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 125 of 455 papers

TitleStatusHype
Perception Encoder: The best visual embeddings are not at the output of the networkCode8
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and ResolutionCode6
A Survey on Visual MambaCode4
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video UnderstandingCode3
Gramian Multimodal Representation Learning and AlignmentCode2
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo BenchmarkCode2
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learningCode2
Revisiting Classifier: Transferring Vision-Language Models for Video RecognitionCode2
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEsCode2
UniFormer: Unifying Convolution and Self-attention for Visual RecognitionCode2
Video Swin TransformerCode2
Is Space-Time Attention All You Need for Video Understanding?Code2
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?Code2
X3D: Expanding Architectures for Efficient Video RecognitionCode2
Temporal Segment Networks for Action Recognition in VideosCode2
Video-GPT via Next Clip DiffusionCode1
When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysisCode1
HERMES: temporal-coHERent long-forM understanding with Episodes and SemanticsCode1
Learning Video Context as Interleaved Multimodal SequencesCode1
MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and BilibiliCode1
X-MIC: Cross-Modal Instance Conditioning for Egocentric Action GeneralizationCode1
Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based ApproachCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
A Simple Video Segmenter by Tracking Objects Along Axial TrajectoriesCode1
Quantized Distillation: Optimizing Driver Activity Recognition Models for Resource-Constrained EnvironmentsCode1
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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