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

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Showing 401–450 of 455 papers

TitleStatusHype
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
On the Surprising Effectiveness of Transformers in Low-Labeled Video Recognition—0
Open Vocabulary Multi-Label Video Classification—0
Optimizing GPT for Video Understanding: Zero-Shot Performance and Prompt Engineering—0
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network—0
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks—0
Overview of Tencent Multi-modal Ads Video Understanding Challenge—0
Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering—0
Pig aggression classification using CNN, Transformers and Recurrent Networks—0
PrAViC: Probabilistic Adaptation Framework for Real-Time Video Classification—0
Predicting Driver Self-Reported Stress by Analyzing the Road Scene—0
PreViTS: Contrastive Pretraining with Video Tracking Supervision—0
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Recent Trends in 2D Object Detection and Applications in Video Event Recognition—0
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation—0
Multi-Task Learning of Generalizable Representations for Video Action Recognition—0
Revisiting Few-shot Activity Detection with Class Similarity Control—0
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding—0
Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification—0
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning—0
S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces—0
Saliency-guided video classification via adaptively weighted learning—0
Selective Structured State-Spaces for Long-Form Video Understanding—0
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks—0
Self-Supervised Multi-Task Procedure Learning from Instructional Videos—0
Self-supervised Temporal Learning—0
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification—0
Semi-supervised and Deep learning Frameworks for Video Classification and Key-frame Identification—0
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model—0
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking—0
Smoothed Gaussian Mixture Models for Video Classification and Recommendation—0
SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition—0
Sparse Coding and Dictionary Learning With Linear Dynamical Systems—0
Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification—0
Spatio-Temporal Fusion Networks for Action Recognition—0
Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis—0
Spectral Nonlocal Block for Neural Network—0
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition—0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition—0
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch—0
Technical Report: Disentangled Action Parsing Networks for Accurate Part-level Action Parsing—0
Temporal Alignment Prediction for Few-Shot Video Classification—0
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates—0
Temporal Coherent Test-Time Optimization for Robust Video Classification—0
TenAd: A Tensor-based Low-rank Black Box Adversarial Attack for Video Classification—0
t-EVA: Time-Efficient t-SNE Video Annotation—0
P2ANet: A Dataset and Benchmark for Dense Action Detection from Table Tennis Match Broadcasting Videos—0
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All datasetsBreakfastCOINMoBYouTube-8MHockey Fight Detection DatasetCharadesHome Action GenomeKineticsMultimodal PISASomething-Something V1Something-Something V2SRI-APPROVE Fine-Grained Video Classification

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)95.2—Unverified
2MA-LMMAccuracy (%)93—Unverified
3S5Accuracy (%)90.7—Unverified
4TranS4merAccuracy (%)90.27—Unverified
5D-Sprv.Accuracy (%)89.9—Unverified
6ViS4merAccuracy (%)88.2—Unverified
7GHRMAccuracy (%)75.5—Unverified
8TimeceptionAccuracy (%)71.3—Unverified
9VideoGraphAccuracy (%)69.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HERMESAccuracy (%)93.5—Unverified
2MA-LMMAccuracy (%)93.2—Unverified
3S5Accuracy (%)90.8—Unverified
4D-Sprv.Accuracy (%)90—Unverified
5TranS4merAccuracy (%)89.3—Unverified
6ViS4merAccuracy (%)88.4—Unverified
7TSNAccuracy (%)73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1VTNAccuracy77.85—Unverified
2I3DAccuracy72.11—Unverified
3ConvLSTMAccuracy69.71—Unverified
#ModelMetricClaimedVerifiedStatus
1DCGN (self-attention graph pooling)Hit@187.7—Unverified
2Hierarchical LSTM with MoEHit@186.8—Unverified
3Mixture-of-2-ExpertsHit@170.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy99.5—Unverified
2CNN+LSTM1:1 Accuracy98—Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridmAP38.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Cooperative Ours (3rd-person)Accuracy (%)24.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MultigridTop-177.6—Unverified
#ModelMetricClaimedVerifiedStatus
1VideoAccuracy (%)73.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy84—Unverified
#ModelMetricClaimedVerifiedStatus
1MSNet-R50En (ours)Top-5 Accuracy91—Unverified
#ModelMetricClaimedVerifiedStatus
1Multi-Label Prototypes Contrastive LearningAUPR88.4—Unverified