Zobrazeno 1 - 10
of 443
pro vyhledávání: '"Wen Congcong"'
Remote Sensing Image Captioning (RSIC) presents unique challenges and plays a critical role in applications. Traditional RSIC methods often struggle to produce rich and diverse descriptions. Recently, with advancements in VLMs, efforts have emerged t
Externí odkaz:
http://arxiv.org/abs/2411.01595
Zero-Shot Object Goal Navigation (ZS-OGN) enables robots or agents to navigate toward objects of unseen categories without object-specific training. Traditional approaches often leverage categorical semantic information for navigation guidance, which
Externí odkaz:
http://arxiv.org/abs/2410.23978
We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic
Externí odkaz:
http://arxiv.org/abs/2410.21926
In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach i
Externí odkaz:
http://arxiv.org/abs/2410.21037
Autor:
Wen, Congcong, Huang, Yisiyuan, Huang, Hao, Huang, Yanjia, Yuan, Shuaihang, Hao, Yu, Lin, Hui, Liu, Yu-Shen, Fang, Yi
Object navigation is crucial for robots, but traditional methods require substantial training data and cannot be generalized to unknown environments. Zero-shot object navigation (ZSON) aims to address this challenge, allowing robots to interact with
Externí odkaz:
http://arxiv.org/abs/2410.18570
Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a
Externí odkaz:
http://arxiv.org/abs/2410.06549
Autor:
Wen, Congcong, Liu, Yifan, Bethala, Geeta Chandra Raju, Peng, Zheng, Lin, Hui, Liu, Yu-Shen, Fang, Yi
Robot navigation is an important research field with applications in various domains. However, traditional approaches often prioritize efficiency and obstacle avoidance, neglecting a nuanced understanding of human behavior or intent in shared spaces.
Externí odkaz:
http://arxiv.org/abs/2409.04965
LiDAR sensors are crucial for providing high-resolution 3D point cloud data in autonomous driving systems, enabling precise environmental perception. However, real-world adverse weather conditions, such as rain, fog, and snow, introduce significant n
Externí odkaz:
http://arxiv.org/abs/2408.13802
Autor:
Tian, Yu, Wen, Congcong, Shi, Min, Afzal, Muhammad Muneeb, Huang, Hao, Khan, Muhammad Osama, Luo, Yan, Fang, Yi, Wang, Mengyu
Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have introduced new methodologies and datasets in medical AI. However, the fair
Externí odkaz:
http://arxiv.org/abs/2407.08813
Autor:
Xiang, Siyuan, Tseng, Chin, Wen, Congcong, Desai, Deshana, Kou, Yifeng, Starly, Binil, Panozzo, Daniele, Feng, Chen
We introduce the first work on benchmarking and evaluating deep clustering algorithms on large-scale non-categorical 3D CAD models. We first propose a workflow to allow expert mechanical engineers to efficiently annotate 252,648 carefully sampled pai
Externí odkaz:
http://arxiv.org/abs/2404.19134