13 accepted papers
Failure-based variable ordering heuristics (VOH) are efficient general-purpose search heuristics for solving constraint satisfaction problems (CSP). They learn from the failures detected during the search and select the variables that are most likely to fail. The current failure-based VOHs, i.e. the
Singleton consistency significantly reduces the search space of backtracking search for solving constraint satisfaction problems (CSP). However, it is too expensive to be used throughout the search, and it has not been successfully applied in solving general CSPs. In this paper, we propose a variabl
Learning robust and generalizable manipulation skills from few demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. Although recent imitation learning methods have achieved impressive results, they often require a large amount of
Advances in image generation technologies have raised growing concerns about their potential misuse, particularly in producing misinformation and deepfakes. This creates an urgent demand for effective methods to detect AI-generated images (AIGIs). While progress has been made, achieving reliable per…
Driven by the rapid development of deep learning technology, the YOLO series has set a new benchmark for real-time object detectors. Additionally, transformer-based structures have emerged as the most powerful solution in the field, greatly extending the model's receptive field and achieving signifi…
In this paper, we propose a lightweight and efficient state-space model-based instance segmentation network named MambaInst, which extracts deep semantic features through a LightSSM Block consisting of gating mechanisms and residual connectivity to model long-distance spatial dependencies with linea…
Variable ordering heuristics (VOH) play a central role in solving Constraint Satisfaction Problems (CSP). The performance of different VOHs may vary greatly when solving the same CSP instance, so identifying an efficient candidate VOH for a given CSP has been a key issue in the community. In this st…
In this paper, we introduce an image inpainting method based on the State Space Model (SSM), named Restoration Mamba (RestorMamba). This approach incorporates effi-cient long-range dependency modeling within the network, which is particularly suited for the complexities of high-texture and high-reso…
Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mixture-of-Experts (MoE) model has recently been sho…
In linguistics, all languages can be considered as symbolic systems, with each language relying on symbolic processes to associate specific symbols with meanings. In the same language, there is a fixed correspondence between linguistic symbol and meaning. In different languages, universal meanings f…
AllDifferent constraint is widely used in Constraint Programming to model real world problems. Existing Generalized Arc Consistency (GAC) algorithms map an AllDifferent constraint onto a bipartite graph and utilize the structure of Strongly Connected Components (SCCs) in the graph to filter values.…
Restart-based Branch-and-Bound Search (BBS) is a standard algorithm for solving Constraint Optimization Problems (COPs). In this paper, we propose an approach to find good partial assignments to jumpstart search at each restart for general COPs, which are identified by comparing different best solut…
In congestion games, users make myopic routing decisions to jam each other, and the social planner with the full information designs mechanisms on information or payment side to regulate. However, it is difficult to obtain time-varying traffic conditions, and emerging crowdsourcing platforms (e.g.,…