6 accepted papers
Cardiac ultrasound diagnosis is critical for cardiovascular disease assessment, but acquiring standard views remains highly operator-dependent. Existing medical segmentation models often yield anatomically inconsistent results in images with poor textural differentiation between distinct feature cla…
Recent advancements in Large Visual Language Models (LVLMs) have gained significant attention due to their remarkable reasoning capabilities and proficiency in generalization. However, processing a large number of visual tokens and generating long-context outputs impose substantial computational ove…
Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides limited coverage of complex chart scenarios and computation-intensive reasoning tasks prevalent in real-world applications
"Inspired by the remarkable achievements of Large Language Models (LLMs), Large Vision-Language Models (LVLMs) have likewise experienced significant advancements. However, the increased computational cost and token budget occupancy associated with lengthy visual tokens pose significant challenge to…
Radar semantic segmentation is a challenging task in environmental understanding, due as the radar data is noisy and suffers measurement ambiguities, which could lead to poor feature learning. To better tackle such difficulties, we present a novel and high-performance Transformer-based Radar Semanti…
Compared to conventional object detection which focuses on high-level image content, face manipulation detection pays more attention to low-level artifacts and temporal discrepancies. However, there are few methods considering both of these two characteristics. In this work, we propose a novel manip…