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Found 4 papers, 2 papers with code
TextHawk: Exploring Efficient Fine-Grained Perception of Multimodal Large Language Models
We conduct extensive experiments on both general and document-oriented MLLM benchmarks, and show that TextHawk outperforms the state-of-the-art methods, demonstrating its effectiveness and superiority in fine-grained document perception and general abilities.
Morphy: A Datamorphic Software Test Automation Tool
This paper focuses on the datamorphism combination strategies by giving their definitions and implementation algorithms.
DeepGeneMD: A Joint Deep Learning Model for Extracting Gene Mutation-Disease Knowledge from PubMed Literature
Understanding the pathogenesis of genetic diseases through different gene activities and their relations to relevant diseases is important for new drug discovery and drug repositioning.
Global Continuous Optimization with Error Bound and Fast Convergence
This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory.
