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Found 9 papers, 6 papers with code
Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey.
Elysium: Exploring Object-level Perception in Videos via MLLM
Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking, remains understudied.
Ranked #1 on
Zero-Shot Single Object Tracking
on LaSOT
Multi-task Learning for Low-resource Second Language Acquisition Modeling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned.
Unsupervised Question Answering via Answer Diversifying
Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models.
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network
The proposed model mainly focuses on the evidence selection phase of long document question answering.
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking
To alleviate the problem, it might be possible to generate long-document QA pairs via unsupervised question answering (UQA) methods.
SciMRC: Multi-perspective Scientific Machine Reading Comprehension
However, the dataset has ignored the fact that different readers may have different levels of understanding of the text, and only includes single-perspective question-answer pairs, leading to a lack of consideration of different perspectives.
Mix-Initiative Response Generation with Dynamic Prefix Tuning
Specifically, IDPT decouples initiative factors into different prefix parameters and uses the attention mechanism to adjust the selection of initiatives in guiding generation dynamically.
MedDr: Diagnosis-Guided Bootstrapping for Large-Scale Medical Vision-Language Learning
The rapid advancement of large-scale vision-language models has showcased remarkable capabilities across various tasks.
