Zobrazeno 1 - 10
of 995
pro vyhledávání: '"Takeda Kazuya"'
Predicting pedestrian behavior is challenging yet crucial for applications such as autonomous driving and smart city. Recent deep learning models have achieved remarkable performance in making accurate predictions, but they fail to provide explanatio
Externí odkaz:
http://arxiv.org/abs/2410.12195
Pedestrian action prediction is of great significance for many applications such as autonomous driving. However, state-of-the-art methods lack explainability to make trustworthy predictions. In this paper, a novel framework called MulCPred is propose
Externí odkaz:
http://arxiv.org/abs/2409.09446
Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. To mitigate driving hazards and ensure personal safety, it is crucial to assist vehicles in anticipating important objects during travel. Previous
Externí odkaz:
http://arxiv.org/abs/2311.06497
Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in
Externí odkaz:
http://arxiv.org/abs/2310.17193
Publikováno v:
Multimedia Tools and Applications, 2024, 1-17
In many sports, player re-identification is crucial for automatic video processing and analysis. However, most of the current studies on player re-identification in multi- or single-view sports videos focus on re-identification in the closed-world se
Externí odkaz:
http://arxiv.org/abs/2310.11700
General-purpose mobile robots need to complete tasks without exact human instructions. Large language models (LLMs) is a promising direction for realizing commonsense world knowledge and reasoning-based planning. Vision-language models (VLMs) transfo
Externí odkaz:
http://arxiv.org/abs/2310.04981
This study introduces a novel training paradigm, audio difference learning, for improving audio captioning. The fundamental concept of the proposed learning method is to create a feature representation space that preserves the relationship between au
Externí odkaz:
http://arxiv.org/abs/2309.08141
Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainabil
Externí odkaz:
http://arxiv.org/abs/2307.09050
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game situation and make decisions. Previous studies using deep reinforcement l
Externí odkaz:
http://arxiv.org/abs/2305.17886