5 papers
Firebolt-VL: Efficient Vision-Language Understanding with Cross-Modality Modulation
Quoc-Huy Trinh, Mustapha Abdullahi, Bo Zhao +1
Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment i…
Viper-F1: Fast and Fine-Grained Multimodal Understanding with Cross-Modal State-Space Modulation
Quoc-Huy Trinh
Recent advances in multimodal large language models (MLLMs) have enabled impressive progress in vision-language understanding, yet their high computational cost limits deployment i…
Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentation
Juntong Fan, Shuyi Fan, Debesh Jha +4
Accurate endoscopic image segmentation on the polyps is critical for early colorectal cancer detection. However, this task remains challenging due to low contrast with surrounding…
FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention
Jun Zeng, KC Santosh, Deepak Rajan Nayak +4
Colonoscopy is vital in the early diagnosis of colorectal polyps. Regular screenings can effectively prevent benign polyps from progressing to CRC. While deep learning has made imp…
L2GNet: Optimal Local-to-Global Representation of Anatomical Structures for Generalized Medical Image Segmentation
Vandan Gorade, Sparsh Mittal, Neethi Dasu +3
Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast, Synergistic Continuous and…