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 101150 of 455 papers

TitleStatusHype
Non-local Neural NetworksCode1
Movie Genre Classification by Language Augmentation and Shot SamplingCode0
Analysis and Extensions of Adversarial Training for Video ClassificationCode0
ReSpike: Residual Frames-based Hybrid Spiking Neural Networks for Efficient Action RecognitionCode0
A Multimodal Handover Failure Detection Dataset and BaselinesCode0
Representation Flow for Action RecognitionCode0
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural NetworksCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Read My Ears! Horse Ear Movement Detection for Equine Affective State AssessmentCode0
Differentiable Resolution Compression and Alignment for Efficient Video Classification and RetrievalCode0
DejaVid: Encoder-Agnostic Learned Temporal Matching for Video ClassificationCode0
Attention Clusters: Purely Attention Based Local Feature Integration for Video ClassificationCode0
Attention Bottlenecks for Multimodal FusionCode0
Deep RNN Framework for Visual Sequential ApplicationsCode0
Deep Multimodality Learning for UAV Video Aesthetic Quality AssessmentCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Distributed Intelligent Video Surveillance for Early Armed Robbery Detection based on Deep LearningCode0
Augmenting Ego-Vehicle for Traffic Near-Miss and Accident Classification Dataset using Manipulating Conditional Style TranslationCode0
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge ReportCode0
NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTubeCode0
Deep Learning for Video Classification and CaptioningCode0
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video ClassificationCode0
ActivityNet: A Large-Scale Video Benchmark for Human Activity UnderstandingCode0
ECO: Efficient Convolutional Network for Online Video UnderstandingCode0
Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancerCode0
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video ClassificationCode0
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016Code0
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under OcclusionsCode0
Pushing the boundaries of event subsampling in event-based video classification using CNNsCode0
Robust Real-Time Violence Detection in Video Using CNN And LSTMCode0
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
MLtuner: System Support for Automatic Machine Learning TuningCode0
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video ClassificationCode0
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video ArchitecturesCode0
Malicious or Benign? Towards Effective Content Moderation for Children's VideosCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
MetaVD: A Meta Video Dataset for enhancing human action recognition datasetsCode0
MU-Bench: A Multitask Multimodal Benchmark for Machine UnlearningCode0
Adversarial Perturbations Against Real-Time Video Classification SystemsCode0
Long-term Leap Attention, Short-term Periodic Shift for Video ClassificationCode0
Learn to cycle: Time-consistent feature discovery for action recognitionCode0
Loss Switching Fusion with Similarity Search for Video ClassificationCode0
Learning Spatio-Temporal Representation with Local and Global DiffusionCode0
Adversarial Framing for Image and Video ClassificationCode0
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action RecognitionCode0
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Learning Unseen Modality InteractionCode0
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant AnalysisCode0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
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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