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Vision-Language Models for Vision Tasks: A Survey

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arxiv 2304.00685 v2 pith:6TUBGWPC submitted 2023-04-03 cs.CV

Jingyi Zhang , Jiaxing Huang , Sheng Jin , Shijian Lu This is my paper
classification cs.CV
keywords visualrecognitionmethodstasksmodelspre-trainingsurveyvision-language
verification ladder T0 review T1 audit T2 compute T3 formal
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Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laborious and time-consuming visual recognition paradigm. To address the two challenges, Vision-Language Models (VLMs) have been intensively investigated recently, which learns rich vision-language correlation from web-scale image-text pairs that are almost infinitely available on the Internet and enables zero-shot predictions on various visual recognition tasks with a single VLM. This paper provides a systematic review of visual language models for various visual recognition tasks, including: (1) the background that introduces the development of visual recognition paradigms; (2) the foundations of VLM that summarize the widely-adopted network architectures, pre-training objectives, and downstream tasks; (3) the widely-adopted datasets in VLM pre-training and evaluations; (4) the review and categorization of existing VLM pre-training methods, VLM transfer learning methods, and VLM knowledge distillation methods; (5) the benchmarking, analysis and discussion of the reviewed methods; (6) several research challenges and potential research directions that could be pursued in the future VLM studies for visual recognition. A project associated with this survey has been created at https://github.com/jingyi0000/VLM_survey.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. One Model, Many Graphs: Learning over Attributed Graphs across Heterogeneous Modalities with Vision-Language Models

    cs.LG 2026-07 conditional novelty 6.0 of 10

    OMG-VLM is a single VLM-based model that handles text-, image-, and multi-attributed graphs through structure-aware adapters, reporting gains on several node/link prediction benchmarks.

  2. Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings

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    A robot using GPT-4o labeled hazards in a simulated disaster room, and VR users preferred and rated these annotations highly, though the study lacks a controlled baseline comparison.

  3. When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective

    cs.SE 2025-09 conditional novelty 6.0 of 10

    The first empirical taxonomy of LLM tasks in UAVs, with an academia-industry comparison and survey, shows LLMs are used mainly for planning and interaction, not direct control.

  4. In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systems

    cs.CV 2025-07 conditional novelty 6.0 of 10

    An in-context learning framework with open-source vision-language models detects face presentation and morphing attacks without training, beating CLIP-based zero-shot baselines on PAD but with performance highly sensi...

  5. Humanoid World Models: Open World Foundation Models for Humanoid Robotics

    cs.RO 2025-06 conditional novelty 6.0 of 10

    Masked-transformers trained on humanoid video forecast future frames with better FID than flow-matching models, and parameter sharing cut model size 33-53% with minimal quality loss.

  6. RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning

    cs.AI 2025-10 conditional novelty 5.0 of 10

    A 3B VLM trained with SFT plus GRPO and an LCS-based reward reaches 55.3% on EmbodiedBench's EB-ALFRED, beating GPT-4o-mini and the 7B REBP planner.

  7. BlueGlass: A Framework for Composite AI Safety

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  8. From Image Captioning to Visual Storytelling

    cs.CL 2025-07 unverdicted novelty 4.0 of 10

    Visual storytelling improves by treating it as image captioning followed by language-to-language story generation, with a new 'ideality' metric to gauge distance from an oracle.

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