Yue Xi

The state space models (SSMs) have demonstrated remarkable performance in image restoration tasks by the Semantic-Guided Neighboring, which effectively mitigates long-range dependency decay issues in token interaction. However, their effectiveness significantly declines under challenging low-light conditions, where severe image degradation undermines reliable pixel-level semantic guidance. To address this limitation, we propose Uncertainty State Space Restoration, a novel approach that enables Mamba architectures to effectively utilize the robust guidance from the uncertainty map for restoration tasks. A novel Uncertainty-Guided Mamba framework, UGMamba, is developed for Low-Light Image Enhancement (LLIE), which features two key innovations: 1) Uncertainty-Aware Restoration that generates a robust uncertainty-aware guidance to prioritize the recovery of challenging regions such as textured and edge pixels with high uncertainty, and 2) Frequency-Domain Illumination Modeling in State Space Block that directly addresses illumination-related degradation via signal amplitude analysis. Extensive experiments across five datasets demonstrate UGMamba's superior performance, surpassing fourteen state-of-the-art methods performance in both restoration accuracy and computational efficiency.

Nighttime object detection presents significant challenges due to the scarcity of large-scale, high-quality annotations across diverse nighttime scenarios. To circumvent the need for manual nighttime image annotation, researchers have explored Unsupervised Domain Adaptive Object Detection (UDA-OD), which transfers knowledge from labeled daytime datasets to unlabeled nighttime data through pseudo-labeling. While existing approaches have shown promising results, their effectiveness remains limited by the low quality of pseudo labels, restricting model adaptation to nighttime conditions. To address these limitations, we propose Reliable-Teacher, a novel mutual-learning framework that comprehensively leverages target domain knowledge through Uncertainty-Guided Collaborative Learning. Specifically, our approach consists of three key components: 1) A Collaborative Pseudo-Label Construction module that intelligently integrates reliable Teacher-generated pseudo-labels into Student proposals, significantly enhancing pseudo-label quality; 2) An Uncertainty-Guided Consistency Reasoning module that enforces inter-category consistency between Teacher and Student predictions at both anchor and bounding box levels; 3) A Reliability-Weighted Classification Loss that minimizes the influence of unreliable predictions to further enhance uncertainty-guided learning. Extensive experiments demonstrate that Reliable-Teacher significantly outperforms state-of-the-art methods, achieving performance gain of up to 3.1%, 2.2% and 1.7% mAP on BDD100K [1], SHIFT [2], and VisDrone [3] benchmarks, respectively. Upon acceptance, our code will be released to facilitate further research in this domain.

Unlike traditional target tracking tasks, UAV tracking scenarios in consumer electronics applications present complex challenges such as background interference, target occlusion, and aerial perspective change, which directly affect tracking performance. To tackle these challenges, we propose FCADtrack, a Spatial Context Awareness-Based Adaptive Real-Time UAV Tracking method for consumer electronics. FCADtrack focuses on fine-tuning to meet UAV-specific requirements and introduces a spatial information interaction mechanism to enhance tracking robustness. First, it incorporates an Adapter Fine-Tuning Module into the ViT backbone, trained on UAV-specific datasets, enabling the model to capture distinctive UAV tracking features and improve adaptability in complex scenarios. Additionally, the Feature Enhanced Contextual Attention (FECA) module is introduced, comprising the Local Spatial Feature Enhanced Module (FEM) and the Global Spatial-Channel Attention Module (SCAM). The FEM extracts rich target features and suppresses background interference through diverse convolutional operations, while the SCAM enhances global feature representation by integrating spatial and channel information using Global Average Pooling (GAP) and Global Max Pooling (GMP). These components significantly boost target feature representation and reduce background interference. The proposed FCADtrack has achieved competitive tracking performance compared with existing methods. Furthermore, it runs a real-time processing speed of 30.3 FPS on the Jetson Orin NX16 platform, demonstrating its potential for real-time consumer electronics applications. Code is released at: https://github.com/qin490/FCADtracker.

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively.

Occluded person re-identification (ReID) is a challenging task due to some of the essential features are interfered by obstacles or other pedestrians. Multi-granularity local feature extraction and recognition can effectively improve the accuracy of ReID under occlusion. However, manual segmentation methods for local features can lead to feature misalignment. Feature alignment based on pose estimation often ignores non-body details (e.g., handbags, backpacks, etc.) while increasing the complexity of the model. To address the above challenges, we propose a novel Adaptive Occlusion-Aware Network (AOANet), which mainly consists of two modules, the Adaptive Position Extractor (APE) and the Occlusion Awareness Module (OAM). In order to adaptively extract distinguishing features of body parts, APE optimizes the representation of multi-granularity features by the guidance of attention mechanism and keypoint features. To further perceive the occluded region, the OAM is developed by adaptively calculating the occlusion weights for body parts. These weights can lead to highlighting the non-occluded parts and suppressing the occluded parts, which in turn improves the accuracy in the occluded situation. Extensive experimental results confirm the advantages of our method on the MSMT17, DukeMTMC-reID, Market-1501, Occluded-Duke and Occluded-ReID datasets. The comparative results demonstrate that our method outperforms comparable methods. Especially on the Occluded-Duke dataset, our method achieved 70.6% mAP and 81.2% Rank-1 accuracy.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, nonlinear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a fine-grained parameter predictor (FPP) to estimate pixelwise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on nonuniform illuminations in drone-captured images. In order to learn the nonlinear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a progressive filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art (SOTA) methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset .

Benefiting from the advances in object detection in remote sensing, detecting objects in images captured by drones has achieved promising performance in recent years. However, drone-view object detection in rainy weather conditions (Rainy DroneDet) remains a challenge, as small-sized objects blurred by rain streaks offer a little valuable information for robust detection. In this paper, we propose a Collaborative Deraining Network called “CoDerainNet”, which simultaneously and interactively trains a deraining subnetwork and a droneDet subnetwork to improve the accuracy of Rainy DroneDet. Furthermore, we propose a Collaborative Teaching paradigm called “ColTeaching”, which leverages rain-free features extracted by the Deraining Subnetwork and teaches the DroneDet Subnetwork such features, to remove rain-specific interference in features for DroneDet. Due to the lack of an existing dataset for Rainy DroneDet, we built three drone datasets, including two synthetic datasets, namely RainVisdrone and RainUAVDT, and one real drone dataset, called RainDrone. Extensive experiment results on the three rainy datasets show that CoDerainNet can significantly reduce the computational costs of state-of-the-art (SOTA) object detectors while maintaining detection performance comparable to these SOTA models.