11 citations · 14 across the 6 of their papers we have counts for
7 papers
SparseCoop: Cooperative Perception with Kinematic-Grounded Queries
Jiahao Wang, Zhongwei Jiang, Wenchao Sun +8
Cooperative perception is critical for autonomous driving, overcoming the inherent limitations of a single vehicle, such as occlusions and constrained fields-of-view. However, curr…
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
Wenwen Li, Sizhe Wang, Hyunho Lee +4
Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventi…
Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers
Chenyang Lu, Daan de Geus, Gijs Dubbelman
This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Tra…
Part-aware Panoptic Segmentation
Daan de Geus, Panagiotis Meletis, Chenyang Lu +2
In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifi…
Image-Graph-Image Translation via Auto-Encoding
Chenyang Lu, Gijs Dubbelman
This work presents the first convolutional neural network that learns an image-to-graph translation task without needing external supervision. Obtaining graph representations of im…
Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
Panagiotis Meletis, Xiaoxiao Wen, Chenyang Lu +2
In this technical report, we present two novel datasets for image scene understanding. Both datasets have annotations compatible with panoptic segmentation and additionally they ha…
Semantic Foreground Inpainting from Weak Supervision
Chenyang Lu, Gijs Dubbelman
Semantic scene understanding is an essential task for self-driving vehicles and mobile robots. In our work, we aim to estimate a semantic segmentation map, in which the foreground…