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Found 4 papers, 3 papers with code
Are We on the Right Way for Evaluating Large Vision-Language Models?
We evaluate 16 leading LVLMs on MMStar to assess their multi-modal capabilities, and on 7 benchmarks with the proposed metrics to investigate their data leakage and actual multi-modal gain.
SA$^2$VP: Spatially Aligned-and-Adapted Visual Prompt
Typical methods for visual prompt tuning follow the sequential modeling paradigm stemming from NLP, which represents an input image as a flattened sequence of token embeddings and then learns a set of unordered parameterized tokens prefixed to the sequence representation as the visual prompts for task adaptation of large vision models.
ShareGPT4V: Improving Large Multi-Modal Models with Better Captions
In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data.
Ranked #1 on
visual instruction following
on LLaVA-Bench
TSAM: Temporal Link Prediction in Directed Networks based on Self-Attention Mechanism
In this paper, we address the problem of temporal link prediction in directed networks and propose a deep learning model based on GCN and self-attention mechanism, namely TSAM.
