28 accepted papers
Dense visual prediction tasks, including object detection and segmentation, inherently require precise and discriminative positional information to delineate object boundaries and pixel regions. Recent DETR-based frameworks advance dense prediction tasks through iterative attention applied to conten
Lifelong Multi-Agent Pathfinding (Lifelong MAPF) is an extension of the Multi-Agent Pathfinding (MAPF) problem. It has significant applications in scenarios such as warehouse logistics and delivery services. Narrow passages that restrict side-by-side traversal are common in such scenarios, posing a …
Bayesian optimization (BO) is a powerful approach for black-box optimization, and in many real-world problems, domain experts possess valuable prior knowledge about promising regions of the search space. However, existing prior-informed BO methods are often overly complex, tied to specific acquisiti…
No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality assessment independently of media types, prior knowledge on quality learned from i
Audio-visual quality assessment (AVQA) research has been stalled by limitations of existing datasets: they are typically small in scale, with insufficient diversity in content and quality, and annotated only with overall scores. These shortcomings provide limited support for model development and mu…
Despite deep learning-based methods perform remarkably well in surface defect detection recently, the unpredictable shapes and sizes of surface defects and complicated texture background still pose enormous challenges for existing methods. To address these problems, we propose a novel surface defect…
Recently, learned image compression algorithms have achieved significant performance. The entropy model is crucial for improving the rate-distortion performance by estimating the probability distribution of latent representation. In this paper, we propose an adaptive fusion entropy model for learned…
Cooperative scalar field mapping is an important task for multi-robot systems. However, the limited communication and computation resources of robots have hindered the application of cooperative field mapping in large-scale scenarios. This letter proposes an adaptive-resolution Gaussian process mapp
Video Super-Resolution (VSR) is essential for reconstructing high-definition sequences from correlated video frames. While Transformer-based VSR methods have improved reconstruction quality, they require substantial computational resources, limiting deployment on resource-constrained devices. To tac…
Multi-agent adversarial tasks such as swarm robotics and autonomous vehicle coordination, demand efficient decentralized collaboration under partial observability. While model-free multi-agent RL (MF-MARL) methods suffer from necessitating extensive environment interactions, most existing multi-agen
Object-goal navigation is a highly challenging task where an agent must navigate to a target solely based on visual observations. Current reinforcement learning-based methods for object-goal navigation face two major challenges: first, the agent lacks sufficient perception of environmental context i
Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches struggle with challenges such as inefficient exploration, poor generalization and inadequate deadlock resolution.
Anomaly detection has garnered significant attention for its extensive industrial application value. Most existing methods focus on single-view scenarios and fail to detect anomalies hidden in blind spots, leaving a gap in addressing the demands of multi-view detection in practical applications. Ens…
Stationary node data retrieval and adaptive sampling are two important tasks in marine environment monitoring based on autonomous underwater vehicles (AUVs). Enabling AUVs to carry out these two heterogeneous tasks within a single deployment is crucial for the efficient utilization of hardware resou
Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high c
DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention which is proven effective for improving performance but also introduces a…
Diffusion-based Video Super-Resolution (VSR) is renowned for generating perceptually realistic videos, yet it grapples with maintaining detail consistency across frames due to stochastic fluctuations. The traditional approach of pixel-level alignment is ineffective for diffusion-processed frames bec…
As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR n…
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) translation. Naively using 2D-I2I translation metho…
In this letter, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions,
Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs. One of the most common approaches for variable-rate is to channel- wisely or spatial-uniformly scale the internal features. How…
This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always as…
This letter focuses on information-theoretic active mapping and exploration with beam-based range sensing robots. Traditional works based on hand-engineered inverse sensor model (ISM) mapping or kernel inference methods lead to imbalanced accuracy and efficiency. This motivates us to propose a new a
This paper investigates the task coordination of multi-robot where each robot has a private individual temporal logic task specification; and also has to jointly satisfy a globally given collaborative temporal logic task specification. To efficiently generate feasible and optimized task execution pl…
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the…
Map building is a fundamental task in many robotic applications. In this paper, we propose a novel approach for 3D mapping of indoor environments which allows a robot avatar to collaborate with a human seamlessly through a virtual reality (VR) device. The 3D map is created using the 3D data from an…
This paper proposes and implements an auditory perception platform for a home service robot that serves the elderly living alone at home. A layered architecture for the proposed platform was developed to realize the various auditory perception capabilities while enabling a remote caregiver to involv…
Teaching robots manipulative skills through human demonstration is an important research problem and can be used to quickly program robots in future manufacturing industries. To understand human demonstration, manipulative actions need to be recognized. To improve the recognition performance, we use…