1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track

• URL: http://arxiv.org/abs/2301.04795v1
• Date: Thu, 12 Jan 2023 03:44:30 GMT
• Title: 1st Place Solution for ECCV 2022 OOD-CV Challenge Image Classification Track
• Authors: Yilu Guo, Xingyue Shi, Weijie Chen, Shicai Yang, Di Xie, Shiliang Pu, Yueting Zhuang
• Abstract summary: OOD-CV challenge is an out-of-distribution generalization task. In this challenge, our core solution can be summarized as that Noisy Label Learning Is A Strong Test-Time Domain Adaptation method. After integrating Test-Time Augmentation and Model Ensemble strategies, our solution ranks the first place on the Image Classification Leaderboard of the OOD-CV Challenge.
• Score: 64.49153847504141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: OOD-CV challenge is an out-of-distribution generalization task. In this challenge, our core solution can be summarized as that Noisy Label Learning Is A Strong Test-Time Domain Adaptation Optimizer. Briefly speaking, our main pipeline can be divided into two stages, a pre-training stage for domain generalization and a test-time training stage for domain adaptation. We only exploit labeled source data in the pre-training stage and only exploit unlabeled target data in the test-time training stage. In the pre-training stage, we propose a simple yet effective Mask-Level Copy-Paste data augmentation strategy to enhance out-of-distribution generalization ability so as to resist shape, pose, context, texture, occlusion, and weather domain shifts in this challenge. In the test-time training stage, we use the pre-trained model to assign noisy label for the unlabeled target data, and propose a Label-Periodically-Updated DivideMix method for noisy label learning. After integrating Test-Time Augmentation and Model Ensemble strategies, our solution ranks the first place on the Image Classification Leaderboard of the OOD-CV Challenge. Code will be released in https://github.com/hikvision-research/OOD-CV.

Related papers

• Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification [34.37262622415682]
  We propose a new adaptation framework called Data Adaptive Traceback. Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data. We adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning.
  arXiv  Detail & Related papers  (2024-07-11T18:01:58Z)
• Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition [72.35438297011176]
  We propose a novel method to realize seamless adaptation of pre-trained models for visual place recognition (VPR) Specifically, to obtain both global and local features that focus on salient landmarks for discriminating places, we design a hybrid adaptation method. Experimental results show that our method outperforms the state-of-the-art methods with less training data and training time.
  arXiv  Detail & Related papers  (2024-02-22T12:55:01Z)
• Test-Time Training for Semantic Segmentation with Output Contrastive Loss [12.535720010867538]
  Deep learning-based segmentation models have achieved impressive performance on public benchmarks, but generalizing well to unseen environments remains a major challenge. This paper introduces Contrastive Loss (OCL), known for its capability to learn robust and generalized representations, to stabilize the adaptation process. Our method excels even when applied to models initially pre-trained using domain adaptation methods on test domain data, showcasing its resilience and adaptability.
  arXiv  Detail & Related papers  (2023-11-14T03:13:47Z)
• Robust Source-Free Domain Adaptation for Fundus Image Segmentation [3.585032903685044]
  Unlabelled Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled data to the target domain with only unlabelled data. In this study, we propose a two-stage training stage for robust domain adaptation. We propose a novel robust pseudo-label and pseudo-boundary (PLPB) method, which effectively utilizes unlabeled target data to generate pseudo labels and pseudo boundaries.
  arXiv  Detail & Related papers  (2023-10-25T14:25:18Z)
• 1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track [71.12470906323298]
  Generalize-then-Adapt (G&A) framework is composed of a two-stage domain generalization part and a one-stage domain adaptation part. The proposed G&A framework help us achieve the first place on the object detection leaderboard of the OOD-CV challenge.
  arXiv  Detail & Related papers  (2023-01-12T03:50:46Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Semi-Supervised Domain Adaptation with Prototypical Alignment and Consistency Learning [86.6929930921905]
  This paper studies how much it can help address domain shifts if we further have a few target samples labeled. To explore the full potential of landmarks, we incorporate a prototypical alignment (PA) module which calculates a target prototype for each class from the landmarks. Specifically, we severely perturb the labeled images, making PA non-trivial to achieve and thus promoting model generalizability.
  arXiv  Detail & Related papers  (2021-04-19T08:46:08Z)
• Two-phase Pseudo Label Densification for Self-training based Domain Adaptation [93.03265290594278]
  We propose a novel Two-phase Pseudo Label Densification framework, referred to as TPLD. In the first phase, we use sliding window voting to propagate the confident predictions, utilizing intrinsic spatial-correlations in the images. In the second phase, we perform a confidence-based easy-hard classification. To ease the training process and avoid noisy predictions, we introduce the bootstrapping mechanism to the original self-training loss.
  arXiv  Detail & Related papers  (2020-12-09T02:35:25Z)

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.