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pro vyhledávání: '"Zhao, Tong"'
Tiny object detection is becoming one of the most challenging tasks in computer vision because of the limited object size and lack of information. The label assignment strategy is a key factor affecting the accuracy of object detection. Although ther
Externí odkaz:
http://arxiv.org/abs/2407.02394
Associating unstructured data with structured information is crucial for real-world tasks that require relevance search. However, existing graph learning benchmarks often overlook the rich semantic information associate with each node. To bridge such
Externí odkaz:
http://arxiv.org/abs/2406.16321
The pervasive use of AI applications is increasingly influencing our everyday decisions. However, the ethical challenges associated with AI transcend conventional ethics and single-discipline approaches. In this paper, we propose aspirational ethical
Externí odkaz:
http://arxiv.org/abs/2404.14070
Road surface conditions, especially geometry profiles, enormously affect driving performance of autonomous vehicles. Vision-based online road reconstruction promisingly captures road information in advance. Existing solutions like monocular depth est
Externí odkaz:
http://arxiv.org/abs/2404.06605
In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. Autonomous mobile robots, in particular, have become integral to the modernization efforts in warehouses. One n
Externí odkaz:
http://arxiv.org/abs/2404.04832
Collaborative filtering (CF) has exhibited prominent results for recommender systems and been broadly utilized for real-world applications. A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong c
Externí odkaz:
http://arxiv.org/abs/2404.08660
Autor:
Shiao, William, Ju, Mingxuan, Guo, Zhichun, Chen, Xin, Papalexakis, Evangelos, Zhao, Tong, Shah, Neil, Liu, Yozen
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions of users. One common issue in real RS is the cold-start problem, where
Externí odkaz:
http://arxiv.org/abs/2403.18280
Autor:
Guo, Zhichun, Zhao, Tong, Liu, Yozen, Dong, Kaiwen, Shiao, William, Shah, Neil, Chawla, Nitesh V.
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite th
Externí odkaz:
http://arxiv.org/abs/2402.09711
Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of ri
Externí odkaz:
http://arxiv.org/abs/2402.08931
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in deep learning. However, their application to graph data poses distinct
Externí odkaz:
http://arxiv.org/abs/2402.08170