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Found 5 papers, 3 papers with code
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
To this end, we meticulously designed a differential video captioning strategy, which is stable, scalable, and efficient for generating captions for videos with arbitrary resolution, aspect ratios, and length.
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 #2 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.
