Triplet Online Instance Matching Loss for Person Re-identification

• URL: http://arxiv.org/abs/2002.10560v1
• Date: Mon, 24 Feb 2020 21:55:56 GMT
• Title: Triplet Online Instance Matching Loss for Person Re-identification
• Authors: Ye Li, Guangqiang Yin, Chunhui Liu, Xiaoyu Yang, Zhiguo Wang
• Abstract summary: We propose a Triplet Online Instance Matching (TOIM) loss function, which lays emphasis on the hard samples and improves the accuracy of person ReID effectively. It combines the advantages of OIM loss and Triplet loss and simplifies the process of batch construction. It can be trained on-line when handle the joint detection and identification task.
• Score: 14.233828198522266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mining the shared features of same identity in different scene, and the unique features of different identity in same scene, are most significant challenges in the field of person re-identification (ReID). Online Instance Matching (OIM) loss function and Triplet loss function are main methods for person ReID. Unfortunately, both of them have drawbacks. OIM loss treats all samples equally and puts no emphasis on hard samples. Triplet loss processes batch construction in a complicated and fussy way and converges slowly. For these problems, we propose a Triplet Online Instance Matching (TOIM) loss function, which lays emphasis on the hard samples and improves the accuracy of person ReID effectively. It combines the advantages of OIM loss and Triplet loss and simplifies the process of batch construction, which leads to a more rapid convergence. It can be trained on-line when handle the joint detection and identification task. To validate our loss function, we collect and annotate a large-scale benchmark dataset (UESTC-PR) based on images taken from surveillance cameras, which contains 499 identities and 60,437 images. We evaluated our proposed loss function on Duke, Marker-1501 and UESTC-PR using ResNet-50, and the result shows that our proposed loss function outperforms the baseline methods by a maximum of 21.7%, including Softmax loss, OIM loss and Triplet loss.

Related papers

• Class Anchor Margin Loss for Content-Based Image Retrieval [97.81742911657497]
  We propose a novel repeller-attractor loss that falls in the metric learning paradigm, yet directly optimize for the L2 metric without the need of generating pairs. We evaluate the proposed objective in the context of few-shot and full-set training on the CBIR task, by using both convolutional and transformer architectures.
  arXiv  Detail & Related papers  (2023-06-01T12:53:10Z)
• Adaptive Sparse Pairwise Loss for Object Re-Identification [25.515107212575636]
  Pairwise losses play an important role in training a strong ReID network. We propose a novel loss paradigm termed Sparse Pairwise (SP) loss. We show that SP loss and its adaptive variant AdaSP loss outperform other pairwise losses.
  arXiv  Detail & Related papers  (2023-03-31T17:59:44Z)
• Learning Feature Recovery Transformer for Occluded Person Re-identification [71.18476220969647]
  We propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its $k$-nearest neighbors in the gallery to recover the complete features.
  arXiv  Detail & Related papers  (2023-01-05T02:36:16Z)
• Unified Loss of Pair Similarity Optimization for Vision-Language Retrieval [35.141916376979836]
  There are two popular loss functions used for vision-language retrieval, i.e., triplet loss and contrastive learning loss. This paper proposes a unified loss of pair similarity optimization for vision-language retrieval.
  arXiv  Detail & Related papers  (2022-09-28T07:01:22Z)
• Do Lessons from Metric Learning Generalize to Image-Caption Retrieval? [67.45267657995748]
  The triplet loss with semi-hard negatives has become the de facto choice for image-caption retrieval (ICR) methods that are optimized from scratch. Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning. We ask whether these findings generalize to the setting of ICR by comparing three loss functions on two ICR methods.
  arXiv  Detail & Related papers  (2022-02-14T15:18:00Z)
• A Decidability-Based Loss Function [2.5919311269669003]
  Biometric problems often use deep learning models to extract features from images, also known as embeddings. In this work, a loss function based on the decidability index is proposed to improve the quality of embeddings for the verification routine. The proposed approach is compared against the Softmax (cross-entropy), Triplets Soft-Hard, and the Multi Similarity losses in four different benchmarks.
  arXiv  Detail & Related papers  (2021-09-12T14:26:27Z)
• Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification [33.23261883419256]
  Batch Hard Triplet loss is widely used in person re-identification tasks, but it does not perform well in the Visible-Infrared person re-identification task. We propose a batch all triplet selection strategy, which selects all the possible triplets among samples to optimize instead of the hardest triplet. We also introduce Unified Batch All Triplet loss and Cosine Softmax loss to collaboratively optimize the cosine distance between image vectors.
  arXiv  Detail & Related papers  (2021-03-08T08:58:52Z)
• A Unified Framework of Surrogate Loss by Refactoring and Interpolation [65.60014616444623]
  We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent. We validate the effectiveness of UniLoss on three tasks and four datasets.
  arXiv  Detail & Related papers  (2020-07-27T21:16:51Z)
• Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification [51.110453988705395]
  Video-based person re-identification (Re-ID) is an important computer vision task. We introduce a new metric learning method called Attribute-aware Identity-hard Triplet Loss (AITL) To achieve a complete model of video-based person Re-ID, a multi-task framework with Attribute-driven Spatio-Temporal Attention (ASTA) mechanism is also proposed.
  arXiv  Detail & Related papers  (2020-06-13T09:15:38Z)
• Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification [118.72423376789062]
  We introduce a multi-class classification loss, i.e., N-tuple loss, to jointly consider multiple (N) instances for per-query optimization. With the multi-class classification incorporated, our model achieves the state-of-the-art performance on the benchmark person ReID datasets.
  arXiv  Detail & Related papers  (2020-06-08T23:34:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.