2016
DOI: 10.1007/978-3-319-46466-4_52
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MARS: A Video Benchmark for Large-Scale Person Re-Identification

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Cited by 777 publications
(736 citation statements)
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References 41 publications
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“…CNN can learn discriminative embeddings by itself without part-matching. Zheng et al [1], [8], [16] directly use a conventional finetuning approach on Market1501 [3], PRW [8] and MARS [16] and outperform many recent results. Wu et al [17] combine CNN embeddings with the hand-crafted features in the FC layer.…”
Section: B Identification Modelsmentioning
confidence: 99%
See 1 more Smart Citation
“…CNN can learn discriminative embeddings by itself without part-matching. Zheng et al [1], [8], [16] directly use a conventional finetuning approach on Market1501 [3], PRW [8] and MARS [16] and outperform many recent results. Wu et al [17] combine CNN embeddings with the hand-crafted features in the FC layer.…”
Section: B Identification Modelsmentioning
confidence: 99%
“…The high-level feature from the fine-tuned CNN has shown a discriminative ability [8], [16] and it is more compact than the activations in the intermediate layers. So in our model, …”
Section: Verification Lossmentioning
confidence: 99%
“…Ahmed et al [15] presented a deep convolutional architecture that captured local relationships between person images based on mid-level features. Generally, deep learning is usually utilized to learn feature representations by using deep convolutional features [14][15][16][17] or from the fully connected features [18][19][20] in person re-identification works.…”
Section: Related Workmentioning
confidence: 99%
“…(i) Feature construction and learning aim at designing or studying discriminative appearance descriptions [8][9][10][11][12][13][14][15][16][17][18][19][20] that are robust for distinguishing different pedestrians across arbitrary cameras. However, handcrafted feature construction is extremely challenging due to miscellaneous and complicated variations.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, assuming the availability of multiple shots of a target person available, multi-shot re-ID also attracted the interests of many researchers [50,51,37]. Furthermore, by extending multiple shots to a short video clip, Wang et al [52] started the work on video-based re-ID and drew a lot attentions from other researchers [53,54,55,56]. Around the same time, several unsupervised re-ID methods [57,58,59,60] were proposed to tackle the challenge of ground truth labeling for person re-ID.…”
mentioning
confidence: 99%