multi-label on YouTube-8M Dataset—0
Isometric Transformation Invariant Graph-based Deep Neural Network—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Deep Discriminative Model for Video Classification—0
Deep Architectures and Ensembles for Semantic Video Classification—0
Adversarial Perturbations Against Real-Time Video Classification SystemsCode0
A convex method for classification of groups of examples—0
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames—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