9 accepted papers
Existing image reflection removal methods struggle to handle complex reflections. Accurate language descriptions can help the model understand the image content to remove complex reflections. However, due to blurred and distorted interferences in reflected images, machine-generated language descript
Drug discovery (DD) has tremendously contributed to maintaining and improving public health. Hypothesizing that inhibiting protein misfolding can slow disease progression, researchers focus on target identification (Target ID) to find protein structures for drug binding. While Large Language Models…
Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning. While recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation, adapting these capabilities to 3D scenes remains underexplored. In…
The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional…
The integration of language and 3D perception is critical for embodied AI and robotic systems to perceive, understand, and interact with the physical world. Spatial reasoning, a key capability for understanding spatial relationships between objects, remains underexplored in current 3D vision-languag…
Vision transformers (ViTs) have been outstanding in multiple dense prediction tasks, including image matting. However, the high computational and training costs of ViTs lead to a bottleneck for applications on low computing power devices. In this paper, we propose a novel transformer-specific knowle…
Most of the previous exposure correction methods learn dense pixel-wise transformations to achieve promising results but consume huge computational resources. Recently Learnable 3D lookup tables (3D LUTs) have demonstrated impressive performance and efficiency for image enhancement. However these me…
Most existing portrait matting models either require expensive auxiliary information or try to decompose the task into sub-tasks that are usually resource-hungry. These challenges limit its application on low-power computing devices. In this paper, we propose an ultra-light-weighted portrait matting…
Most existing human matting algorithms tried to separate pure human-only foreground from the background. In this paper, we propose a Virtual Multi-modality Foreground Matting (VMFM) method to learn human-object interactive foreground (human and objects interacted with him or her) from a raw RGB imag…