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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 455 papers

TitleStatusHype
DAiSEE: Towards User Engagement Recognition in the Wild—0
Deep Architectures and Ensembles for Semantic Video Classification—0
Deep Discriminative Model for Video Classification—0
Deep End2End Voxel2Voxel Prediction—0
CNNs for JPEGs: A Study in Computational Cost—0
Deep Motion Features for Visual Tracking—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Deep Unsupervised Key Frame Extraction for Efficient Video Classification—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
Distributed Deep Convolutional Neural Networks for the Internet-of-Things—0
Distribution Adaptive INT8 Quantization for Training CNNs—0
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark—0
Document-Level Sentiment Analysis of Urdu Text Using Deep Learning Techniques—0
DynamoNet: Dynamic Action and Motion Network—0
Efficient Action Localization with Approximately Normalized Fisher Vectors—0
Efficient Audio-Visual Fusion for Video Classification—0
Efficient Large Scale Video Classification—0
Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification—0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification—0
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification—0
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks—0
End-to-End Video Classification with Knowledge Graphs—0
Evaluating Two-Stream CNN for Video Classification—0
Evolution of active categorical image classification via saccadic eye movement—0
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification—0
Factor Analysis in Fault Diagnostics Using Random Forest—0
Factorized Topic Models—0
FASTER Recurrent Networks for Efficient Video Classification—0
Fast Video Classification via Adaptive Cascading of Deep Models—0
Few-Shot Video Classification via Representation Fusion and Promotion Learning—0
Few-Shot Video Classification via Temporal Alignment—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Fine-grained Video Categorization with Redundancy Reduction Attention—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
Fusing Multi-Stream Deep Networks for Video Classification—0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification—0
Generating Natural Language Summaries for Multimedia—0
Generating Video Description using Sequence-to-sequence Model with Temporal Attention—0
GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video—0
Goal-driven text descriptions for images—0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition—0
Graph-based Isometry Invariant Representation Learning—0
Handcrafted Local Features are Convolutional Neural Networks—0
Hand Hygiene Video Classification Based on Deep Learning—0
Hand Pose Classification Based on Neural Networks—0
Harnessing Object and Scene Semantics for Large-Scale Video Understanding—0
Hierarchical Label Inference for Video Classification—0
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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