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

Showing 201225 of 455 papers

TitleStatusHype
Large-scale Video Classification guided by Batch Normalized LSTM Translator0
Non-local NetVLAD Encoding for Video Classification0
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling0
Large-Scale YouTube-8M Video Understanding with Deep Neural Networks0
FSD-10: A Dataset for Competitive Sports Content Analysis0
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API0
Deep Motion Features for Visual Tracking0
Fine-grained Video Categorization with Redundancy Reduction Attention0
Learning Correlation Structures for Vision Transformers0
Learning Expressive And Generalizable Motion Features For Face Forgery Detection0
Calibrating Class Weights with Multi-Modal Information for Partial Video Domain Adaptation0
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification0
Fine-Grained AutoAugmentation for Multi-Label Classification0
Attention-Aware Noisy Label Learning for Image Classification0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression0
Learning spatio-temporal representations with temporal squeeze pooling0
Multi-Agent Reinforcement Learning Based Frame Sampling for Effective Untrimmed Video Recognition0
Deep-Temporal LSTM for Daily Living Action Recognition0
Few-Shot Video Classification via Temporal Alignment0
Few-Shot Video Classification via Representation Fusion and Promotion Learning0
Defending Against Multiple and Unforeseen Adversarial Videos0
Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification0
Appending Adversarial Frames for Universal Video Attack0
Fast Video Classification via Adaptive Cascading of Deep Models0
MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)95.2Unverified
2MA-LMMAccuracy (%)93Unverified
3S5Accuracy (%)90.7Unverified
4TranS4merAccuracy (%)90.27Unverified
5D-Sprv.Accuracy (%)89.9Unverified
6ViS4merAccuracy (%)88.2Unverified
7GHRMAccuracy (%)75.5Unverified
8TimeceptionAccuracy (%)71.3Unverified
9VideoGraphAccuracy (%)69.5Unverified
#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)93.5Unverified
2MA-LMMAccuracy (%)93.2Unverified
3S5Accuracy (%)90.8Unverified
4D-Sprv.Accuracy (%)90Unverified
5TranS4merAccuracy (%)89.3Unverified
6ViS4merAccuracy (%)88.4Unverified
7TSNAccuracy (%)73.4Unverified
#ModelMetricClaimedVerifiedStatus
1VTNAccuracy77.85Unverified
2I3DAccuracy72.11Unverified
3ConvLSTMAccuracy69.71Unverified
#ModelMetricClaimedVerifiedStatus
1DCGN (self-attention graph pooling)Hit@187.7Unverified
2Hierarchical LSTM with MoEHit@186.8Unverified
3Mixture-of-2-ExpertsHit@170.1Unverified
#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy99.5Unverified
2CNN+LSTM1:1 Accuracy98Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridmAP38.2Unverified
#ModelMetricClaimedVerifiedStatus
1Cooperative Ours (3rd-person)Accuracy (%)24.7Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridTop-177.6Unverified
#ModelMetricClaimedVerifiedStatus
1VideoAccuracy (%)73.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy84Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy91Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Label Prototypes Contrastive LearningAUPR88.4Unverified