3 accepted papers
Understanding charts requires models to jointly reason over geometric visual patterns, structured numerical data, and natural language -- a capability where current vision-language models (VLMs) remain limited. We introduce ChartNet, a high-quality, million-scale multimodal dataset designed to advan
In this work, we propose a novel framework for image segmentation guided by visual prompting which leverages the power of vision foundation models. Inspired by recent advancements in computer vision, our approach integrates multiple large-scale pretrained models to address the challenges of segment…
Deep neural networks achieve outstanding results for challenging image classification tasks. However, the design of network topologies is a complex task, and the research community is conducting ongoing efforts to discover top-accuracy topologies, either manually or by employing expensive architectu…