10 accepted papers
Current foundation model for photoplethysmography (PPG) signals is challenged by the intrinsic redundancy and noise of the signal. Standard masked modeling often yields trivial solutions while contrastive methods lack morphological precision. To address these limitations, we propose a Statistical-pr…
When aligning large language models (LLMs), their performance across various tasks (such as being helpful, harmless, and honest) is heavily influenced by the composition of the training data. However, it is difficult to determine what mixture of data should be used to produce a model with strong per…
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily relies on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic d…
Video Corpus Moment Retrieval (VCMR) is a challenging task that aims to localize query-specified moments from a collection of untrimmed videos. The recent state-of-the-art method, JSG, tries to tackle this task using only video-level annotations in a weakly-supervised setting. However, the late fusi…
Fine-tuning large language models (LLMs) faces significant memory challenges due to the high cost of back-propagation. MeZO addresses this using zeroth-order (ZO) optimization, matching memory usage to inference but suffering from slow convergence due to varying curvatures across model parameters. T
Partially Relevant Video Retrieval (PRVR) is a practical yet challenging task that involves retrieving videos based on queries relevant to only specific segments. While existing works follow the paradigm of developing models to process unimodal features, powerful pretrained vision-language models li
Video corpus moment retrieval (VCMR) is a challenging task aimed at localizing specific segments from untrimmed videos within a vast video collection. It has long been addressed using end-to-end supervised or weakly-supervised methods, which often lack explainability and rely on laborious annotation…
Due to the high complexity of a football match, the opponents' strategies are variable and unknown. Thus predicting the opponents' future intentions accurately based on current situation is crucial for football players' decision-making. To better anticipate the opponents and learn more effective str…
Sparse reward remains a valuable and challenging problem in multi-agent reinforcement learning (MARL). This paper addresses this issue from a new perspective, i.e., lazy agents. We empirically illustrate how lazy agents damage learning from both exploration and exploitation. Then, we propose a novel…
Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such sys…