10 accepted papers
Continual learning (CL) in vision-language models (VLMs) faces significant challenges in improving task adaptation and avoiding catastrophic forgetting. Existing methods usually have heavy inference burden or rely on external knowledge, while Low-Rank Adaptation (LoRA) has shown potential in reducin
In the field of mixed-motive games, extensive multi-agent learning studies have explored the balance between egoism (individual interest), utilitarianism (collective interest), and egalitarianism (fairness). Traditional approaches often rely on manually designed reward functions, social norms, and a…
Our research focuses on few-shot fine-grained image classification (FS-FGIC), which faces two main challenges: the similarity of fine-grained objects and a limited number of samples. Traditional feature reconstruction networks enhance key features through spatial reconstruction and error minimizatio…
Understanding temporal dynamics is critical for conversational agents, enabling effective content analysis and informed decision-making. However, time-aware datasets, particularly for persona-grounded conversations, are still limited, which narrows their scope and diminishes their complexity. To add…
The seizure early warning devices based on multichannel EEG signals is one of the most used assisted-living strategies for drug-resistant epileptic patients. One of the challenges in the development of these devices is that existing algorithms cannot avoid the effects of electrode loosening. To alle…
Adversarial audio attacks pose a significant threat to the growing use of large audio-language models (LALMs) in voice-based human-machine interactions. While existing research focused on model-specific adversarial methods, real-world applications demand a more generalizable and universal approach t…
The remarkable multimodal capabilities demonstrated by OpenAI’s GPT-4 have sparked significant interest in the development of multimodal Large Language Models (LLMs). A primary research objective of such models is to align visual and textual modalities effectively while comprehending human instructi…
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive questions. This necessitates not only the parsing of temporal information within questions but also the identification and un…
In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet dis…
In this paper, we introduce a composable and extensible drone with tilting rotors (CEDTR). We aimed for a function that could optimally match the load capacity, degree of freedom (DOF), speed and endurance with diverse mission requirements by changing the quantity and form of combinations. First, we…