10 accepted papers
Multi-modal prompt learning is a parameter-efficient approach to adapt large vision--language models to downstream classification tasks. However, prompts can inadvertently evolve into a high-capacity pathway encoding environment-dependent spurious correlations that are only predictive in the source …
The rapid expansion of remote sensing technology has generated massive amounts of unlabeled multi-view data distributed across different institutions. Analyzing this data presents significant challenges, as centralized processing incurs prohibitive communication costs and raises data privacy concern…
Remote Sensing Image (RSI) segmentation has made significant strides, emerging as a leading solution for interpreting remote sensing data. However, due to the substantial domain gap between different remote sensors and limited computational resources, existing RSI segmentation methods often suffer f…
The scarcity of labeled data poses a significant challenge for deep learning-based medical image segmentation. To address this, this study introduces the novel Foundation Model-based Few-Shot Segmentation (FM-FSS) paradigm. FM-FSS capitalizes on the knowledge distilled from pre-trained foundation mo…
Large-scale text-to-image diffusion models, (e.g., DALL-E, SDXL) are capable of generating famous persons by simply referring to their names. Is it possible to make such models generate generic identities as simple as the famous ones, e.g., just use a name? In this paper, we explore the existence of…
Infrared-visible image fusion (IVF) and infrared-to-visible image translation (I2V) are two closely related tasks in multimodal image processing, both aimed at combining or transforming infrared and visible modalities to enhance image information content. Existing methods typically focus on either f…
Retrieval-Augmented Large Language Models(RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. W…
Deep neural networks possess substantial learning capacities and robust expressive power, making them prone to overfitting mislabeled data. Fortunately, the memorization effect shows that the networks tend to memorize the clean data first, and then gradually memorize the mislabeled data. Correspondi…
Visual Question Answering (VQA) models often prioritize language cues over visual knowledge, leading to the "language prior" phenomenon. To address this, researchers have proposed methods to balance language and image information during training and inference. However, these approaches often struggl…
Domain adversarial training is a popular approach for Unsupervised Domain Adaptation (DA). However, the transferability of adversarial training framework may drop greatly on the adaptation tasks with a large distribution divergence between source and target domains. In this paper, we propose a new a…