71 citations · 267 across the 24 of their papers we have counts for
24 papers
MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos
Xuehai He, Weixi Feng, Kaizhi Zheng +11
Multimodal Language Language Models (MLLMs) demonstrate the emerging abilities of "world models" -- interpreting and reasoning about complex real-world dynamics. To assess these ab…
MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding
Zekun Li, Xianjun Yang, Kyuri Choi +11
Scientific figure interpretation is a crucial capability for AI-driven scientific assistants built on advanced Large Vision Language Models. However, current datasets and benchmark…
Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models
Wanrong Zhu, Jennifer Healey, Ruiyi Zhang +2
Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability. In graphic design, non…
List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs
An Yan, Zhengyuan Yang, Junda Wu +8
Set-of-Mark (SoM) Prompting unleashes the visual grounding capability of GPT-4V, by enabling the model to associate visual objects with tags inserted on the image. These tags, mark…
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
An Yan, Zhengyuan Yang, Wanrong Zhu +9
We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical user interface (GUI) navigation task. MM-Navigator can interact with a smartphone screen as human users,…
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadalla, Irena Gao, Josh Gardner +13
We introduce OpenFlamingo, a family of autoregressive vision-language models ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce an open-source replicati…
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
Yonatan Bitton, Hritik Bansal, Jack Hessel +6
We introduce VisIT-Bench (Visual InsTruction Benchmark), a benchmark for evaluation of instruction-following vision-language models for real-world use. Our starting point is curati…
VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View
Raphael Schumann, Wanrong Zhu, Weixi Feng +3
Incremental decision making in real-world environments is one of the most challenging tasks in embodied artificial intelligence. One particularly demanding scenario is Vision and L…
Multimodal Procedural Planning via Dual Text-Image Prompting
Yujie Lu, Pan Lu, Zhiyu Chen +3
Embodied agents have achieved prominent performance in following human instructions to complete tasks. However, the potential of providing instructions informed by texts and images…
LayoutGPT: Compositional Visual Planning and Generation with Large Language Models
Weixi Feng, Wanrong Zhu, Tsu-jui Fu +6
Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on…
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation
Wanrong Zhu, Xinyi Wang, Yujie Lu +4
The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models,…
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text
Wanrong Zhu, Jack Hessel, Anas Awadalla +7
In-context vision and language models like Flamingo support arbitrarily interleaved sequences of images and text as input. This format not only enables few-shot learning via interl…
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
Xinyi Wang, Wanrong Zhu, Michael Saxon +2
In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context le…
CLIP also Understands Text: Prompting CLIP for Phrase Understanding
An Yan, Jiacheng Li, Wanrong Zhu +3
Contrastive Language-Image Pretraining (CLIP) efficiently learns visual concepts by pre-training with natural language supervision. CLIP and its visual encoder have been explored o…
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Wanrong Zhu, An Yan, Yujie Lu +4
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context. On the other hand, when generating text, human writers are gifted…
Neuro-Symbolic Procedural Planning with Commonsense Prompting
Yujie Lu, Weixi Feng, Wanrong Zhu +4
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for human…
Imagination-Augmented Natural Language Understanding
Yujie Lu, Wanrong Zhu, Xin Eric Wang +2
Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations. Such abilities ena…
End-to-end Dense Video Captioning as Sequence Generation
Wanrong Zhu, Bo Pang, Ashish V. Thapliyal +2
Dense video captioning aims to identify the events of interest in an input video, and generate descriptive captions for each event. Previous approaches usually follow a two-stage g…
ImaginE: An Imagination-Based Automatic Evaluation Metric for Natural Language Generation
Wanrong Zhu, Xin Eric Wang, An Yan +2
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references. This differs from human language…
Diagnosing Vision-and-Language Navigation: What Really Matters
Wanrong Zhu, Yuankai Qi, Pradyumna Narayana +6
Vision-and-language navigation (VLN) is a multimodal task where an agent follows natural language instructions and navigates in visual environments. Multiple setups have been propo…
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations
Wanrong Zhu, Xin Eric Wang, Pradyumna Narayana +3
A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is criti…
Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
Wanrong Zhu, Xin Eric Wang, Tsu-Jui Fu +5
One of the most challenging topics in Natural Language Processing (NLP) is visually-grounded language understanding and reasoning. Outdoor vision-and-language navigation (VLN) is s…
Text Infilling
Wanrong Zhu, Zhiting Hu, Eric Xing
Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of r…
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu, Haoran Shi, Bowen Tan +12
We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, su…