178 citations · 629 across the 44 of their papers we have counts for
8 papers · 1 filter
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
Le Xue, Manli Shu, Anas Awadalla +30
This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…
Task Me Anything
Jieyu Zhang, Weikai Huang, Zixian Ma +7
Benchmarks for large multimodal language models (MLMs) now serve to simultaneously assess the general capabilities of models instead of evaluating for a specific capability. As a r…
Iterated Learning Improves Compositionality in Large Vision-Language Models
Chenhao Zheng, Jieyu Zhang, Aniruddha Kembhavi +1
A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and lang…
m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks
Zixian Ma, Weikai Huang, Jieyu Zhang +2
Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-a…
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Chengkai Hou, Jieyu Zhang, Tianyi Zhou
Data augmentation (DA) is widely used to improve the generalization of neural networks by enforcing the invariances and symmetries to pre-defined transformations applied to input d…
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality
Cheng-Yu Hsieh, Jieyu Zhang, Zixian Ma +2
In the last year alone, a surge of new benchmarks to measure compositional understanding of vision-language models have permeated the machine learning ecosystem. Given an image, th…
Subclass-balancing Contrastive Learning for Long-tailed Recognition
Chengkai Hou, Jieyu Zhang, Haonan Wang +1
Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and s…
DataComp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang +31
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model arch…