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 301325 of 455 papers

TitleStatusHype
DAiSEE: Towards User Engagement Recognition in the Wild0
Deep Architectures and Ensembles for Semantic Video Classification0
Deep Discriminative Model for Video Classification0
Deep End2End Voxel2Voxel Prediction0
CNNs for JPEGs: A Study in Computational Cost0
Deep Motion Features for Visual Tracking0
Deep Multimodal Learning: An Effective Method for Video Classification0
Deep-Temporal LSTM for Daily Living Action Recognition0
Deep Unsupervised Key Frame Extraction for Efficient Video Classification0
Defending Against Multiple and Unforeseen Adversarial Videos0
Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning0
Discriminatively Trained Latent Ordinal Model for Video Classification0
Distributed Deep Convolutional Neural Networks for the Internet-of-Things0
Distribution Adaptive INT8 Quantization for Training CNNs0
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques0
DynamoNet: Dynamic Action and Motion Network0
Efficient Action Localization with Approximately Normalized Fisher Vectors0
Efficient Audio-Visual Fusion for Video Classification0
Efficient Large Scale Video Classification0
Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification0
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification0
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks0
End-to-End Video Classification with Knowledge Graphs0
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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