RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning

• URL: http://arxiv.org/abs/2209.08630v1
• Date: Sun, 18 Sep 2022 18:45:06 GMT
• Title: RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning
• Authors: Wei-Ting Chen, I-Hsiang Chen, Chih-Yuan Yeh, Hao-Hsiang Yang, Hua-En Chang, Jian-Jiun Ding, Sy-Yen Kuo
• Abstract summary: Vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. We construct a training paradigm called textbfRVSL which integrates ReID and domain transformation techniques. We show that the proposed method can achieve state-of-the-art performance on hazy vehicle ReID problems.
• Score: 24.13217601503959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. Several algorithms have been developed and obtained considerable success. However, most existing methods have unpleasant performance in the hazy scenario due to poor visibility. Though some strategies are possible to resolve this problem, they still have room to be improved due to the limited performance in real-world scenarios and the lack of real-world clear ground truth. Thus, to resolve this problem, inspired by CycleGAN, we construct a training paradigm called \textbf{RVSL} which integrates ReID and domain transformation techniques. The network is trained on semi-supervised fashion and does not require to employ the ID labels and the corresponding clear ground truths to learn hazy vehicle ReID mission in the real-world haze scenes. To further constrain the unsupervised learning process effectively, several losses are developed. Experimental results on synthetic and real-world datasets indicate that the proposed method can achieve state-of-the-art performance on hazy vehicle ReID problems. It is worth mentioning that although the proposed method is trained without real-world label information, it can achieve competitive performance compared to existing supervised methods trained on complete label information.

Related papers

• Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning [2.3558144417896583]
  The planning problem constitutes a fundamental aspect of the autonomous driving framework. We propose Validity Learning on Failures, VL(on failure) as a remedy to address this issue. We show that VL(on failure) outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2024-06-03T17:25:18Z)
• Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight [20.92646531472541]
  We propose a novel approach that combines the performance of Reinforcement Learning (RL) and the sample efficiency of Imitation Learning (IL) Our framework contains three phases: training a teacher policy using RL with privileged state information, distilling it into a student policy via IL, and adaptive fine-tuning via RL. testing in both simulated and real-world scenarios shows our approach can not only learn in scenarios where RL from scratch fails but also outperforms existing IL methods in both robustness and performance.
  arXiv  Detail & Related papers  (2024-03-18T19:25:57Z)
• Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning [4.195122359359966]
  Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.
  arXiv  Detail & Related papers  (2023-08-28T03:49:13Z)
• Practical Imitation Learning in the Real World via Task Consistency Loss [18.827979446629296]
  This paper introduces a self-supervised loss that encourages sim and real alignment both at the feature and action-prediction levels. We achieve 80% success across ten seen and unseen scenes using only 16.2 hours of teleoperated demonstrations in sim and real.
  arXiv  Detail & Related papers  (2022-02-03T21:43:06Z)
• Incremental Embedding Learning via Zero-Shot Translation [65.94349068508863]
  Current state-of-the-art incremental learning methods tackle catastrophic forgetting problem in traditional classification networks. We propose a novel class-incremental method for embedding network, named as zero-shot translation class-incremental method (ZSTCI) In addition, ZSTCI can easily be combined with existing regularization-based incremental learning methods to further improve performance of embedding networks.
  arXiv  Detail & Related papers  (2020-12-31T08:21:37Z)
• BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments [54.22535063244038]
  We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios with dense and heterogeneous traffic consisting of cars, trucks, two-and three-wheelers, and pedestrians.
  arXiv  Detail & Related papers  (2020-09-22T08:25:44Z)
• Deep Traffic Sign Detection and Recognition Without Target Domain Real Images [52.079665469286496]
  We propose a novel database generation method that requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
  arXiv  Detail & Related papers  (2020-07-30T21:06:47Z)
• RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real [74.45688231140689]
  We introduce the RL-scene consistency loss for image translation, which ensures that the translation operation is invariant with respect to the Q-values associated with the image. We obtain RL-CycleGAN, a new approach for simulation-to-real-world transfer for reinforcement learning.
  arXiv  Detail & Related papers  (2020-06-16T08:58:07Z)
• Syn2Real Transfer Learning for Image Deraining using Gaussian Processes [92.15895515035795]
  CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. Due to challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset.
  arXiv  Detail & Related papers  (2020-06-10T00:33:18Z)
• Auto-Rectify Network for Unsupervised Indoor Depth Estimation [119.82412041164372]
  We establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth. We propose a data pre-processing method that rectifies training images by removing their relative rotations for effective learning. Our results outperform the previous unsupervised SOTA method by a large margin on the challenging NYUv2 dataset.
  arXiv  Detail & Related papers  (2020-06-04T08:59:17Z)

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.