6 accepted papers
Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the de
Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep learning have undeniably brought about substantial performance improvements in these fields. Nevertheless, existing methods mainly rely on task-specific ar
Vanilla convolution and window-based self-attention have shown significant success in image dehazing. However, they are constrained by limited receptive fields and ignore frequency gaps between dehazed and clear images. The former hampers the modeling of global dependencies, while the latter impedes…
High dynamic range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded vision are trained on low dynamic range (LDR) inputs and suffer substantial per…
Multimodal learning significantly benefits cancer survival prediction, especially the integration of pathological images and genomic data. Despite advantages of multimodal learning for cancer survival prediction, massive redundancy in multimodal data prevents it from extracting discriminative and co…
In this paper we propose a novel model towards multi-frame super-resolution, which leverages multiple RAW images and yields a super-resolved RGB image. To facilitate the pixel misalignment in burst photography, we apply a refined Pyramid Cascading and Deformable Convolution (PCD) feature alignment m…