16 accepted papers
Detecting spoofing in financial trading is a critical data mining task. Traditional machine learning models often focus on individual node features, failing to capture the contextual relationships among interconnected nodes. Graph-based methodologies have enhanced this by effectively integrating rel
In human–robot interaction (HRI), maintaining a safe distance is the primary requirement. The flexibility and safety of HRI are often limited by the efficiency and accuracy of distance algorithms. This study proposes a fast and accurate method based on the Gilbert-Johnson-Keerthi (GJK) algorithm by
In the field of multimodal representation learning, existing research has primarily focused on exploring modal consistency and modal complementarity, while overlooking the positive role of modal difference. Moreover, modal difference establishes a bonding relationship between modal consistency and m…
In Versatile Video Coding (VVC), Rate-Distortion Optimized Quantization (RDOQ) is a widely adopted technique to strike a balance between bit rate and distortion. However, the computational complexity introduced by RDOQ poses significant challenges for real-time applications. To address this issue, w…
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. Math Word Problems (MWPs) serve as a crucial benchmark for evaluating LLMs’ reasoning abilities. While most research primarily focuses on improving accuracy, it often neglects understanding and addressing
Knowledge tracing uses students’ answer record data and the relationship between exercises to predict students’ future answering performance. However, in the deep learning model, the input manner of mini-batch may prevent the network from learning global information such as the difficulty of exercis…
Accurate mass estimation of table-top grown strawberries under field conditions remains challenging due to frequent occlusions and pose variations. This study proposes a vision-based pipeline integrating RGB-D sensing and deep learning to enable non-destructive, real-time and online mass estimation.
Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadru…
Multimodal machine learning is often hindered by two critical challenges: modality missingness and modality imbalance. These challenges significantly degrade the performance of multimodal models. The majority of existing methods either require the availability of full-modality data during the traini
In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficie…
Large language models (LLMs) are highly effective in various natural language processing (NLP) tasks. However, they are susceptible to producing unreliable conjectures in ambiguous contexts called hallucination. This paper presents a new method for evaluating LLM hallucination in Question Answering…
Clinical text summarization has proven successful in generating concise and coherent summaries. However, these summaries may include unintended text with hallucinations, which can mislead clinicians and patients. Existing methods for mitigating hallucinations can be categorized into task-specific an…
The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as sensor odometry and SLAM tasks. Impressive demos and benchmark scores have arisen, which may suggest the maturity of existing navigation tech
Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets of unseen relation types from unstructured texts, with a core challenge of training models to recognize new relations without labeled data. The seminal work handles this task by leveraging pre-trained language models t…
Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or famili…
Visual odometry is important for plenty of applications such as autonomous vehicles, and robot navigation. It is challenging to conduct visual odometry in textureless scenes or environments with sudden illumination changes where popular feature-based methods or direct methods cannot work well. To ad…