9 accepted papers
Visuo-tactile sensors have been widely adopted in robotic manipulation. However, inherent heterogeneity in sensor designs hinders the learning of unified tactile representations in cross-sensor scenarios. Existing methods that focus on reconstruction or task-specific supervision often fail to captur…
Low-Rank Adaptation (LoRA) has emerged as a promising paradigm for Continual Learning. It independently updates its low-rank factors ($A$ and $B$), creating a composite update to the full weight matrix through their interaction. To prevent catastrophic forgetting, this update should remain orthogona…
Precise spatial fidelity in Image-to-3D multi-instance generation is critical for downstream real-world applications. Recent work attempts to address this by fine-tuning pre-trained Image-to-3D (I23D) models on multi-instance datasets, which incurs substantial training overhead and struggles to guar…
The development of Multimodal Large Language Models (MLLMs) has seen significant progress, driven by increasing demands across various fields (e.g., multimodal agents, embodied intelligence). While model-driven approaches aim to enhance MLLM capabilities through diverse architectures, their performa…
Role-Playing Agents (RPAs), benefiting from large language models, is an emerging interactive AI system that simulates roles or characters with diverse personalities. However, existing methods primarily focus on mimicking dialogues among roles in textual form, neglecting the role’s voice traits (e.g…
Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning a tiny set of context vectors. Nevertheless, in this work, we reveal that freezing the parameters of VLMs during learnin…
The goal of Universal Cross-Domain Retrieval (UCDR) is to achieve robust performance in generalized test scenarios wherein data may belong to strictly unknown domains and categories during training. Recently pre-trained models with prompt tuning have shown strong generalization capabilities and atta…
Cross-modal retrieval aims to build correspondence between multiple modalities by learning a common representation space. Typically, an image can match multiple texts semantically and vice versa, which significantly increases the difficulty of this task. To address this problem, probabilistic embedd…
Transformer-based architectures with grid features represent the state-of-the-art in visual and language reasoning tasks, such as visual question answering and image-text matching. However, directly applying them to image captioning may result in spatial and fine-grained semantic information loss. T…