7 citations · 31 across the 43 of their papers we have counts for
12 papers · 2 filters
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training
Ziqi Gao, Weikai Huang, Jieyu Zhang +2
Recent advances in text-to-vision generation excel in visual fidelity but struggle with compositional generalization and semantic alignment. Existing datasets are noisy and weakly…
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
Arijit Ray, Jiafei Duan, Ellis Brown +9
Reasoning about motion and space is a fundamental cognitive capability that is required by multiple real-world applications. While many studies highlight that large multimodal lang…
LATTE: Learning to Think with Vision Specialists
Zixian Ma, Jianguo Zhang, Zhiwei Liu +9
While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabiliti…
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Baiqi Li, Zhiqiu Lin, Wenxuan Peng +7
Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are the…
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…
The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better
Scott Geng, Cheng-Yu Hsieh, Vivek Ramanujan +4
Generative text-to-image models enable us to synthesize unlimited amounts of images in a controllable manner, spurring many recent efforts to train vision models with synthetic dat…
Multilingual Diversity Improves Vision-Language Representations
Thao Nguyen, Matthew Wallingford, Sebastin Santy +5
Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on s…
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…
SPARO: Selective Attention for Robust and Compositional Transformer Encodings for Vision
Ankit Vani, Bac Nguyen, Samuel Lavoie +2
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under dis…
Videoshop: Localized Semantic Video Editing with Noise-Extrapolated Diffusion Inversion
Xiang Fan, Anand Bhattad, Ranjay Krishna
We introduce Videoshop, a training-free video editing algorithm for localized semantic edits. Videoshop allows users to use any editing software, including Photoshop and generative…
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…