22 accepted papers
The Mixture-of-Experts (MoE) architecture is a powerful technique for scaling language models, yet it often suffers from expert homogenization, where experts learn redundant functionalities, thereby limiting MoE's full potential. To address this, we introduce Expert Divergence Learning, a novel pre-…
Multi-channel data fusion is essential for capturing comprehensive representations in complex systems. While graph convolutional networks have demonstrated remarkable efficacy, existing fusion paradigms primarily rely on discrete architectures governed by first-order propagation. These models are ty
Underwater images commonly suffer from foreground-background ambiguity, loss of structural details, and severely reduced contrast, which collectively make underwater object detection (UOD) an inherently challenging task. To handle this issue, we present a residual-guided hierarchical calibration net
Open-vocabulary semantic segmentation extends pixel-level recognition to arbitrary text-described categories. Despite strong global semantic understanding, vision-language models such as CLIP exhibit limited spatial precision and semantic ambiguity across large vocabularies, constraining their effec
Dual-arm robots possess exceptional collaborative capabilities and versatility, demonstrating broad application prospects across various fields. As a significant research area for dual-arm robots, the requirements for coordinated motion control are gradually increasing. In practical applications, ro
Vision-and-Language Navigation in Continuous Environments (VLN-CE) presents challenges due to environmental variations and domain shifts, making it difficult for agents to generalize beyond seen environments. Most existing methods rely on learning correlations between observations and actions from t
The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made significant advancements in machine translation, their capacity for translating numbers has not been thoroughly explore…
Pedestrian trajectory prediction ensures safe navigation in autonomous driving and intelligent robots. Existing methods have shown promising results but still face challenges in handling dynamic environments, social interactions, and high-dimensional data. In this paper, we propose a novel PhysGCN-D
In this paper we focus on capturing closely interacted two-person motions from monocular videos an important yet understudied topic. Unlike less-interacted motions closely interacted motions contain frequently occurring inter-human occlusions which pose significant challenges to existing capturing a…
With the evolution of LLMs, they are endowed with impressive logical reasoning, or vertical thinking capabilities. But can they think out of the box? Do they possess proficient lateral thinking abilities? Following the setup of Lateral Thinking Puzzles, we propose a novel evaluation benchmark, LatEv…
Vision-and-Language Navigation (VLN) in continuous environments aims to navigate robot agents in unseen environments following natural language instructions. The majority of existing approaches rely on constructing semantic maps or topological maps to record information. However, semantic maps overl…
Collision-free navigation is a critical issue in robotic systems as the environment is often dynamic and uncertain. This paper investigates a data-stream-driven motion control problem for mobile robots to avoid randomly moving obstacles when the probability distribution of the obstacle’s movement is…
Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, because CLIP alone cannot provide enough guidance in…
Despite various probabilistic methods for modeling the uncertainty and ambiguity in human mesh recovery, their overall precision is limited because existing formulations for joint rotations are either not constrained to SO(3) or difficult to learn for neural networks. To address such an issue, we de…
This letter addresses the problem of designing an interception strategy for an underactuated uncrewed surface vessel (USV) in the presence of uncertain external disturbances and unknown internal parameters i.e., linear and nonlinear damping coefficients, vehicle mass, etc. The interception strategy
This letter addresses the robust trajectory tracking problem for a Quadrotor-Slung-Load System (QSLS), which consists of a point-mass load and a rigid-body quadrotor connected by an inelastic cable. To construct the controller, we employ the backstepping technique and propose an Uncertainty and Dist
In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution…
"Existing one-shot face reenactment methods either present obvious artifacts in large pose transformations, or cannot well-preserve the identity information in the source images, or fail to meet the requirements of real-time applications due to the intensive amount of computation involved. In this p…
This paper addresses the flexible formation problem for unmanned surface vehicles in the presence of obstacles. Building upon the leader-follower formation scheme, a hybrid line-of-sight based flexible platooning method is proposed for follower vehicle to keep tracking the leader ship. A fusion arti…
Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete…
For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches. The network is stabilized by distribution matching of key statistical features at multiple scales. Unlike the existing methods which impose mo…
The task of room layout estimation is to locate the wall-floor, wall-ceiling, and wall-wall boundaries. Most recent methods solve this problem based on edge/keypoint detection or semantic segmentation. However, these approaches have shown limited attention on the geometry of the dominant planes and…