A framework that unifies image generation and retrieval tasks using Large Multimodal Models (LMMs).
Uses a training-free autoregressive manner to autonomously select the best-matched visual result for complex human queries.
Researchers typically provide these frameworks via arXiv or associated project pages. Download TIGER exe
It utilizes a dual-channel network to capture both structural properties of drugs and broad biomedical node relationships.
It uses a unique Frequency-Frame Interleaved (FFI) block to improve model performance while maintaining a small footprint. A framework that unifies image generation and retrieval
It enables models to recognize geometric requirements and synthesize code to invoke external specialized libraries for exact calculations.
A lightweight deep learning model designed for efficient . It utilizes a dual-channel network to capture both
This is a designed to predict drug interactions by exploiting heterogeneous graphs. It uses a "deep feature" approach by incorporating embeddings from both drug molecular graphs and biomedical knowledge graphs.