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 276300 of 455 papers

TitleStatusHype
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates0
Smoothed Gaussian Mixture Models for Video Classification and Recommendation0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models0
t-EVA: Time-Efficient t-SNE Video Annotation0
Deep Multimodality Learning for UAV Video Aesthetic Quality AssessmentCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Attention-Aware Noisy Label Learning for Image Classification0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations0
Defending Against Multiple and Unforeseen Adversarial Videos0
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation0
Self-Supervised Multi-Task Procedure Learning from Instructional Videos0
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge ReportCode0
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos0
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels0
NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTubeCode0
Learn to cycle: Time-consistent feature discovery for action recognitionCode0
Video Understanding as Machine Translation0
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators0
Video Contents Understanding using Deep Neural Networks0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition0
Revisiting Few-shot Activity Detection with Class Similarity Control0
VideoSSL: Semi-Supervised Learning for Video Classification0
Learning spatio-temporal representations with temporal squeeze pooling0
FSD-10: A Dataset for Competitive Sports Content Analysis0
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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