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Found 4 papers, 2 papers with code
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #42 on
Visual Question Answering
on MM-Vet
CARLS: Cross-platform Asynchronous Representation Learning System
In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
Unifying Specialist Image Embedding into Universal Image Embedding
Therefore, we propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem.
Graph-RISE: Graph-Regularized Image Semantic Embedding
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
Ranked #11 on
Image Classification
on iNaturalist
