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Scalable Person Re-identification: A Benchmark
TLDR
A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
TLDR
A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
Beyond Part Models: Person Retrieval with Refined Part Pooling
TLDR
This paper targets at learning discriminative part-informed features for person retrieval and lays emphasis on the content consistency within each part, resulting in refined parts with enhanced within-part consistency.
MARS: A Video Benchmark for Large-Scale Person Re-Identification
TLDR
It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
CenterNet: Keypoint Triplets for Object Detection
TLDR
This paper presents an efficient solution that explores the visual patterns within individual cropped regions with minimal costs, and builds the framework upon a representative one-stage keypoint-based detector named CornerNet, which improves both precision and recall.
Person Re-identification in the Wild
TLDR
A new dataset, PRW, is introduced to evaluate Person Re-identification in the Wild, and it is shown that pedestrian detection aids re-ID through two simple yet effective improvements: a cascaded fine-tuning strategy that trains a detection model first and then the classification model, and a Confidence Weighted Similarity (CWS) metric that incorporates detection scores into similarity measurement.
Deep Modular Co-Attention Networks for Visual Question Answering
TLDR
A deep Modular Co-Attention Network (MCAN) that consists of Modular co-attention layers cascaded in depth that significantly outperforms the previous state-of-the-art models and is quantitatively and qualitatively evaluated on the benchmark VQA-v2 dataset.
The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking
TLDR
This work constructs a new UAV benchmark focusing on complex scenarios with new level challenges, and is the first time to explore issues in unconstrained scenes comprehensively in UAV based real scenes.
Feature selection using principal feature analysis
TLDR
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 is proposed, and is called Principal Feature Analysis (PFA).
Pose-Driven Deep Convolutional Model for Person Re-identification
TLDR
A Pose-driven Deep Convolutional (PDC) model is proposed to learn improved feature extraction and matching models from end to end and explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
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