The primary goal of white balance (WB) for sRGB images is to correct inaccurate color temperatures, ensuring that images display natural, neutral colors. While existing WB methods yield reasonable results, their effectiveness is limited. They either focus solely on global color adjustments applied before the camera-specific image signal processing pipeline or rely on end-to-end models that generate WB outputs without accounting for global color trends, leading to suboptimal correction. To address these limitations, we propose an Auxiliary Bimodal Cross-domain Transformer (ABC-Former) that enhances WB correction by leveraging complementary knowledge from global color information from CIELab and RGB histograms alongside sRGB inputs. By introducing an Interactive Channel Attention (ICA) module to facilitate cross-modality global knowledge transfer, ABC-Former achieves more precise WB correction. Experimental results on benchmark WB datasets show that ABC-Former performs favorably against state- of-the-art WB methods. The source code is available at https://github.com/ytpeng-aimlab/ABC-Former.

Image deraining has gained significant attention in recent years due to its essential role in applications like autonomous driving and surveillance systems. Despite their good performance in rain removal, most image-deraining models are trained on synthetic datasets, leading to a performance gap when applied to real-world scenarios due to differences between synthetic and actual rain patterns. Although some real-world datasets exist, they may contain unsuitable images for training image-deraining models, such as those without clear rain or containing haze, which could lead to suboptimal model performance. Therefore, we propose Automated Understanding and Refinement Agents (AURA) to refine deraining datasets by leveraging large vision-language models to filter out inappropriate training data and categorize images based on the rain density (e.g., light, moderate, and heavy rain). This automated refinement and annotation process enhances dataset quality, resulting in improved deraining model performance in real-world scenarios. Experimental results demonstrate that training deraining models on datasets refined and annotated by AURA can significantly enhance their deraining performance.

Traffic Atomic Activity, which describes traffic patterns for topological intersection dynamics, is a crucial topic for the advancement of intelligent driving systems. However, existing atomic activity datasets are collected from an egocentric view, which cannot support the scenarios where traffic activities in an entire intersection are required. Moreover, existing datasets only provide video-level atomic activity annotations, which require exhausting efforts to manually trim the videos for recognition and limit their applications to untrimmed videos. To bridge this gap, we introduce the Aerial Traffic Atomic Activity Recognition and Segmentation (ATARS) dataset, the first aerial dataset designed for multilabel atomic activity analysis. We offer atomic activity labels for each frame, which accurately record the intervals for traffic activities. Moreover, we propose a novel task, Multi-label Temporal Atomic Activity Recognition, enabling the study of accurate temporal localization for atomic activity and easing the burden of manual video trimming for recognition. We conduct extensive experiments to evaluate existing state-of-theart models on both atomic activity recognition and temporal atomic activity segmentation. The results highlight the unique challenges of our ATARS dataset, such as recognizing extremely small objects’ activities. We further provide a comprehensive discussion analyzing these challenges and offer valuable insights for future direction to improve recognition of atomic activity in an aerial view. Our source code and dataset are available at https://github.com/magecliff96/ATARS/.

Images captured on rainy days often contain rain streaks that can obscure important scenery and degrade the performance of high-level vision tasks, such as image segmentation in autonomous vehicles. As a result, image deraining, a low-level vision task focused on removing rain streaks from images, has gained popularity over the past decade. Recent advancements have primarily concentrated on supervised image deraining methods, which rely on paired rain-clean image datasets to train deep neural network models. However, collecting such paired real data is challenging and time-consuming. To address this, our method introduces a novel self-supervised approach that leverages the proposed locally dominant gradient prior and non-local self-similarity stochastic sampling. This approach extracts potential rain streaks and generates stochastic derained references for image deraining. Experimental results on public benchmark image-deraining datasets show that our proposed method performs favorably against state-of-the-art few-shot and self-supervised image deraining methods.