8 accepted papers
The rise of AI agents powered by large language models (LLMs) has transformed intelligent systems by enabling autonomous tool utilizing, reasoning, and action across diverse tasks. Despite this rapid progress, existing video summarization approaches primarily focus on feature extraction or frame-lev
Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, an…
The field of conditional Electrocardiogram(ECG) generation focuses on generating specified ECGs under given conditions for medical purposes. Existing methods are typically based on conditions of simple inputs like text or lead types. However, they struggle to handle the complexity of radar heart sou…
Synthetic data generation has emerged as a promising approach to enhance the reasoning capabilities of large language models. However, existing methods remain hindered by high costs—either through expensive API access or additional intermediate training—and are limited in their ability to generalize…
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains and current DG methods are default to the setting that data from source and target domains share identical categories. Nevertheless there exists unseen classes from target domains in practical scenarios.…
Deep neural network compression methods can produce small-scale networks and utilizes fine-tuning to get back the dropped accuracy. Despite their remarkable performance, the fine-tuning procedure is limited to the requirement of a huge training dataset, which is a time-consuming progress. To address…
In multimodal video event localization, we usually leverage feature fusion across different axes, such as the modality and temporal axes, for better context. To reduce the costs of detailed annotations, recent solutions explore weakly supervised settings. However, we observe that when feature fusion…
Many effective solutions have been proposed to reduce the redundancy of models for inference acceleration. Nevertheless, common approaches mostly focus on eliminating less important filters or constructing efficient operations, while ignoring the pattern redundancy in feature maps. We reveal that ma…