SOTAVerified

Activity Recognition

Human Activity Recognition is the problem of identifying events performed by humans given a video input. It is formulated as a binary (or multiclass) classification problem of outputting activity class labels. Activity Recognition is an important problem with many societal applications including smart surveillance, video search/retrieval, intelligent robots, and other monitoring systems.

Source: Learning Latent Sub-events in Activity Videos Using Temporal Attention Filters

Papers

Showing 901910 of 1322 papers

TitleStatusHype
Learning and Verification of Task Structure in Instructional Videos0
Learning Attribute Representation for Human Activity Recognition0
Learning Ensembles of Potential Functions for Structured Prediction With Latent Variables0
Learning from Imbalanced Multiclass Sequential Data Streams Using Dynamically Weighted Conditional Random Fields0
M3Act: Learning from Synthetic Human Group Activities0
Learning from the Best: Contrastive Representations Learning Across Sensor Locations for Wearable Activity Recognition0
Generating Fair Universal Representations using Adversarial Models0
Learning Privately from Multiparty Data0
Learning-to-Learn Personalised Human Activity Recognition Models0
Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Structured Keypoint PoolingAccuracy93.4Unverified
2Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics datasetAccuracy90.4Unverified
3Human Skeletons + Change DetectionAccuracy90.25Unverified
4Separable Convolutional LSTMAccuracy89.75Unverified
5SPIL ConvolutionAccuracy89.3Unverified
6Flow Gated NetworkAccuracy87.25Unverified
#ModelMetricClaimedVerifiedStatus
1FocusCLIPTop-3 Accuracy (%)10.47Unverified
2CLIPTop-3 Accuracy (%)6.49Unverified
#ModelMetricClaimedVerifiedStatus
1Boutaleb et al.1:1 Accuracy97.91Unverified
#ModelMetricClaimedVerifiedStatus
1all-landmark-modelActivity Recognition0.76Unverified