Zobrazeno 1 - 10
of 841
pro vyhledávání: '"Su, Hung"'
Autor:
Su, Hung-Ting, Hsu, Ya-Ching, Lin, Xudong, Shi, Xiang-Qian, Niu, Yulei, Hsu, Han-Yuan, Lee, Hung-yi, Hsu, Winston H.
Large language models (LLMs) equipped with chain-of-thoughts (CoT) prompting have shown significant multi-step reasoning capabilities in factual content like mathematics, commonsense, and logic. However, their performance in narrative reasoning, whic
Externí odkaz:
http://arxiv.org/abs/2409.14324
The robust self-training (RST) framework has emerged as a prominent approach for semi-supervised adversarial training. To explore the possibility of tackling more complicated tasks with even lower labeling budgets, unlike prior approaches that rely o
Externí odkaz:
http://arxiv.org/abs/2409.12946
Pre-explored Semantic Maps, constructed through prior exploration using visual language models (VLMs), have proven effective as foundational elements for training-free robotic applications. However, existing approaches assume the map's accuracy and d
Externí odkaz:
http://arxiv.org/abs/2409.04837
Autor:
Faure, Gueter Josmy, Yeh, Jia-Fong, Chen, Min-Hung, Su, Hung-Ting, Hsu, Winston H., Lai, Shang-Hong
Existing research often treats long-form videos as extended short videos, leading to several limitations: inadequate capture of long-range dependencies, inefficient processing of redundant information, and failure to extract high-level semantic conce
Externí odkaz:
http://arxiv.org/abs/2408.17443
Autor:
Su, Hung-Ting, Chao, Chun-Tong, Hsu, Ya-Ching, Lin, Xudong, Niu, Yulei, Lee, Hung-Yi, Hsu, Winston H.
Large Language Models (LLMs) have demonstrated effectiveness not only in language tasks but also in video reasoning. This paper introduces a novel dataset, Tropes in Movies (TiM), designed as a testbed for exploring two critical yet previously overlo
Externí odkaz:
http://arxiv.org/abs/2406.10923
Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present
Externí odkaz:
http://arxiv.org/abs/2405.17507
Autor:
Yen, Ting-Kang, Morawski, Igor, Dangi, Shusil, He, Kai, Lin, Chung-Yi, Yeh, Jia-Fong, Su, Hung-Ting, Hsu, Winston
Event-based object detection has recently garnered attention in the computer vision community due to the exceptional properties of event cameras, such as high dynamic range and no motion blur. However, feature asynchronism and sparsity cause invisibl
Externí odkaz:
http://arxiv.org/abs/2403.18330
Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activities, or cellular traffic, on roadways as a proxy for vehicle flow. H
Externí odkaz:
http://arxiv.org/abs/2403.12991
Autor:
Yeh, Jia-Fong, Hung, Kuo-Han, Lo, Pang-Chi, Chung, Chi-Ming, Wu, Tsung-Han, Su, Hung-Ting, Chen, Yi-Ting, Hsu, Winston H.
We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas
Externí odkaz:
http://arxiv.org/abs/2402.03860
To address the limitations of traffic prediction from location-bound detectors, we present Geographical Cellular Traffic (GCT) flow, a novel data source that leverages the extensive coverage of cellular traffic to capture mobility patterns. Our exten
Externí odkaz:
http://arxiv.org/abs/2401.03138