How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey

• URL: http://arxiv.org/abs/2412.08158v1
• Date: Wed, 11 Dec 2024 07:29:04 GMT
• Title: How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey
• Authors: Yayun Qi, Hongxi Li, Yiqi Song, Xinxiao Wu, Jiebo Luo,
• Abstract summary: In recent years, the rise of pre-trained models is driving the research on vision-language tasks. Inspired by the powerful capabilities of pre-trained models, new paradigms have emerged to solve the classic challenges.
• Score: 59.23394353614928
• License:
• Abstract: The exploration of various vision-language tasks, such as visual captioning, visual question answering, and visual commonsense reasoning, is an important area in artificial intelligence and continuously attracts the research community's attention. Despite the improvements in overall performance, classic challenges still exist in vision-language tasks and hinder the development of this area. In recent years, the rise of pre-trained models is driving the research on vision-language tasks. Thanks to the massive scale of training data and model parameters, pre-trained models have exhibited excellent performance in numerous downstream tasks. Inspired by the powerful capabilities of pre-trained models, new paradigms have emerged to solve the classic challenges. Such methods have become mainstream in current research with increasing attention and rapid advances. In this paper, we present a comprehensive overview of how vision-language tasks benefit from pre-trained models. First, we review several main challenges in vision-language tasks and discuss the limitations of previous solutions before the era of pre-training. Next, we summarize the recent advances in incorporating pre-trained models to address the challenges in vision-language tasks. Finally, we analyze the potential risks associated with the inherent limitations of pre-trained models and discuss possible solutions, attempting to provide future research directions.

Related papers

• A Survey on Vision Autoregressive Model [15.042485771127346]
  Autoregressive models have demonstrated impressive performance in natural language processing (NLP) Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods.
  arXiv  Detail & Related papers  (2024-11-13T14:59:41Z)
• ReVLA: Reverting Visual Domain Limitation of Robotic Foundation Models [55.07988373824348]
  We study the visual generalization capabilities of three existing robotic foundation models. Our study shows that the existing models do not exhibit robustness to visual out-of-domain scenarios. We propose a gradual backbone reversal approach founded on model merging.
  arXiv  Detail & Related papers  (2024-09-23T17:47:59Z)
• VIPriors 4: Visual Inductive Priors for Data-Efficient Deep Learning Challenges [12.615348941903594]
  Fourth edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop features two data-impaired challenges. These challenges address the problem of training deep learning models for computer vision tasks with limited data. We aim to stimulate the development of novel approaches that incorporate inductive biases to improve the data efficiency of deep learning models.
  arXiv  Detail & Related papers  (2024-06-26T08:50:51Z)
• Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models [7.736445799116692]
  In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values. We introduce a post-hoc method that utilizes emphdeep reinforcement learning to explore and construct the landscape of failure modes in pre-trained discriminative and generative models. We empirically show the effectiveness of the proposed method across common Computer Vision, Natural Language Processing, and Vision-Language tasks.
  arXiv  Detail & Related papers  (2024-06-11T10:45:41Z)
• Trends, Applications, and Challenges in Human Attention Modelling [65.61554471033844]
  Human attention modelling has proven to be particularly useful for understanding the cognitive processes underlying visual exploration. It provides support to artificial intelligence models that aim to solve problems in various domains, including image and video processing, vision-and-language applications, and language modelling.
  arXiv  Detail & Related papers  (2024-02-28T19:35:30Z)
• 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)
• Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies? [0.06299766708197882]
  We create a new task targeted at evaluating understanding of predicate-noun dependencies in a controlled setup. We evaluate a range of state-of-the-art models and find that their performance on the task varies considerably. This study highlights that targeted and controlled evaluations are a crucial step for a precise and rigorous test of the multimodal knowledge of vision-and-language models.
  arXiv  Detail & Related papers  (2022-10-21T16:07:00Z)
• Vision-and-Language Pretraining [44.253982520038804]
  This article provides a comprehensive revision of contemporary V&L pretraining models. In particular, we categorize and delineate pretraining approaches, along with the summary of state-of-the-art vision-and-language pretrained models.
  arXiv  Detail & Related papers  (2022-07-05T02:18:49Z)
• Causal Reasoning Meets Visual Representation Learning: A Prospective Study [117.08431221482638]
  Lack of interpretability, robustness, and out-of-distribution generalization are becoming the challenges of the existing visual models. Inspired by the strong inference ability of human-level agents, recent years have witnessed great effort in developing causal reasoning paradigms. This paper aims to provide a comprehensive overview of this emerging field, attract attention, encourage discussions, bring to the forefront the urgency of developing novel causal reasoning methods.
  arXiv  Detail & Related papers  (2022-04-26T02:22:28Z)
• Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models [65.19308052012858]
  Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research. We present VALUE, a set of meticulously designed probing tasks to decipher the inner workings of multimodal pre-training. Key observations: Pre-trained models exhibit a propensity for attending over text rather than images during inference.
  arXiv  Detail & Related papers  (2020-05-15T01:06:54Z)

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.