Author name cluster

Lingyu Kong

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

AAAI Conference 2025 Conference Paper

FOCUS: Towards Universal Foreground Segmentation

Zuyao You
Lingyu Kong
Lingchen Meng
Zuxuan Wu

Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unification. Moreover, they primarily focus on recognizing foreground objects without effectively distinguishing them from the background. In this paper, we emphasize the importance of the background and its relationship with the foreground. We introduce FOCUS, the Foreground ObjeCts Universal Segmentation framework that can handle multiple foreground tasks. We develop a multi-scale semantic network using the edge information of objects to enhance image features. To achieve boundary-aware segmentation, we propose a novel distillation method, integrating the contrastive learning strategy to refine the prediction mask in multi-modal feature space. We conduct extensive experiments on a total of 13 datasets across 5 tasks, and the results demonstrate that FOCUS consistently outperforms the state-of-the-art task-specific models on most metrics.

PDF Details DOI

ICRA Conference 2025 Conference Paper

Surface Roughness Estimation for Terrain Perception

Minxiang Ye
Yifei Zhang
Jason Jianjun Gu
Senwei Xiang
Lingyu Kong
Anhuan Xie

Ground terrain perception has become the primary visual task for the robust navigation of intelligent systems in unstructured outdoor environments. However, complex ter-rain poses a significant challenge to vision-based perception. This work introduces a novel estimation task using RGB images to facilitate low-cost terrain perception in extracting surface roughness information. The proposed task presents both semantic-aware and edge-aware roughness descriptors at the pixel level instead of a single value for a given image. To promote the research on the proposed novel terrain roughness estimation task, we introduce a multimodal synthetic dataset for terrain perception in outdoor scenes, containing multiple terrain categories, diverse viewpoints, different lighting and weather conditions, as well as semantic and roughness annotations. Additionally, inspired by computer graphics, we introduce TRENet, a roughness estimation architecture to model the intrinsic correlation of depth-normal-roughness. We also perform ablation studies on the effect of each component and diverse types of inputs. Extensive evaluations and comparisons demonstrate that our method can effectively predict pixel-wise terrain surface roughness with high accuracy.

Details