VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks

• URL: http://arxiv.org/abs/2407.19795v1
• Date: Mon, 29 Jul 2024 08:38:46 GMT
• Title: VolDoGer: LLM-assisted Datasets for Domain Generalization in Vision-Language Tasks
• Authors: Juhwan Choi, Junehyoung Kwon, JungMin Yun, Seunguk Yu, YoungBin Kim,
• Abstract summary: We propose VolDoGer: Vision-Language dataset for Domain Generalization. This dataset addresses three vision-language tasks: image captioning, visual question answering, and visual entailment. We extend LLM-based data annotation techniques to vision-language tasks, thereby alleviating the burden of recruiting human annotators.
• Score: 6.731844884087068
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Domain generalizability is a crucial aspect of a deep learning model since it determines the capability of the model to perform well on data from unseen domains. However, research on the domain generalizability of deep learning models for vision-language tasks remains limited, primarily because of the lack of required datasets. To address these challenges, we propose VolDoGer: Vision-Language Dataset for Domain Generalization, a dedicated dataset designed for domain generalization that addresses three vision-language tasks: image captioning, visual question answering, and visual entailment. We constructed VolDoGer by extending LLM-based data annotation techniques to vision-language tasks, thereby alleviating the burden of recruiting human annotators. We evaluated the domain generalizability of various models, ranging from fine-tuned models to a recent multimodal large language model, through VolDoGer.

Related papers

• Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models [27.45225442048711]
  We introduce CCMD-8M, which overcomes the data barriers of unifying vision-centric and vision-language tasks. We also present Griffon-G, a general large multimodal model that addresses both vision-centric and vision-language tasks within a single end-to-end paradigm.
  arXiv  Detail & Related papers  (2024-10-21T16:30:29Z)
• Pushing the Limits of Vision-Language Models in Remote Sensing without Human Annotations [5.065947993017157]
  This study introduces an approach to curate vision-language datasets by employing an image decoding machine learning model. We amassed approximately 9.6 million vision-language paired datasets in VHR imagery. The resultant model outperformed counterparts that did not leverage publicly available vision-language datasets.
  arXiv  Detail & Related papers  (2024-09-11T06:36:08Z)
• GenRL: Multimodal-foundation world models for generalization in embodied agents [12.263162194821787]
  Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. Current foundation vision-language models (VLMs) require fine-tuning or other adaptations to be adopted in embodied contexts. Lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications.
  arXiv  Detail & Related papers  (2024-06-26T03:41:48Z)
• Towards Vision-Language Geo-Foundation Model: A Survey [65.70547895998541]
  Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks. This paper thoroughly reviews VLGFMs, summarizing and analyzing recent developments in the field.
  arXiv  Detail & Related papers  (2024-06-13T17:57:30Z)
• Unveiling the Generalization Power of Fine-Tuned Large Language Models [81.70754292058258]
  We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs) Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
  arXiv  Detail & Related papers  (2024-03-14T08:18:59Z)
• Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions [11.786387517781328]
  Vision-Language Models (VLMs) are advanced models that can tackle more intricate tasks such as image captioning and visual question answering. Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs. We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible.
  arXiv  Detail & Related papers  (2024-02-20T18:57:34Z)
• Data-efficient Large Vision Models through Sequential Autoregression [58.26179273091461]
  We develop an efficient, autoregression-based vision model on a limited dataset. We demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint.
  arXiv  Detail & Related papers  (2024-02-07T13:41:53Z)
• General Object Foundation Model for Images and Videos at Scale [99.2806103051613]
  We present GLEE, an object-level foundation model for locating and identifying objects in images and videos. GLEE accomplishes detection, segmentation, tracking, grounding, and identification of arbitrary objects in the open world scenario. We employ an image encoder, text encoder, and visual prompter to handle multi-modal inputs, enabling to simultaneously solve various object-centric downstream tasks.
  arXiv  Detail & Related papers  (2023-12-14T17:26:00Z)
• The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation [56.61543110071199]
  Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target dataset. Previous approaches have attempted to address SFVUDA by leveraging self-supervision derived from the target data itself. We take an approach by exploiting "web-supervision" from Large Language-Vision Models (LLVMs), driven by the rationale that LLVMs contain a rich world prior surprisingly robust to domain-shift.
  arXiv  Detail & Related papers  (2023-08-17T18:12:05Z)
• PaLM-E: An Embodied Multimodal Language Model [101.29116156731762]
  We propose embodied language models to incorporate real-world continuous sensor modalities into language models. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks. Our largest model, PaLM-E-562B with 562B parameters, is a visual-language generalist with state-of-the-art performance on OK-VQA.
  arXiv  Detail & Related papers  (2023-03-06T18:58:06Z)
• Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data [0.0]
  We analyze the ability of pre-trained language models to transfer knowledge among datasets annotated with different type systems. Prediction of the rhetorical role a sentence plays in a case decision is an important and often studied task in AI & Law.
  arXiv  Detail & Related papers  (2021-12-15T04:23:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.