Query-adaptive late fusion for image search and person re-identification

By Liang Zheng, Shengjin Wang, Lu Tian, Ziqiong Liu, Qi Tian. Computer Vision and Pattern Recognition, 2015

316 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c651c882ca20c6c9a8ddb38db3e7dc01c2c29fcd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yi Yang
2. Liang Zheng
3. Luc Van Gool
4. Dacheng Tao
5. Qi Tian
6. Ling Shao
7. Ming-Hsuan Yang
8. Xiaogang Wang

In Media Technology

1. Liang Zheng
2. Qi Tian
3. Yi Yang
4. Jingdong Wang
5. Lingxi Xie
6. Ming-Hsuan Yang
7. Steven C. H. Hoi
8. Ling Shao

Citations in the corpus

• Deep Attributes Driven Multi-Camera Person Re-identification (2016, background): “represent the visual appearance of a person [1,2,3,4,5,6,7] and b) learning a discriminative distance metric hence the distance of features from the same person can be smaller [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25].”
• Deep Representation Learning With Part Loss for Person Re-Identification (2017, background): “Existing approaches conquer this challenge by either seeking discriminative metrics [49, 21, 59, 24, 28, 3, 31, 46, 25, 59, 5, 32, 50], or generating discriminative features [30, 47, 10, 7, 26, 53, 57, 20, 61].”
• Pose-Driven Deep Convolutional Model for Person Re-identification (2017, background): “them [11, 6, 33, 29, 60, 51, 64]; (b) closing the gap between a person’s different features by learning a discriminative distance metric [32, 9, 17, 36, 55, 23, 54, 30, 26, 65, 50, 3, 27, 4, 39, 28, 10, 37, 59].”
• Gated Siamese Convolutional Neural Network Architecture for Human Re-identification (2016, unknown): “QAF [59] 30.”
• Incorporating visual adjectives for image classification (2016, methodology): “In [38], an adaptive fusion strategy is designed to compute the weight on each component automatically.”
• A Siamese Long Short-Term Memory Architecture for Human Re-identification (2016, unknown): “QAF [67] 30.”
• Dynamic Match Kernel With Deep Convolutional Features for Image Retrieval (2018, methodology): “Following the previous literature [1], [53], for the holidays dataset, we merged it with MIR Flickr 1M (1 million) images [25], so that the size of the final dataset is 1, 001, 491.”
• Blind late fusion in multimedia event retrieval (2016, methodology): “[36] propose late fusion methods based on score distributions.”
• Part-Aligned Bilinear Representations for Person Re-identification (2018, background): “Recently, attributes [33–35], transfer learning [36, 37], re-ranking [38, 39], partial person matching [40], and human-in-the-loop learning [41,42], have also been studied.”
• Good Practice in CNN Feature Transfer (2016, methodology): “For image search, we combine multi-layer features by Query Adaptive Fusion (QAF) [48] on the score level.”
• Person Re-identification: Past, Present and Future (2016, methodology): “Zheng et al. [167] propose a query-adaptive fusion method to combine rank results of several re-ID systems.”
• Re-ranking Person Re-identification with k-Reciprocal Encoding (2017, background): “Recently, several researchers [11, 34, 29, 25, 51, 21, 12, 20, 44, 46] have paid attention to re-ranking based method in the re-ID community.”
• Person Re-identification in the Wild (2016, methodology): “In fact, re-ranking methods [2,46,51] will be critical for scalable re-ID.”
• MARS: A Video Benchmark for Large-Scale Person Re-Identification (2016, methodology): “Finally, the multiplequery and multiple-ground truth mode will enable future research in fields such as query re-formulation and search re-ranking [45].”
• Scalable Person Re-identification on Supervised Smoothed Manifold (2017, background): “Current research interests can be coarsely divided into two mainstreams: 1) those focus on designing robust visual descriptors [55, 13, 37, 26, 64] to accurately model the appearance of person; 2) those seek for a discriminative metric [65, 25, 54, 28, 18], under which instances of the same identity”
• Unsupervised Person Re-identification (2017, methodology): “In our experiment, for each PUL iteration, we use all the samples as training data for CNN fine-tuning.”