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 51100 of 455 papers

TitleStatusHype
MViTv2: Improved Multiscale Vision Transformers for Classification and DetectionCode1
Adaptive Token Sampling For Efficient Vision TransformersCode1
AdaPool: Exponential Adaptive Pooling for Information-Retaining DownsamplingCode1
A Closer Look at Few-Shot Video Classification: A New Baseline and BenchmarkCode1
A Unified Taxonomy and Multimodal Dataset for Events in Invasion GamesCode1
Token Shift Transformer for Video ClassificationCode1
Out-of-Distribution Detection Using Union of 1-Dimensional SubspacesCode1
Self-supervised Video Representation Learning with Cross-Stream Prototypical ContrastingCode1
CT-Net: Channel Tensorization Network for Video ClassificationCode1
A Spatio-temporal Attention-based Model for Infant Movement Assessment from VideosCode1
Home Action Genome: Cooperative Compositional Action UnderstandingCode1
Learning Implicit Temporal Alignment for Few-shot Video ClassificationCode1
Busy-Quiet Video Disentangling for Video ClassificationCode1
ViViT: A Video Vision TransformerCode1
Revisiting ResNets: Improved Training and Scaling StrategiesCode1
Piano Skills AssessmentCode1
Self-Supervised Pretraining of 3D Features on any Point-CloudCode1
Reinforcement Learning with Latent FlowCode1
VideoMix: Rethinking Data Augmentation for Video ClassificationCode1
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video ClassificationCode1
Is normalization indispensable for training deep neural network?Code1
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural NetworksCode1
Active Contrastive Learning of Audio-Visual Video RepresentationsCode1
Making a Case for 3D Convolutions for Object Segmentation in VideosCode1
Approximated Bilinear Modules for Temporal ModelingCode1
MotionSqueeze: Neural Motion Feature Learning for Video UnderstandingCode1
Region-based Non-local Operation for Video ClassificationCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
SmallBigNet: Integrating Core and Contextual Views for Video ClassificationCode1
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual RecognitionCode1
Non-Local Neural Networks With Grouped Bilinear Attentional TransformsCode1
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-SpoofingCode1
Convolutional Spiking Neural Networks for Spatio-Temporal Feature ExtractionCode1
Motion-Excited Sampler: Video Adversarial Attack with Sparked PriorCode1
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial LocationCode1
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic ApplicationsCode1
Over-the-Air Adversarial Flickering Attacks against Video Recognition NetworksCode1
A Multigrid Method for Efficiently Training Video ModelsCode1
EEG-based Emotional Video Classification via Learning Connectivity StructureCode1
Billion-scale semi-supervised learning for image classificationCode1
Large Scale Holistic Video UnderstandingCode1
Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance StatisticsCode1
Timeception for Complex Action RecognitionCode1
Compact Generalized Non-local NetworkCode1
On the effectiveness of task granularity for transfer learningCode1
Learning Spatio-Temporal Representation with Pseudo-3D Residual NetworksCode1
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video ClassificationCode1
Non-local Neural NetworksCode1
Deep Temporal Linear Encoding NetworksCode1
YouTube-8M: A Large-Scale Video Classification BenchmarkCode1
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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