43 citations · 78 across the 25 of their papers we have counts for
12 papers · 1 filter
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation
Xinyang Huang, Chuang Zhu, Bowen Zhang +1
Semi-supervised domain adaptation (SSDA) has been widely studied due to its ability to utilize a few labeled target data to improve the generalization ability of the model. However…
Improve Vision Language Model Chain-of-thought Reasoning
Ruohong Zhang, Bowen Zhang, Yanghao Li +6
Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial for improving interpretability and trustworthiness. However, current training recipes lack robust CoT r…
Toward Physics-guided Time Series Embedding
Jiaxi Hu, Bowen Zhang, Qingsong Wen +2
In various scientific and engineering fields, the primary research areas have revolved around physics-based dynamical systems modeling and data-driven time series analysis. Accordi…
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Zhengfeng Lai, Vasileios Saveris, Chen Chen +9
Recent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. For example, while synthetic captions often…
MM-Ego: Towards Building Egocentric Multimodal LLMs for Video QA
Hanrong Ye, Haotian Zhang, Erik Daxberger +9
This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as th…
Contrastive Localized Language-Image Pre-Training
Hong-You Chen, Zhengfeng Lai, Haotian Zhang +7
Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. R…
EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing
Haotian Sun, Tao Lei, Bowen Zhang +5
Diffusion transformers have been widely adopted for text-to-image synthesis. While scaling these models up to billions of parameters shows promise, the effectiveness of scaling bey…
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang, Mingfei Gao, Zhe Gan +20
We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…
LSReGen: Large-Scale Regional Generator via Backward Guidance Framework
Bowen Zhang, Cheng Yang, Xuanhui Liu
In recent years, advancements in AIGC (Artificial Intelligence Generated Content) technology have significantly enhanced the capabilities of large text-to-image models. Despite the…
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +29
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. T…
Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models
Haotian Zhang, Haoxuan You, Philipp Dufter +8
While Ferret seamlessly integrates regional understanding into the Large Language Model (LLM) to facilitate its referring and grounding capability, it poses certain limitations: co…