Domain Generalized Semantic Segmentation (DGSS) has recently attracted lots of research attention aiming to achieve robust segmentation performance on unseen domains, which aligns with diverse real-world applications. Visual Foundation Model (VFM), depending on large-scale pre-trained data and exquisite training strategies with good generalizability, has been explored and mined in some downstream tasks like DGSS. However, existing VFM-based DGSS methods predominantly focus on fine-tuning the VFM to adapt them to the semantic segmentation task. While this improves task alignment, it inevitably introduces additional training overhead and still results in static prediction behavior at inference time, making the model unable to adapt to varying target-domain distributions during deployment. To address these issues, we propose aTraining-free VFM-guided Dynamic Refinement (TVDR) frameworkfor the DGSS task, operating at the inference stage. First, a maximum category voting module is proposed to smooth the segmentation result of large masks generated by Segment Anything Model (SAM). Second, a small-object under-segmentation optimization strategy is proposed to improve the generalization of the small objects. Finally, a fusion refinement module is designed to refine the segmentation results of the above strategies. Compared with existing methods, our strategy does not require any parameter updates for VFM and has the advantages of plug-and-play and flexible deployment. It provides an efficient and practical new paradigm for cross-domain semantic segmentation tasks. Extensive experiments on widely-used benchmarks verified the effectiveness of the proposed approaches. Code is available at: https://github.com/Hectoor/TVDR.

The continuous breakthroughs in 3D reconstruction and rendering technologies have enabled synthesized 3D models to achieve exceptional realism. In particular, Neural Radiance Fields (NeRF) and 3D Gaussian Splattings (3DGS) have gained significant attention due to their impressive ability to deliver high-quality 3D models. However, research on the quality assessment of NeRF/3DGS models remains an underexplored area, hindering the development of relevant generation, compression, and transmission algorithms. To fill this gap, in this paper, we propose the first no-reference quality assessment metric for NeRF/3DGS models rendered in Processed Video Sequences (PVS). Considering the uniqueness and diversity of distortions introduced in NeRF/3DGS models, the core idea behind the proposed metric is to extract universal quality-aware features that are generalizable across various distortions, rather than targeting a specific one. Specifically, inspired by the fact that spatial distortions typically alter the statistical distributions, we first measure the spatial fidelity of the rendered PVS by analyzing the spatial explicit statistics of textural variation and naturalness. Then, motivated by the ability of implicit energy composition changes to reflect temporal distortions, we propose to evaluate the temporal consistency of the rendered PVS through inter-frame discrepancy energy and multi-frame motion energy in the Singular Value Decomposition (SVD) domain. Finally, the spatial explicit statistics and temporal implicit energy are combined as perceptual features to evaluate the quality of NeRF/3DGS models via Support Vector Regression (SVR). Extensive experimental results on three representative databases demonstrate the superiority of the proposed metric in various aspects, such as predictive accuracy, performance stability, and cross-database generalizability. The source code will be publicly available at https://github.com/ZhengyuZhang96/PVS-3DMQA.

Haze deteriorates the quality of captured images, which severely limits the accuracy of clean image-trained object detectors in hazy conditions. Source-free domain adaptation (SFDA) aims to adapt a clean source image-trained detector to the unlabeled hazy target domain without access to the clean source domain data. However, existing source-free object detection (SFOD) methods encounter two issues when leveraging pseudo labeling paradigm: 1) the large domain shift between clear and hazy images may introduce noises in pseudo labels, 2) the single confidence threshold-based methods may ignore valuable information of the low-confidence samples. To address these issues, we propose a multi-level guided discrepancy learning-based approach for SFOD in hazy conditions, named MGDL. Specifically, we first propose a differentiated enhancement module (DEM) to intentionally augment the diversity of data styles. It uses dehazing and random perturbation to generate reliable high-quality labels and strengthen the tolerance to haze-related factors. To enhance the consistency constraints for discrepancy learning, we propose a multi-level guiding strategy (MLGS) which consists of a dual label-level and an instance-level guidance. Considering that false negatives may dominate in noisy labels, we propose a dual label-level guiding (DLG) strategy to excavate comprehensive useful information from high-and low-confidence samples. Besides, an instance-level contrastive learning (ICL) approach is proposed to guide the model to focus on objects and make the model insensitive to style at the same time. Extensive experiments conducted on multiple datasets have demonstrated that our method achieves superior performance over the state-of-the-art SFOD methods.

Image enhancement algorithms can facilitate computer vision tasks in real applications. However, various distortions may also be introduced by image enhancement algorithms. Therefore, the image quality assessment (IQA) plays a crucial role in accurately evaluating enhanced images to provide dependable feedback. Current enhanced IQA methods are mainly designed for single specific scenarios, resulting in limited performance in other scenarios. Besides, no-reference methods predict quality utilizing enhanced images alone, which ignores the existing degraded images that contain valuable information, are not reliable enough. In this work, we propose a degraded-reference image quality assessment method based on dual residual-guided interactive learning (DRGQA) for the enhanced images in multiple scenarios. Specifically, a global and local feature collaboration module (GLCM) is proposed to imitate the perception of observers to capture comprehensive quality-aware features by using convolutional neural networks (CNN) and Transformers in an interactive manner. Then, we investigate the structure damage and color shift distortions that commonly occur in the enhanced images and propose a dual residual-guided module (DRGM) to make the model concentrate on the distorted regions that are sensitive to human visual system (HVS). Furthermore, a distortion-aware feature enhancement module (DEM) is proposed to improve the representation abilities of features in deeper networks. Extensive experimental results demonstrate that our proposed DRGQA achieves superior performance with lower computational complexity compared to the state-of-the-art IQA methods.

With the rapid advancement of autonomous driving technology, there is an increasingly urgent demand for faster and more accurate object detection frameworks. Recently, numerous deep learning-based object detectors have shown impressive performance in real-time driving applications. However, the detection of small objects such as various traffic signs remains challenging due to the complex nature of these objects. This paper proposes a transformer-based detector TS-DETR to improve the accuracy of small object detection in autonomous driving systems. We introduce a constrained decoder structure to focus the model's attention on the predicted boxes. Additionally, a content-aware deformable cross-attention mechanism is proposed to obtain more comprehensive attention weights. Experimental results on challenging public datasets such as TT100K and CCTSDB2021 demonstrate that our method demonstrates substantial performance enhancements while introducing only a slight increment in parameter count compared to current algorithms.

Image dehazing algorithms have emerged to solve the visual impairment caused by haze. It is important to establish dehazed image quality assessment (DQA) methods that can accurately evaluate the dehazed image quality and the performance of dehazing algorithms. However, classical image quality assessment (IQA) and most hand-crafted feature based DQA methods may not be able to adequately measure complex distortions of dehazed images. To address this issue, this paper proposes a Texture Residual-Guided Dehazed image Quality Assessment (TRG-DQA) method. Specifically, we first introduce a global and local feature extraction module employing a combination of the Transformer and convolutional neural networks (CNN) for extracting the comprehensive features. Considering that texture residual maps represent haze density and artifact distortion information, we propose a residual-guided module to guide the model for efficient learning. Additionally, to mitigate the information loss issue that occurs in deeper networks, a distortion-aware feature enhancement module is proposed. Extensive experiments on six DQA databases demonstrate the proposed TRG-DQA achieves superior performance among all the state-of-the-art methods.