REVIEW 4 cited by
LAVIS: A Library for Language-Vision Intelligence
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS.
Forward citations
Cited by 4 Pith papers
-
ART: Adaptive Relation Tuning for Generalized Relation Prediction
ART adapts VLMs for visual relation classification via instruction tuning with adaptive, uncertainty-based instance selection, improving generalization to unseen and rare relations.
-
FREE: Fast and Robust Vision Language Models with Early Exits
An adversarial early-exit method for frozen-backbone vision language models that reuses the final classifier and reports 1.5x inference speedup with comparable accuracy.
-
Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models
Argus fuses multi-view images and camera poses with 3D point cloud features in a frozen-LLM Q-Former architecture, improving 3D question answering, grounding, and scene description over prior 3D-LMMs.
-
IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truth
A visual heatmap tool lets people rate vision-language model reliability in video by inspecting patterns of green and red cells, with user ratings tracking objective F1 scores when those exist.
Discussion (0). Sign in to comment.