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 201–250 of 455 papers

TitleStatusHype
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
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models—0
Multi-Modal Video Feature Extraction for Popularity Prediction—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
Multiresolution Match Kernels for Gesture Video Classification—0
Multiview Hessian regularized logistic regression for action recognition—0
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression—0
Neural architecture impact on identifying temporally extended Reinforcement Learning tasks—0
Neural Networks for irregularly observed continuous-time Stochastic Processes—0
Non-local NetVLAD Encoding for Video Classification—0
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition—0
On Flow Profile Image for Video Representation—0
Contrastive Self-Supervised Learning Leads to Higher Adversarial Susceptibility—0
Online Meta-learning for AutoML in Real-time (OnMAR)—0
Understanding and Training Deep Diagonal Circulant Neural Networks—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition—0
Open Vocabulary Multi-Label Video Classification—0
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering—0
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators—0
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks—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