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

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Showing 201–250 of 455 papers

TitleStatusHype
Token Shift Transformer for Video ClassificationCode1
Temporal Alignment Prediction for Few-Shot Video Classification—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Aligning Correlation Information for Domain Adaptation in Action Recognition—0
Attention Bottlenecks for Multimodal FusionCode0
When Video Classification Meets Incremental Classes—0
Video Swin TransformerCode2
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video ClassificationCode0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition—0
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 Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
A Spatio-temporal Attention-based Model for Infant Movement Assessment from VideosCode1
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture—0
Home Action Genome: Cooperative Compositional Action UnderstandingCode1
Learning Implicit Temporal Alignment for Few-shot Video ClassificationCode1
VidTr: Video Transformer Without Convolutions—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories—0
Busy-Quiet Video Disentangling for Video ClassificationCode1
Classifying Video based on Automatic Content Detection Overview—0
ViViT: A Video Vision TransformerCode1
Revisiting ResNets: Improved Training and Scaling StrategiesCode1
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Is Space-Time Attention All You Need for Video Understanding?Code2
Distribution Adaptive INT8 Quantization for Training CNNs—0
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks—0
Piano Skills AssessmentCode1
Self-Supervised Pretraining of 3D Features on any Point-CloudCode1
Reinforcement Learning with Latent FlowCode1
Self-supervised Temporal Learning—0
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking—0
CNNs for JPEGs: A Study in Computational Cost—0
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates—0
Smoothed Gaussian Mixture Models for Video Classification and Recommendation—0
VideoMix: Rethinking Data Augmentation for Video ClassificationCode1
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video ClassificationCode1
Is normalization indispensable for training deep neural network?Code1
t-EVA: Time-Efficient t-SNE Video Annotation—0
Deep Multimodality Learning for UAV Video Aesthetic Quality AssessmentCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Attention-Aware Noisy Label Learning for Image Classification—0
Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural NetworksCode1
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All datasetsBreakfastCOINMoBYouTube-8MHockey Fight Detection DatasetCharadesHome Action GenomeKineticsMultimodal PISASomething-Something V1Something-Something V2SRI-APPROVE Fine-Grained Video Classification

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