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 151–200 of 455 papers

TitleStatusHype
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models—0
Neural Networks for irregularly observed continuous-time Stochastic Processes—0
CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
Identifying and Resisting Adversarial Videos Using Temporal Consistency—0
A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP—0
Aggregating Frame-level Features for Large-Scale Video Classification—0
Convolutional Drift Networks for Video Classification—0
Hierarchical Label Inference for Video Classification—0
Active Learning for Video Classification with Frame Level Queries—0
A Spectral Nonlocal Block for Neural Networks—0
Context-Aware Detection of Mixed Critical Events using Video Classification—0
Metric-Based Few-Shot Learning for Video Action Recognition—0
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification—0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
Handcrafted Local Features are Convolutional Neural Networks—0
Compound Memory Networks for Few-shot Video Classification—0
A spatiotemporal model with visual attention for video classification—0
Graph-based Isometry Invariant Representation Learning—0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—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
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels—0
Higher-order Network for Action Recognition—0
MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA—0
Goal-driven text descriptions for images—0
GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video—0
Classifying Video based on Automatic Content Detection Overview—0
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames—0
Generating Video Description using Sequence-to-sequence Model with Temporal Attention—0
Generating Natural Language Summaries for Multimedia—0
Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos—0
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification—0
Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning—0
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization—0
ActionVLAD: Learning spatio-temporal aggregation for action classification—0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification—0
Fusing Multi-Stream Deep Networks for Video Classification—0
Approach for Video Classification with Multi-label on YouTube-8M Dataset—0
LookupViT: Compressing visual information to a limited number of tokens—0
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
Fine-grained Video Categorization with Redundancy Reduction Attention—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