Uncalibrated photometric stereo is a formidable challenge task in the field of 3D vision, aiming to reconstruct the surface normal of an object when its shape, material reflectance, and light conditions are all unknown. At present, it remains difficult to address when dealing with more general materials (e.g., anisotropy) with complex reflectance and objects with significant shadows. In addition, the presence of generalized bas-relief ambiguity, which refers to the inherent ambiguity between shape and light, further compounds the challenges of uncalibrated photometric stereo. To overcome these limitations, we propose a lightweight unsupervised neural inverse rendering architecture, called General Material Shadow-Neural Inverse Rendering (GMS-NIR), which can effectively solve the uncalibrated photometric stereo problem by combining a learnable general material reflectance model and a shadow rendering model. We design an optimization strategy that allows GMS-NIR to jointly optimize the surface normal, light direction, and light intensity in an unsupervised manner by utilizing backpropagation to minimize rendering errors. Thorough experiments with diverse real-world datasets affirm the superior performance of our approach compared with alternative uncalibrated photometric stereo methods. GMS-NIR’s ability to handle a variety of materials and complex object shapes while accurately reconstructing surface normal makes it a promising advancement in the fields of computer vision and 3D surface reconstruction.

Gait recognition is a biometric technology that distinguishes individuals by analyzing their walking patterns and holds significant potential for development. Current methods primarily focus on extracting gait features either from the overall appearance or specific local regions. However, they often overlook the partitioning of local regions based on the individual’s body structure, as well as the weighted relationships between global and local features. We propose a Weighted Global-Local Feature Fusion Module to partition local features according to human body parts and adaptively integrate global and local gait features. This approach facilitates fine-grained learning of part-level local features and enhances the discriminative representation of gait features. Furthermore, we employ an Attention-based Multiscale Temporal Aggregation operation to adaptively fuse motion features from different time scales, preserving crucial spatio-temporal information while reducing the length of the time series. The average Rank-1 accuracy in CASIA-B and OUMVLP datasets is 93.0% and 90.7%, respectively. The experimental results demonstrate that our method achieves satisfactory recognition performance, indicating its potential for advancing gait recognition technology.

We propose a learned inverted residual cascaded group small target detection transformer (LIS-DETR) model, a novel approach for small object detection in autonomous driving. This model has a unique backbone network, the basic inverted residual cascaded group mobile block, which enhances feature representation and reduces computational redundancy. A dedicated small detection layer is integrated to improve small object detection specifically. In addition, an adaptive learned positional encoding transformer layer is incorporated to strengthen global contextual relationships, and the designed inner-SIoU loss function further accelerates convergence speed. Experimental results show a 3.1% increase in mAP50 accuracy on VisDrone datasets and a 1.9% improvement on processed SODA10m datasets compared with baseline methods. These advances demonstrate the LIS-DETR model’s strong generalization ability and the significant potential to enhance the efficacy of autonomous driving systems.

High-density printed circuit boards (PCBs) (with a minimum line width of 0.075 mm) display random and irregular defect patterns due to complex manufacturing processes, challenging their reliability. Deep learning-based visual detection methods show high accuracy but depend on the scale and diversity of datasets. The absence of large, well-annotated PCB defect datasets limits these methods in industrial applications. This paper introduces and open-sources an industrially applicable dataset, PCB-BB, which features large-scale, diverse, and high-fidelity data. In our proposed Zero-Shot Defect Feature Optimizer method, we first reconstruct the structure and color features of the learning image and encode these features into a unified codebook. Subsequently, by performing latent space codebook confusion and query decoding at the defect’s corresponding locations, we optimize the morphology and color characteristics of the image surface defects. Detection networks trained with optimized data show significant improvements: a 4.7% increase in F1 score, 2.7% in mAP@50-95, 2.5% in mAP@50, and 10.0% in recall. Our approach addresses key challenges such as scale mismatch and detection efficiency in practical deep learning applications. Enhancing visual detection for complex defects in industrial settings is crucial for advancing the intelligence of detection systems. All resources will be available on GitHub ( https://github.com/OrigArith/PCB-BB-ZSDF-OPT).

In recent years, RGB-T salient object detection (SOD) technology has attracted increasing attention. By incorporating thermal images, it can identify salient objects under challenging scenes such as low illumination. However, due to the inherent modality differences between RGB and thermal images, the interaction and fusion of multi-modal features become a critical challenge. To address this challenge, we propose a novel asymmetric light-aware progressive decoding network (ALPD-Net) for RGB-T SOD. Specifically, we develop an asymmetric light-aware interaction (ALI) module that facilitates effective interaction between the two modalities in an asymmetric manner, reducing interference information. In addition, we propose a Channel-Space Feature Fusion module to select and fuse information from different modalities in both channel and spatial dimensions. Finally, we design a phased progressive decoding strategy that divides the decoding process into two stages, gradually refining features to generate high-quality saliency maps. Extensive experiments conducted on three publicly available RGB-T SOD datasets demonstrate that the proposed ALPD-Net achieves outstanding performance against the state-of-the-art RGB-T SOD methods.

Aiming at the non-uniformity response problems of large array gazing remote sensing cameras due to factors such as production process and working mode, we proposed a non-uniformity correction method based on an improved total variation model. First, an improved total variation model with norm fidelity is constructed based on the distortion model of the imaging system. Next, the improved total variation model is iteratively solved by the Split Bregman splitting method. Then, in the iterative optimization process of the total variation model, the wavelet coefficients of the non-uniformity correction results of the improved total variation model are reconstructed using wavelet transform, and the high-frequency wavelet coefficients affecting the image quality are processed. Finally, the optimal iteration result obtained after wavelet reconstruction is the final correction result, and the non-uniformity correction of the remote sensing image is completed. The experimental results show that the non-uniformity correction effect of the proposed method is superior to other comparison methods. After correction, the non-uniformity coefficient of the image is reduced by more than three times, and the signal-to-noise ratio of the image is increased by more than 20 times, which effectively eliminates the non-uniformity response in the image and preserves the detail information of the image.

In low-light conditions, object detection algorithms suffer from reduced accuracy due to factors such as noise and insufficient information. Current solutions often involve a two-stage process: first, improving image illumination and then performing object detection. However, this method has limitations as these networks work independently. To address this, we propose a parallel object detection algorithm for low-light environments. Our approach simultaneously encodes image features using both an illumination enhancement network and an object detection network. This innovative design allows these networks to adapt to each other, improving feature adaptability for object detection. We enhance adaptive learning efficiency by introducing a novel mutual feedback mechanism, which dynamically adjusts the learning weights of the two networks, thereby enhancing the network’s capacity to encode object-related information in low-light conditions. Experiments were conducted on both real-world and synthetic datasets. On the real-world dataset, the proposed method outperformed the original object detection network, achieving improvements of 4.76% in mAP@0.5, 12.12% in mAP@0.5:0.95, and 8.4% in F1-score. On the synthetic dataset, the method demonstrated gains of 9.67%, 9.75%, and 10.6% in mAP@0.5, mAP@0.5:0.95, and F1-score, respectively. These experimental results indicate that the proposed approach significantly enhances the performance of object detection algorithms under low-light conditions.

The growing demand for immersive experiences has significantly influenced research in the quality assessment of light field images (LFIs). However, LFIs are susceptible to distortion during encoding, transmission, and compression, making accurate distortion measurement a current concern. In the full-reference (FR) quality assessment of LFIs, the discrepancy between the distorted and reference images is typically learned for network acquisition, overlooking the potential to enhance learning efficiency and accuracy by incorporating error map input. To address this, we propose a framework for the quality evaluation of FR LFIs based on multi-feature interactive fusion. The framework comprises three components: feature encoding network, spatial angle interactive fusion network, and score regression network, aimed at obtaining the quality score of LFIs. The feature encoding network leverages different extraction networks to capture spatial, angular, and error information, enabling the network to focus on key areas. In the spatial angle interactive fusion network, the feature fusion network integrates features from different networks, enriching and unifying information, the spatial angle interactive network further extracts distortion information from the enriched feature maps. The resulting framework demonstrates strong subjective and objective agreement in the quality assessment of LFIs, offering theoretical and practical implications for algorithm optimization and application.

In recent years, vision transformers (ViTs) have made significant breakthroughs in computer vision and have demonstrated great potential in large-scale models. However, the quantization methods for convolutional neural network models do not perform well on ViTs models, leading to a significant decrease in accuracy when applied to ViTs models. We extend the quantization parameter optimization method based on the Hessian matrix and apply it to the quantization of the LayerNorm module in ViT models. This approach reduces the impact of quantization on task accuracy for the LayerNorm module and enables more comprehensive quantization of ViT models. To achieve fast quantization of ViTs models, we propose a quantization framework specifically designed for ViTs models: Hessian matrix–aware post-training quantization for vision transformers (HAPTQ). The experimental results on various models and datasets demonstrate that our HAPTQ method, after quantizing the LayerNorm module of various ViT models, can achieve lossless quantization (with an accuracy drop of less than 1%) in ImageNet classification tasks. Specifically, the HAPTQ method achieves 85.81% top-1 accuracy on the ViT-L model.