Unsupervised domain adaptation methods have been proposed to tackle the problem of covariate shift by minimizing the distribution discrepancy between the feature embeddings of source domain and target domain. However, the standard evaluation protocols assume that the conditional label distributions of the two domains are invariant, which is usually not consistent with the real-world scenarios such as long-tailed distribution of visual categories. In this article, the imbalanced domain adaptation (IDA) is formulated for a more realistic scenario where both label shift and covariate shift occur between the two domains. Theoretically, when label shift exists, aligning the marginal distributions may result in negative transfer. Therefore, a novel cluster-level discrepancy minimization (CDM) is developed. CDM proposes cross-domain similarity learning to learn tight and discriminative clusters, which are utilized for both feature-level and distribution-level discrepancy minimization, palliating the negative effect of label shift during domain transfer. Theoretical justifications further demonstrate that CDM minimizes the target risk in a progressive manner. To corroborate the effectiveness of CDM, we propose two evaluation protocols according to the real-world situation and benchmark existing domain adaptation approaches. Extensive experiments demonstrate that negative transfer does occur due to label shift, while our approach achieves significant improvement on imbalanced datasets, including Office-31, Image-CLEF, and Office-Home.

Single-Domain Generalized Object Detection (S-DGOD) aims to train on a single source domain for robust performance across a variety of unseen target domains by taking advantage of an object detector. Existing S-DGOD approaches often rely on data augmentation strategies, including a composition of visual transformations, to enhance the detector's generalization ability. However, the absence of real-world prior knowledge hinders data augmentation from contributing to the diversity of training data distributions. To address this issue, we propose PhysAug, a novel physical model-based non-ideal imaging condition data augmentation method, to enhance the adaptability of the S-DGOD tasks. Drawing upon the principles of atmospheric optics, we develop a universal perturbation model that serves as the foundation for our proposed PhysAug. Given that visual perturbations typically arise from the interaction of light with atmospheric particles, the image frequency spectrum is harnessed to simulate real-world variations during training. This approach fosters the detector to learn domain-invariant representations, thereby enhancing its ability to generalize across various settings. Without altering the network architecture or loss function, our approach significantly outperforms the state-of-the-art across various S-DGOD datasets. In particular, it achieves a substantial improvement of 7.3% and 7.2% over the baseline on DWD and Cityscape-C, highlighting its enhanced generalizability in real-world settings.

Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions.

Accurate visual detection under poor illuminance is arguably an important and challenging practice for robot monitoring. The purpose of this study is to build detectors for inspection robot under nonuniform and poor lighting conditions. To this end, we propose an Unsupervised Lighting Reflectance Estimation framework for dark image detection (ULRE-Net). Based on lightness-color consistency, the “true color" of objects merely depends on the reflectance regardless of the variations of illuminance. The proposed ULRE-Net extracts robust features for detection by estimating a simple and differentiable expression of reflectance, and the method formulates reflectance estimation in an unsupervised learning manner without relying on image pairs. Thorough experiments are conducted to validate the veracity and generality of the proposed method over existing ones.

Recent studies have shown that deep neural networks are sensitive to visual blur. This vulnerability raises concerns about the robustness of learning-based methods in safety-critical applications, such as autonomous driving and industrial inspection. Although many methods have demonstrated the potential to enhance the robustness of vision tasks in blurry scenes, a standard benchmark for comparing and validating their effectiveness is still lacking. To address this, we propose a new benchmark for the comprehensive evaluation of visual recognition tasks in blurry scenes. The benchmark dataset includes a large number of scenes with different blur types, enabling multi-dimensional evaluation and comparison of existing methods. The existing methods are integrated into a unified evaluation framework for comparison, and the mechanism of robustness improvement is roughly categorized as reducing image-level shift and distribution-level shift. Through this unified framework, we identify the strengths and weaknesses of current methods in visual recognition tasks under blurry conditions. Furthermore, we propose a prior knowledge-based regularization module that can be easily incorporated into existing methods, which consistently boosts performance on visual recognition tasks in blurry scenes.