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Found 6 papers, 4 papers with code
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
Charts play a vital role in data visualization, understanding data patterns, and informed decision-making.
Foundation Model is Efficient Multimodal Multitask Model Selector
This paper investigates an under-explored but important problem: given a collection of pre-trained neural networks, predicting their performance on each multi-modal task without fine-tuning them, such as image recognition, referring, captioning, visual question answering, and text question answering.
Tiny LVLM-eHub: Early Multimodal Experiments with Bard
Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach.
LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning.
QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings
This paper reports the details of our submissions in the task 1 of SemEval 2017.
QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base
The techniques of our runs mainly make use of the word embeddings and the knowledge-based method.
