Fine-tuning Vision Foundation Models (VFMs) with a small number of parameters has shown remarkable performance in Domain Generalized Semantic Segmentation (DGSS). Most existing works either train lightweight adapters or refine intermediate features to achieve better generalization on unseen domains. However, they both overlook the fact that long-term pre-trained VFMs often exhibit artifacts, which hinder the utilization of valuable representations and ultimately degrade DGSS performance. Inspired by causal mechanisms, we observe that these artifacts are associated with non-causal factors, which usually reside in the low- and high-frequency components of the VFM spectrum. In this paper, we explicitly examine the causal and non-causal factors of features within VFMs for DGSS, and propose a simple yet effective method to identify and disentangle them, enabling more robust domain generalization. Specifically, we propose Causal-Tune, a novel fine-tuning strategy designed to extract causal factors and suppress non-causal ones from the features of VFMs. First, we extract the frequency spectrum of features from each layer using the Discrete Cosine Transform (DCT). A Gaussian band-pass filter is then applied to separate the spectrum into causal and non-causal components. To further refine the causal components, we introduce a set of causal-aware learnable tokens that operate in the frequency domain, while the non-causal components are discarded. Finally, refined features are transformed back into the spatial domain via inverse DCT and passed to the next layer. Extensive experiments conducted on various cross-domain tasks demonstrate the effectiveness of Causal-Tune. In particular, our method achieves superior performance under adverse weather conditions, improving +4.8% mIoU over the baseline in snow conditions.

Existing SOTA methods in active learning object detection(ALOD) achieve impressive results, but they overlook two problems: (1) the requirement for instance-level labels during initialization, and (2) the constrained localization ability of the pre-trained fully supervised detector in the active learning phase. Problem (1) contradicts the fundamental purpose of active learning in balancing annotation costs and detection performance. Problem (2) arises from the fact that the active learning process relies on a single pre-trained fully supervised detector. To tackle these problems, we propose Image-level Labels Driven active learning object detection (termed as ILD). Specifically, we propose a multi-step reasoning process based on the chain-of-thought only using image-level labels, including a class-number-aware step and an iterative step, to enhance the detection ability of VLM. The detection results of the VLM and weakly supervised detector are used as pseudo ground-truth boxes to initialize a fully supervised detector during AL initialization. Thus, the initialization process of ILD eliminates the requirement for instance-level labels. In the active learning stage, we design two novel uncertainty and diversity acquisition functions to select the most informative images based on collaborative outputs from both the weakly supervised detector and the pre-trained fully supervised detector. The collaborative mechanism jointly measures the uncertainty of two detectors and the diversity of object features, thereby enhancing the localization quality. Extensive experiments demonstrate that the proposed ILD achieves state-of-the-art performance(i.e., 77.5%, 25.7%, and 27.9%) on PASCAL VOC2007, MS COCO2014 and MS COCO2017 datasets, surpassing the SOTA methods by 3.4%, 1.2% and 4.7%, respectively. Our code is publicly available on https://github.com/RuiTianHIT/ILD.

Owing to the normalized instance scales and robust supervision, heatmap-based human pose estimation (HPE) methods with top-down paradigm have achieved a dominant performance. However, there are two inherent deviations in the basic framework, i.e., representation and target deviations, resulting in performance bottlenecks. The representation deviation is caused by transforming various scales of instances into a unified input size, which results in performance degradation because data with different scale-related characteristics can hardly be handled via unified parameters. The target deviation is caused by exploiting a prior distribution (e.g., Gauss) to model the prediction error, which hinders sufficient network training. In this article, we propose a novel framework called DRPose to revise the abovementioned deviations. Specifically, to address the representation deviation, a scale-aware domain bridging (SDB) block is proposed to transfer feature maps from multiple scale-dependent domains into a unified intermediate domain with dynamic parameters. To address the target deviation, a differentiable coordinate decoder (DCD) is presented to adaptively adjust target distribution of heatmaps in an end-to-end manner. Extensive experiments show that the proposed method significantly improves the performance of most existing models with negligible additional cost. Beyond this, our method achieves 77.1% AP on the COCO test-dev set, outperforming prior works with similar model complexity.

Object detection in dark conditions has always been a great challenge due to the complex formation process of low-light images. Currently, the mainstream methods usually adopt domain adaptation with Teacher-Student architecture to solve the dark object detection problem, and they imitate the dark conditions by using non-learnable data augmentation strategies on the annotated source daytime images. Note that these methods neglected to model the intrinsic imaging process, i.e. image signal processing (ISP), which is important for camera sensors to generate low-light images. To solve the above problems, in this paper, we propose a novel method named ISP-Teacher for dark object detection by exploring Teacher-Student architecture from a new perspective (i.e. self-supervised learning based ISP degradation). Specifically, we first design a day-to-night transformation module that consistent with the ISP pipeline of the camera sensors (ISP-DTM) to make the augmented images look more in line with the natural low-light images captured by cameras, and the ISP-related parameters are learned in a self-supervised manner. Moreover, to avoid the conflict between the ISP degradation and detection tasks in a shared encoder, we propose a disentanglement regularization (DR) that minimizes the absolute value of cosine similarity to disentangle two tasks and push two gradients vectors as orthogonal as possible. Extensive experiments conducted on two benchmarks show the effectiveness of our method in dark object detection. In particular, ISP-Teacher achieves an improvement of +2.4% AP and +3.3% AP over the SOTA method on BDD100k and SHIFT datasets, respectively. The code can be found at https://github.com/zhangyin1996/ISP-Teacher.