Plastic bottles are widely used in various aspects of daily life, commonly for the storage of liquids in food and beverage packaging, pharmaceutical products, cosmetics, and personal care items. This widespread use places higher demands on the production quality of plastic bottles. Effective defect detection technologies are needed to optimize the plastic bottle manufacturing process, but the development of such technologies has been hindered by the lack of publicly available datasets. To address this issue, we propose two dedicated datasets for detecting defects in plastic bottles: one for bottle bodies and the other for shoulders. The plastic bottle body dataset consists of 500 images, captured from a professional workstation, containing four types of defects. The plastic bottle shoulder dataset consists of 3,463 images of shoulders with various types and colors, captured under the same conditions, and includes two types of defects as well as neck positioning marks. All data were collected using professional imaging equipment. In addition, we conducted benchmark experiments using common object detection models on the proposed datasets to demonstrate their usability in the field of defect detection. The datasets and related code will be made publicly available at https://github.com/lzy-coder/PBD.

Recently, cluster-based methods have achieved significant success in unsupervised re-ID tasks. The hierarchical clustering algorithm, exemplified by SpCL, has been widely adopted in unsupervised cross-domain adaptation and unsupervised learning. The momentum-based feature update mechanism in SpCL has been integrated into various algorithms, achieving notable results in subsequent studies. In this paper, we propose a multi-queue feature updating algorithm that stores feature vectors corresponding to person IDs in multiple queues. Random sampling is then applied to construct the negative sample matrix for contrastive loss, addressing the limitations of momentum-based updating methods. Additionally, we replace the static temperature coefficient in contrastive loss with a trainable temperature coefficient, enabling the model to automatically balance sensitivity between easy and hard samples. The code is available at https://github.com/bmfarer/multi-queue.git.

The development of large Vision-Language-Action (VLA) models has enhanced the robot’s ability to manipulate objects in unseen scenarios based on language instructions. While existing VLAs have demonstrated promise in various scenarios, they still struggle with effective multi-modal data feature extraction and lack a closed-loop inference framework. In this paper, we propose an advanced VLA model. Unlike previous works that repurpose VLM for action prediction using simple action quantization, we componentized the VLA architecture with a specialized action module conditioned on the model output and a critic module for inference. We demonstrate the performance improvement of diffusion action transformers in modeling continuous temporal actions, with the critic module applied during inference to form a closed-loop model. Extensive experiments on real robots demonstrate that our model significantly outperforms existing methods, with the ability to handle complex, high-precision tasks and generalize to unseen objects and backgrounds.

Imitation learning is an important method for the human-robot skill transfer. However, ensuring that skills learned through imitation remain effective in different environments is a challenge. This article addresses the challenge by proposing a stable autonomous dynamic system that can effectively handle obstacles and disturbances while maintaining trajectory accuracy. We introduce an energy-approximated dynamic subattractor (EADA) method that enhances disturbance resistance by dynamically selecting subattractors through Neum (an energy function derived from demonstration data). By combining velocity modulation algorithms with EADA, the system achieves global stability, precise obstacle avoidance, autonomous trajectory recovery, and rapid response. The proposed framework effectively handles complex scenarios, including environments with multiple obstacles, dynamic obstacles, and disturbances. We validate the proposed approach through simulations on the LASA dataset and real-world robotic experiments (both single-arm and dual-arm robots), demonstrating its effectiveness in achieving smooth and accurate obstacle avoidance trajectories with generalization capability.

Coded Aperture Snapshot Spectral Imaging (CASSI) multiplexes 3D Hyperspectral Images (HSIs) into a 2D sensor to capture dynamic spectral scenes, which, however, sacrifices the spatial information. Dual-Camera Compressive Hyperspectral Imaging (DCCHI) enhances CASSI by incorporating a Panchromatic (PAN) camera to compensate for the loss of spatial information in CASSI. However, the dual-camera structure of DCCHI disrupts the diagonal property of the product of the sensing matrix and its transpose, making it difficult to efficiently and accurately solve the data subproblem in closed-form and thereby hindering the application of model-based methods and Deep Unfolding Networks (DUNs) that rely on such a closed-form solution. To address this issue, we propose an Alternating Direction DUN, named ADRNN, which decouples the imaging model of DCCHI into a CASSI subproblem and a PAN subproblem. The ADRNN alternately solves data terms analytically and a joint prior term in these subproblems. Additionally, we propose a Cross Spectral Transformer (XST) to exploit the joint prior. The XST utilizes cross spectral attention to exploit the correlation between the compressed HSI and the PAN image, and incorporates Grouped-Query Attention (GQA) to alleviate the burden of parameters and computational cost brought by impartially treating the compressed HSI and the PAN image. Furthermore, we built a real DCCHI system and captured large-scale indoor and outdoor scenes for future academic research. Extensive experiments on both simulation and real datasets demonstrate that the proposed method achieves state-of-the-art (SOTA) performance. The code and datasets have been open-sourced at: https://github.com/ShawnDong98/ADRNN-XST.

In the coded aperture snapshot spectral imaging (CASSI) system, deep unfolding networks (DUNs) have demonstrated excellent performance in recovering 3-D hyperspectral images (HSIs) from 2-D measurements. However, some noticeable gaps exist between the imaging model used in DUNs and the real CASSI imaging process, such as the sensing error as well as photon and dark current noise, compromising the accuracy of solving the data subproblem and the prior subproblem in DUNs. To address this issue, we propose a degradation estimation network (DEN) to correct the imaging model used in DUNs by simultaneously estimating the sensing error and the noise level, thereby improving the performance of DUNs. Additionally, we propose an efficient local and non-local transformer (LNLT) to solve the prior subproblem, which not only effectively models local and non-local similarities but also reduces the computational cost of the window-based global multihead self-attention (MSA). Furthermore, we transform the DUN into a recurrent neural network (RNN) by sharing parameters of DNNs across stages, which not only allows DNN to be trained more adequately but also significantly reduces the number of parameters. The proposed DERNN-LNLT achieves state-of-the-art (SOTA) performance with fewer parameters on both simulation and real datasets (code:https://github.com/ShawnDong98/DERNN-LNLT).

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a MixS2Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the MixS2Transformer into the RDLUF leads to an end-to-end trainable neural network RDLUF-MixS2. Experimental results establish the superior performance of the proposed method over existing ones. Code is available: https://github.com/ShawnDong98/RDLUF_MixS2

Existing location optimization solutions only consider the positioning of resouces/seeds at the best location in a target area by minimizing a certain metric over distance. In reality, what really matters is time. In this paper, the location optimization problem is formulated as the expected response time minimization problem rather than a distance minimization problem. Moreover, we propose an algorithm that takes into consideration various stochastic factors which affect the location optimization problem, such as non-uniform probability distribution of the demands, road congestion level, and vehicles' maximum speed. Our proposed algorithm shows promising performance when the disparity among vehicle capabilities (e.g., maximum speed) are large and the environment constraints (e.g., traffic jam) are taken into consideration.

JIRiSS (Information Retrieval based Software Search for Java) is a software exploration tool that uses an indexing engine based on an information retrieval method. JIRiSS is implemented as a plug-in for Eclipse and it allows the user to search Java source code for the implementation of concepts formulated as natural language queries. The results of the query are presented as a ranked list of software methods or classes, ordered by the similarity to the user query. In addition to that, JIRiSS includes other advanced features like automatically generated software vocabulary, advanced query formulation options including spell-checking as well as fragment-based search.