Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

• URL: http://arxiv.org/abs/2310.01129v1
• Date: Mon, 2 Oct 2023 12:03:05 GMT
• Title: Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
• Authors: Eurico Almeida, Bruno Silva, Jorge Batista
• Abstract summary: This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID) We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability.
• Score: 1.415880606410688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-ID task without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the https://github.com/videturfortuna/vehicle_reid_itsc2023.

Related papers

• Any Image Restoration with Efficient Automatic Degradation Adaptation [132.81912195537433]
  We propose a unified manner to achieve joint embedding by leveraging the inherent similarities across various degradations for efficient and comprehensive restoration. Our network sets new SOTA records while reducing model complexity by approximately -82% in trainable parameters and -85% in FLOPs.
  arXiv  Detail & Related papers  (2024-07-18T10:26:53Z)
• Robust Ensemble Person Re-Identification via Orthogonal Fusion with Occlusion Handling [4.431087385310259]
  Occlusion remains one of the major challenges in person reidentification (ReID) We propose a deep ensemble model that harnesses both CNN and Transformer architectures to generate robust feature representations.
  arXiv  Detail & Related papers  (2024-03-29T18:38:59Z)
• AaP-ReID: Improved Attention-Aware Person Re-identification [2.5761958263376745]
  AaP-ReID is a more effective method for person ReID that incorporates channel-wise attention into a ResNet-based architecture. Our method incorporates the Channel-Wise Attention Bottleneck block and can learn discriminating features by dynamically adjusting the importance ofeach channel in the feature maps.
  arXiv  Detail & Related papers  (2023-09-27T16:54:38Z)
• Dynamic Enhancement Network for Partial Multi-modality Person Re-identification [52.70235136651996]
  We design a novel dynamic enhancement network (DENet), which allows missing arbitrary modalities while maintaining the representation ability of multiple modalities. Since the missing state might be changeable, we design a dynamic enhancement module, which dynamically enhances modality features according to the missing state in an adaptive manner.
  arXiv  Detail & Related papers  (2023-05-25T06:22:01Z)
• Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification [27.75907274034702]
  We propose a novel deep learning framework named Progressive Modality-shared Transformer (PMT) for effective VI-ReID. To reduce the negative effect of modality gaps, we first take the gray-scale images as an auxiliary modality and propose a progressive learning strategy. To cope with the problem of large intra-class differences and small inter-class differences, we propose a Discriminative Center Loss.
  arXiv  Detail & Related papers  (2022-12-01T02:20:16Z)
• Deep Model Reassembly [60.6531819328247]
  We explore a novel knowledge-transfer task, termed as Deep Model Reassembly (DeRy) The goal of DeRy is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks. We demonstrate that on ImageNet, the best reassemble model achieves 78.6% top-1 accuracy without fine-tuning.
  arXiv  Detail & Related papers  (2022-10-24T10:16:13Z)
• Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search [65.51181219410763]
  One-step methods have been developed to handle pedestrian detection and identification sub-tasks using a single network. There are two major challenges in the current one-step approaches. We propose a decoupled and memory-reinforced network (DMRNet) to overcome these problems.
  arXiv  Detail & Related papers  (2021-02-22T06:19:45Z)
• Lightweight Multi-Branch Network for Person Re-Identification [6.353193172884524]
  This paper proposes a lightweight network that combines global, part-based, and channel features in a unified multi-branch architecture that builds on the resource-efficient OSNet backbone. Using a well-founded combination of training techniques and design choices, our final model achieves state-of-the-art results on CUHK03 labeled, CUHK03 detected, and Market-1501 with 85.1% mAP / 87.2% rank1, 82.4% mAP / 84.9% rank1, and 91.5% mAP / 96.3% rank1, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:28:46Z)
• ResNeSt: Split-Attention Networks [86.25490825631763]
  We present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification.
  arXiv  Detail & Related papers  (2020-04-19T20:40:31Z)
• When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks [57.0502745301132]
  We propose Micro-Dense Nets, a novel architecture with global residual learning and local micro-dense aggregations. Our micro-dense block can be integrated with neural architecture search based models to boost their performance.
  arXiv  Detail & Related papers  (2020-04-19T08:34:52Z)

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.