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 351–400 of 455 papers

TitleStatusHype
Higher-order Network for Action Recognition—0
Identifying and Resisting Adversarial Videos Using Temporal Consistency—0
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames—0
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning—0
Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning—0
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture—0
iqiyi Submission to ActivityNet Challenge 2019 Kinetics-700 challenge: Hierarchical Group-wise Attention—0
Isometric Transformation Invariant Graph-based Deep Neural Network—0
Label Denoising with Large Ensembles of Heterogeneous Neural Networks—0
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision—0
Language as the Medium: Multimodal Video Classification through text only—0
Large-scale Video Classification guided by Batch Normalized LSTM Translator—0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling—0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks—0
Learning Correlation Structures for Vision Transformers—0
Learning Expressive And Generalizable Motion Features For Face Forgery Detection—0
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification—0
Learning Natural Consistency Representation for Face Forgery Video Detection—0
Learning Representative Temporal Features for Action Recognition—0
Learning spatio-temporal representations with temporal squeeze pooling—0
Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification—0
Linear Video Transformer with Feature Fixation—0
Long-short Term Motion Feature for Action Classification and Retrieval—0
LookupViT: Compressing visual information to a limited number of tokens—0
Towards Train-Test Consistency for Semi-supervised Temporal Action Localization—0
MANIFOLDNET: A DEEP NEURAL NETWORK FOR MANIFOLD-VALUED DATA—0
Metric-Based Few-Shot Learning for Video Action Recognition—0
Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification—0
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification—0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language—0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Multi-label Video Classification for Underwater Ship Inspection—0
Multi-modal Aggregation for Video Classification—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
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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