Mirror detection in dynamic scenes plays a crucial role in ensuring safety for various applications, such as drone tracking and robot navigation. However, current mirror detection models often fail in areas with mirrors that have a similar visual and color appearance to their surrounding objects. They also struggle to generalize well in complex cases, primarily due to limited annotated datasets. In this work, we propose a novel temporal prompt learning network with depth memory (TPD-Net) to address these critical challenges. Our approach includes several key components. First, we introduce a Temporal Prompt Generator (TPG) to learn temporal prompt features. Then, we devise Multi-layer Depth-aware Adaptor (MDA) modules to progressively adapt prompt features from the TPG, thereby learning mirror-related features by embedding temporal depth information as guidance. Moreover, we further refine these mirror-related features by constructing a depth memory and a Depth Memory Read module to read the temporal depths stored in the memory, boosting video mirror detection. Experimental results on a benchmark dataset show that our TPD-Net significantly outperforms 22 state-of-the-art methods in video mirror detection tasks. Our code, models, and results are publicly available athttps://github.com/ge-xing/TPDNet.

Congestion remains one of the most critical yet inadequately addressed challenges in air cargo material handling systems. The industry currently faces two key limitations: (1) traditional definitions of congestion fail to meet analytical requirements, and (2) root cause analysis relies heavily on manual expert judgment. To address these issues, this paper introduces CargoFlow, a system co-developed with industry partners that integrates advanced search algorithms, large language models (LLMs), and a 3D interactive data visualization interface. The system enables efficient and accurate detection of complex congestion patterns, including those imperceptible to human operators. CargoFlow employs optimized search algorithms to detect intricate congestion patterns efficiently, overcoming the limitations of human-driven inspection. By leveraging advanced LLMs with sophisticated prompt engineering, the system min-imizes human effort in diagnosing root causes. The proposed solution has been deployed and rigorously tested in real-world industrial environments, demonstrating its practical effectiveness.

Automated Guided Vehicles (AGV) are becoming increasingly important in modern warehouses to cope with the enormous logistic demands of developing e-commerce and the growing operational costs. The key component of implementing such a system is planning the paths of a large horde of AGVs to deliver orders from shelves to packing locations. The existing solutions regard it as a Multi Agent PathFinding (MAPF) problem, but they can hardly be applied in practice because none of them could satisfy the continuous (temporal), lifelong (future task unknown and keeps appearing), kinematic (acceleration/deceleration/rotation), online (fast response), and scalability (large network, large AGV number, large task number) at the same time. Therefore, we first propose an AGV routing framework that can satisfy all these properties with its corresponding routing algorithm. Then, to improve the efficiency, we propose the Multi-Hop Conflict-Aware Search method (MHCAS) with action combination, MHSC to reduce the search space, and OHSMD to decompose motions such that routing time is reduced by three orders of magnitude. Extensive experimental studies verify the superiority of our methods compared with the state-of-the-art.

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.

In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model. Second, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. We will show the mathematical properties of the model such as existence and uniqueness of solution and establish a maximum/minimum principle for the solution of the model. Extensive experiments on various collections of biological images suggest that the proposed model is effective for segmentation and is computationally efficient.

We propose a variant of the Mumford-Shah model for the segmentation of a pair of overlapping objects with additive intensity value. Unlike standard segmentation models, it does not only determine distinct objects in the image, but also recover the possibly multiple membership of the pixels. To accomplish this, some a priori knowledge about the smoothness of the object boundary is integrated into the model. Additivity is imposed through a soft constraint which allows the user to control the degree of additivity and is more robust than the hard constraint. We also show analytically that the additivity parameter can be chosen to achieve some stability conditions. To solve the optimization problem involving geometric quantities efficiently, we apply a multiphase level set method. Segmentation results on synthetic and real images validate the good performance of our model, and demonstrate the model's applicability to images with multiple channels and multiple objects.

The Mumford-Shah model is one of the most successful image segmentation models. However, existing algorithms for the model are often very sensitive to the choice of the initial guess. To make use of the model effectively, it is essential to develop an algorithm which can compute a global or near global optimal solution efficiently. While gradient descent based methods are well-known to find a local minimum only, even many stochastic methods do not provide a practical solution to this problem either. In this paper, we consider the computation of a global minimum of the multiphase piecewise constant Mumford-Shah model. We propose a hybrid approach which combines gradient based and stochastic optimization methods to resolve the problem of sensitivity to the initial guess. At the heart of our algorithm is a well-designed basin hopping scheme which uses global updates to escape from local traps in a way that is much more effective than standard stochastic methods. In our experiments, a very high-quality solution is obtained within a few stochastic hops whereas the solutions obtained with simulated annealing are incomparable even after thousands of steps. We also propose a multiresolution approach to reduce the computational cost and enhance the search for a global minimum. Furthermore, we derived a simple but useful theoretical result relating solutions at different spatial resolutions.