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 251–300 of 455 papers

TitleStatusHype
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing—0
Temporal Alignment Prediction for Few-Shot Video Classification—0
Fine-Grained AutoAugmentation for Multi-Label Classification—0
Aligning Correlation Information for Domain Adaptation in Action Recognition—0
Attention Bottlenecks for Multimodal FusionCode0
When Video Classification Meets Incremental Classes—0
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video ClassificationCode0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition—0
A Study On the Effects of Pre-processing On Spatio-temporal Action Recognition Using Spiking Neural Networks Trained with STDP—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture—0
VidTr: Video Transformer Without Convolutions—0
Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories—0
On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study—0
Classifying Video based on Automatic Content Detection Overview—0
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths—0
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNsCode0
Distribution Adaptive INT8 Quantization for Training CNNs—0
Privacy-Preserving Video Classification with Convolutional Neural Networks—0
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks—0
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking—0
Self-supervised Temporal Learning—0
CNNs for JPEGs: A Study in Computational Cost—0
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates—0
Smoothed Gaussian Mixture Models for Video Classification and Recommendation—0
Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models—0
t-EVA: Time-Efficient t-SNE Video Annotation—0
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 Classification—0
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation—0
Self-Supervised Multi-Task Procedure Learning from Instructional Videos—0
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge ReportCode0
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification—0
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos—0
3D CNN-PCA: A Deep-Learning-Based Parameterization for Complex Geomodels—0
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 Translation—0
Optimizing Temporal Convolutional Network inference on FPGA-based accelerators—0
Video Contents Understanding using Deep Neural Networks—0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition—0
Revisiting Few-shot Activity Detection with Class Similarity Control—0
VideoSSL: Semi-Supervised Learning for Video Classification—0
Learning spatio-temporal representations with temporal squeeze pooling—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
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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