Pedestrian attribution recognition is the task of recognising pedestrian features - such as whether they are talking on a phone, whether they have a backpack, and so on.
( Image credit: HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis )
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Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Ranked #2 on Pedestrian Attribute Recognition on UAV-Human
DOMAIN GENERALIZATION FINE-GRAINED IMAGE CLASSIFICATION IMAGE-TO-IMAGE TRANSLATION OBJECT DETECTION PEDESTRIAN ATTRIBUTE RECOGNITION PEDESTRIAN TRAJECTORY PREDICTION PERSON RE-IDENTIFICATION RETINAL OCT DISEASE CLASSIFICATION SEMANTIC SEGMENTATION
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
Ranked #1 on Pedestrian Attribute Recognition on UAV-Human
Pedestrian analysis plays a vital role in intelligent video surveillance and is a key component for security-centric computer vision systems.
Ranked #2 on Pedestrian Attribute Recognition on PETA
To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute.
Ranked #1 on Pedestrian Attribute Recognition on PETA
Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models.
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.
Ranked #1 on Chinese Named Entity Recognition on MSRA (0..5sec metric)
3D FACE RECONSTRUCTION 3D POINT CLOUD CLASSIFICATION ANOMALY DETECTION ASPECT-BASED SENTIMENT ANALYSIS CHINESE NAMED ENTITY RECOGNITION CROSS-MODAL RETRIEVAL DOCUMENT IMAGE CLASSIFICATION DRUG DISCOVERY ENTITY LINKING FACIAL EXPRESSION RECOGNITION FEDERATED LEARNING FEW-SHOT IMAGE CLASSIFICATION HAND POSE ESTIMATION IMAGE DENOISING MACHINE TRANSLATION MEDICAL IMAGE SEGMENTATION MULTI-LABEL CLASSIFICATION MULTIVIEW GAIT RECOGNITION PAIN INTENSITY REGRESSION PEDESTRIAN ATTRIBUTE RECOGNITION PERSON RE-IDENTIFICATION POINT CLOUD SUPER RESOLUTION SELF-SUPERVISED ACTION RECOGNITION SELF-SUPERVISED VIDEO RETRIEVAL SEMANTIC SEGMENTATION SEMI-SUPERVISED VIDEO OBJECT SEGMENTATION SEQUENTIAL IMAGE CLASSIFICATION SINGLE IMAGE DERAINING SLOT FILLING SPEECH ENHANCEMENT SPEECH RECOGNITION SPOKEN LANGUAGE IDENTIFICATION TRAFFIC PREDICTION TRAJECTORY PREDICTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED VIDEO OBJECT SEGMENTATION VIDEO SALIENCY DETECTION VIDEO SEMANTIC SEGMENTATION VIDEO SUMMARIZATION VISUAL OBJECT TRACKING WEAKLY SUPERVISED OBJECT DETECTION ZERO-SHOT TRANSFER IMAGE CLASSIFICATION NETWORKING AND INTERNET ARCHITECTURE
RAP has in total 41, 585 pedestrian samples, each of which is annotated with 72 attributes as well as viewpoints, occlusions, body parts information.