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 151200 of 455 papers

TitleStatusHype
Group NormalizationCode0
Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural NetworksCode0
Pushing the boundaries of event subsampling in event-based video classification using CNNsCode0
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation LearningCode0
Read My Ears! Horse Ear Movement Detection for Equine Affective State AssessmentCode0
Gated Channel Transformation for Visual RecognitionCode0
Adversarial Framing for Image and Video ClassificationCode0
VidModEx: Interpretable and Efficient Black Box Model Extraction for High-Dimensional SpacesCode0
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action RecognitionCode0
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under OcclusionsCode0
Approaches Toward Physical and General Video Anomaly DetectionCode0
Budgeted Training: Rethinking Deep Neural Network Training Under Resource ConstraintsCode0
Fine-grained Activity Recognition in Baseball VideosCode0
Multi-modality transrectal ultrasound video classification for identification of clinically significant prostate cancerCode0
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video ClassificationCode0
MU-Bench: A Multitask Multimodal Benchmark for Machine UnlearningCode0
BRIDLE: Generalized Self-supervised Learning with QuantizationCode0
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant AnalysisCode0
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)Code0
ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence AlignmentCode0
NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTubeCode0
Hallucinating Optical Flow Features for Video ClassificationCode0
Adversarial Perturbations Against Real-Time Video Classification SystemsCode0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Fast Non-Local Neural Networks with Spectral Residual LearningCode0
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation LearningCode0
FakeClaim: A Multiple Platform-driven Dataset for Identification of Fake News on 2023 Israel-Hamas WarCode0
Hierarchical Deep Recurrent Architecture for Video UnderstandingCode0
Appearance-and-Relation Networks for Video ClassificationCode0
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video ClassificationCode0
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric TransformationsCode0
Extending Information Bottleneck Attribution to Video SequencesCode0
MetaVD: A Meta Video Dataset for enhancing human action recognition datasetsCode0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
S4ND: Modeling Images and Videos as Multidimensional Signals Using State SpacesCode0
Exploring Temporal Information for Improved Video UnderstandingCode0
MLtuner: System Support for Automatic Machine Learning TuningCode0
Exploring Audio Cues for Enhanced Test-Time Video Model AdaptationCode0
Loss Switching Fusion with Similarity Search for Video ClassificationCode0
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in VideoCode0
Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference MetricsCode0
Long-term Leap Attention, Short-term Periodic Shift for Video ClassificationCode0
Malicious or Benign? Towards Effective Content Moderation for Children's VideosCode0
Learning Unseen Modality InteractionCode0
Beyond Short Snippets: Deep Networks for Video ClassificationCode0
Learn to cycle: Time-consistent feature discovery for action recognitionCode0
Encoding Video and Label Priors for Multi-label Video Classification on YouTube-8M datasetCode0
Efficient Video Classification Using Fewer FramesCode0
Learning Representations from EEG with Deep Recurrent-Convolutional Neural NetworksCode0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
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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