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Scalable Person Re-identification: A Benchmark
TLDR
This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Expand
Beyond Part Models: Person Retrieval with Refined Part Pooling
TLDR
We propose a network named Part-based Convolutional Baseline (PCB) which conducts uniform partition on the conv-layer for learning part-level features. Expand
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
TLDR
We present a new dataset called MSMT171 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. Expand
MARS: A Video Benchmark for Large-Scale Person Re-Identification
TLDR
We introduce a new video re-id dataset, named Motion Analysis and Re-identification Set (MARS), a video extension of the Market-1501 dataset. Expand
Person Re-identification in the Wild
TLDR
This paper presents a novel large-scale dataset and comprehensive baselines for end-to-end pedestrian detection and person recognition in raw video frames. Expand
CenterNet: Keypoint Triplets for Object Detection
TLDR
In object detection, keypoint-based approaches often experience the drawback of a large number of incorrect object bounding boxes, arguably due the lack of an additional assessment inside cropped regions. Expand
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
TLDR
We construct a large scale challenging UAV benchmark focusing on complex scenarios with new level challenges. Expand
Feature selection using principal feature analysis
TLDR
This paper proposes a novel method for dimensionality reduction of a feature set by choosing a subset of the original features that contains most of the essential information, using the same criteria as PCA. Expand
Deep Modular Co-Attention Networks for Visual Question Answering
TLDR
We propose a deep Modular Co-Attention Network (MCAN) that consists of MCA layers cascaded in depth. Expand
Pose-Driven Deep Convolutional Model for Person Re-identification
TLDR
We propose a Pose-driven Deep Convolutional (PDC) model to learn improved feature extraction and matching models from end to end. Expand
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