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Generative AI based on foundation models provides a first glimpse into the world represented by machines trained on vast amounts of multimodal data ingested by these models during training. If we consider the resulting models as knowledge…

Computers and Society · Computer Science 2024-04-12 Zilong Liu , Krzysztof Janowicz , Kitty Currier , Meilin Shi

We propose an efficient method to ground pretrained text-only language models to the visual domain, enabling them to process arbitrarily interleaved image-and-text data, and generate text interleaved with retrieved images. Our method…

Computation and Language · Computer Science 2023-06-16 Jing Yu Koh , Ruslan Salakhutdinov , Daniel Fried

We present Thinking with Generated Images, a novel paradigm that fundamentally transforms how large multimodal models (LMMs) engage with visual reasoning by enabling them to natively think across text and vision modalities through…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Ethan Chern , Zhulin Hu , Steffi Chern , Siqi Kou , Jiadi Su , Yan Ma , Zhijie Deng , Pengfei Liu

Large natural language models (such as GPT-3 or T5) demonstrate impressive abilities across a range of general NLP tasks. Here, we show that the knowledge embedded in such models provides a useful inductive bias, not just on traditional NLP…

Computation and Language · Computer Science 2021-10-07 Christopher Michael Rytting , David Wingate

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms. We present a unified generative method to acquire a shared semantic/visual embedding that enables the learning…

Computation and Language · Computer Science 2021-08-02 Nisha Pillai , Cynthia Matuszek , Francis Ferraro

Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the…

Computation and Language · Computer Science 2020-10-16 Ana Marasović , Chandra Bhagavatula , Jae Sung Park , Ronan Le Bras , Noah A. Smith , Yejin Choi

The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous…

Computer Vision and Pattern Recognition · Computer Science 2023-10-03 Deyao Zhu , Jun Chen , Xiaoqian Shen , Xiang Li , Mohamed Elhoseiny

Image captioning as a multimodal task has drawn much interest in recent years. However, evaluation for this task remains a challenging problem. Existing evaluation metrics focus on surface similarity between a candidate caption and a set of…

Computation and Language · Computer Science 2019-12-20 Huiyuan Xie , Tom Sherborne , Alexander Kuhnle , Ann Copestake

Visual grounding tasks aim to localize image regions based on natural language references. In this work, we explore whether generative VLMs predominantly trained on image-text data could be leveraged to scale up the text annotation of…

Computer Vision and Pattern Recognition · Computer Science 2024-07-23 Shijie Wang , Dahun Kim , Ali Taalimi , Chen Sun , Weicheng Kuo

The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of…

Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typical workflows in visualization and visual analytics,…

Human-Computer Interaction · Computer Science 2024-09-10 Alexandra Diehl , Alfie Abdul-Rahman , Benjamin Bach , Mennatallah El-Assady , Matthias Kraus , Robert S. Laramee , Daniel A. Keim , Min Chen

Joint video-language learning has received increasing attention in recent years. However, existing works mainly focus on single or multiple trimmed video clips (events), which makes human-annotated event boundaries necessary during…

Computer Vision and Pattern Recognition · Computer Science 2023-05-18 Teng Wang , Jinrui Zhang , Feng Zheng , Wenhao Jiang , Ran Cheng , Ping Luo

Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which is essential for…

Computer Vision and Pattern Recognition · Computer Science 2026-02-24 Yubin Wang , Xinyang Jiang , De Cheng , Xiangqian Zhao , Zilong Wang , Dongsheng Li , Cairong Zhao

Textual grounding, i.e., linking words to objects in images, is a challenging but important task for robotics and human-computer interaction. Existing techniques benefit from recent progress in deep learning and generally formulate the task…

Computer Vision and Pattern Recognition · Computer Science 2018-03-30 Raymond A. Yeh , Minh N. Do , Alexander G. Schwing

Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise…

To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called…

Current word embedding models despite their success, still suffer from their lack of grounding in the real world. In this line of research, Gunther et al. 2022 proposed a behavioral experiment to investigate the relationship between words…

Computation and Language · Computer Science 2023-11-01 Hassan Shahmohammadi , Maria Heitmeier , Elnaz Shafaei-Bajestan , Hendrik P. A. Lensch , Harald Baayen

Image captioning, which generates natural language descriptions of the visual information in an image, is a crucial task in vision-language research. Previous models have typically addressed this task by aligning the generative capabilities…

Computer Vision and Pattern Recognition · Computer Science 2024-09-02 Qian Cao , Xu Chen , Ruihua Song , Xiting Wang , Xinting Huang , Yuchen Ren

The upsurge in pre-trained large models started by ChatGPT has swept across the entire deep learning community. Such powerful models demonstrate advanced generative ability and multimodal understanding capability, which quickly set new…

Computer Vision and Pattern Recognition · Computer Science 2025-02-28 Ning Ding , Yehui Tang , Zhongqian Fu , Chao Xu , Kai Han , Yunhe Wang

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with…