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 251–300 of 455 papers

TitleStatusHype
ActionVLAD: Learning spatio-temporal aggregation for action classification—0
Active Learning for Video Classification with Frame Level Queries—0
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction—0
AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction—0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression—0
Aggregating Frame-level Features for Large-Scale Video Classification—0
Aligning Correlation Information for Domain Adaptation in Action Recognition—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception—0
AM Flow: Adapters for Temporal Processing in Action Recognition—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
An Effective Way to Improve YouTube-8M Classification Accuracy in Google Cloud Platform—0
An Empirical Study of Autoregressive Pre-training from Videos—0
An Investigation on Hardware-Aware Vision Transformer Scaling—0
Appending Adversarial Frames for Universal Video Attack—0
Approach for Video Classification with Multi-label on YouTube-8M Dataset—0
A spatiotemporal model with visual attention for video classification—0
A Spectral Nonlocal Block for Neural Networks—0
A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP—0
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection—0
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API—0
Attend and Interact: Higher-Order Object Interactions for Video Understanding—0
Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification—0
Attention-Aware Noisy Label Learning for Image Classification—0
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification—0
A Unified Method for First and Third Person Action Recognition—0
Automatic Concept Extraction for Concept Bottleneck-based Video Classification—0
AVD: Adversarial Video Distillation—0
AV-MaskEnhancer: Enhancing Video Representations through Audio-Visual Masked Autoencoder—0
AWSD: Adaptive Weighted Spatiotemporal Distillation for Video Representation—0
Benchmarking Edge AI Platforms for High-Performance ML Inference—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories—0
Beyond Transfer Learning: Co-finetuning for Action Localisation—0
Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers—0
BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models—0
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation—0
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization—0
Charades-Ego: A Large-Scale Dataset of Paired Third and First Person Videos—0
Classifying Video based on Automatic Content Detection Overview—0
Class Prototypes Based Contrastive Learning for Classifying Multi-Label and Fine-Grained Educational Videos—0
CM3T: Framework for Efficient Multimodal Learning for Inhomogeneous Interaction Datasets—0
Compound Memory Networks for Few-shot Video Classification—0
Context-Aware Detection of Mixed Critical Events using Video Classification—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Convolutional Drift Networks for Video Classification—0
Co-training Transformer with Videos and Images Improves Action Recognition—0
CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification—0
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification—0
DAiSEE: Towards User Engagement Recognition in the Wild—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