Call for Papers » Generative AI & Vision
Generative AI & Vision Conference - Call for Papers
We invite your original quality submissions to the Computer Vision Conference 2027 Track on Generative AI & Vision.
This track considers the generative turn in computer vision, where models increasingly create, transform, or reason jointly across visual and linguistic content rather than only classifying or detecting what is already there. We invite contributions on diffusion, flow-based, and adversarial generative models, foundation models and vision transformers, and vision-language models that ground text in visual content. Submissions may include architecture and training papers pushing generation quality or efficiency, evaluation work examining what current generative vision models get wrong, or applications combining synthesis with real-world creative, scientific, or industrial workflows. Photorealism or fluency alone is not sufficient; particularly valued is evidence of controllability, faithfulness to the input or prompt, and honest characterization of failure cases such as hallucinated content or unstable multimodal grounding. The track ultimately looks for generative and multimodal vision work that expands what these models can be trusted to produce, not just what they can be made to produce once.
Round 1 (Closed)
- Submission Deadline: 01 September 2026
- Notification of Acceptance: 15 September 2026
- Camera-Ready Submission: 01 October 2026
- Conference Dates: 15-16 April 2027
Round 2 (Open)
- Submission Deadline: 01 October 2026
- Notification of Acceptance: 01 November 2026
- Camera-Ready Submission: 01 December 2026
- Conference Dates: 15-16 April 2027
All times are in Anywhere on Earth (AoE) time zone.
Topics of Interest
- Foundation Models in Vision: Large pretrained vision models adaptable to downstream tasks such as detection, segmentation, and classification through fine-tuning or prompting.
- Vision Transformers: Transformer-based architectures applied to visual data, offering an alternative to convolutional networks for image and video understanding.
- Diffusion Models: Generative models that synthesize images and video by iteratively denoising random noise, widely used for image generation and editing.
- Generative Adversarial Networks: Generative models that pit a generator against a discriminator to produce realistic synthetic data, widely used in image synthesis.
- Generative Flow Models: Normalizing flow and flow-matching approaches that model exact likelihoods for image and video generation, offering an alternative to diffusion and adversarial training with tractable inference.
- Vision-Language Models: Models that jointly reason over images and text, including visual question answering, image captioning, and grounding language in visual content, extending foundation model research into genuinely multimodal understanding.
- Text-to-Image/Video Synthesis: Generative methods that produce images or video from natural language descriptions.
- Photorealistic Image Synthesis: Generative techniques that produce highly realistic synthetic imagery for simulation, media, and data augmentation.
- Multi-modal Image Fusion: Combining imagery from different sensor modalities, such as infrared and visible light, into a unified representation.
- Motion Synthesis: Generation of realistic motion sequences for characters, objects, or scenes, used in animation, simulation, and video generation.
Submission Guidelines
- We accept initial submission in PDF format only.
- To adhere to the double-blind peer review process, authors must refrain from including any details related to their identity, such as author names, affiliations, or country information, within the paper.
- Authors are responsible for ensuring that their paper is thoroughly checked and proofread prior to submission. Please note that no changes can be made to the paper once it has been submitted for review. However, if the paper is accepted, minor revisions may be made before final publication to address any areas for improvement.
- When submitting the camera-ready version of the paper, please ensure that all author information is included, as required by the paper formatting guidelines. Author names, email addresses, and their sequence will be considered final as per the submitted camera-ready version.
- Submissions must primarily consist of original work. No more than 25% of the content should be derived from previously published material by the same authors, and any such reused content must be properly cited. Therefore, at least 75% of the manuscript should contain new, unpublished material.
Formatting Guidelines
- Manuscripts should typically be confined to 18 pages for the main text (excluding references and appendices of upto 7 pages). Submissions that exceed this limit may be considered at the discretion of the conference chair, particularly for review or survey papers.
- Manuscripts should present original research that aligns with the conference themes and topics.
- It is the responsibility of the authors to ensure the accuracy of all citations, quotations, figures, maps, and tables included in their manuscript.
- The abstract should adhere to the 150-250 word limit as per the conference guidelines.
- Figures and tables should be placed within the manuscript at the appropriate locations where they are referenced and must be clear and legible.
- All figures, tables, references, appendices, algorithms, annexures, and supplementary materials must be clearly numbered, and properly cited in the text.
- Tables must be neatly aligned within the page margins and must not overflow the page boundaries.
- Figure captions and table headings should be concise, unique, and ideally limited to a maximum of two lines.
- References should be formatted correctly and consistently, including all the necessary details such as author names, paper titles, publication sources, and years. Every reference listed must be cited in the main text.
- The use of foreign languages in the manuscript is not allowed unless accompanied by a corresponding English translation.
- The manuscript must comply with the formatting guidelines specified in the provided template.
Review Process
Submissions will undergo an anonymous review process (double-blind). As a result, authors must ensure their manuscript does not disclose their identity or affiliation. Specifically, authors should remove their names and affiliations from the manuscript, anonymize any citations or references to their own related work, and omit any acknowledgments, including funding sources.
Each paper will be evaluated by a minimum of three regular program committee members or two senior program committee members. Acceptance will be determined based on the originality, technical depth, elegance, impact (either practical or theoretical), and overall presentation quality.
- Original: The paper should introduce a new idea, project, or issue; provide fresh insights into existing research; present new findings; or offer a novel perspective on established information.
- Engaging: The presentation should be interactive, engaging the audience, or address community needs in a way that is likely to attract substantial conference attendance.
- Significant: The paper should tackle critical issues related to improving the effectiveness of current methods, and its content should be clear and broadly applicable.
- Quality: Any claims made in the paper must be supported by sufficient data, properly reference existing work, and honestly acknowledge any limitations.
- Clear: The outcomes of the paper should be presented in a manner that is easily understood.
- Relevant: The paper’s subject matter must align with at least one of the conference’s designated tracks.
Use of AI-Assisting Tools
The conference permits the responsible use of Generative AI (GenAI) tools in manuscript preparation. Authors are required to observe the following:
- Declare Usage – Any use of GenAI must be disclosed in the manuscript (e.g., in a Declaration on Generative AI section).
- Maintain Responsibility – Authors must critically review and edit AI-assisted text; unedited AI output is not acceptable.
- No AI Authorship – GenAI tools cannot be listed as authors. Authorship is limited to humans who take full responsibility for the work.
- Limit Role of AI – GenAI may support drafting and language refinement, but the development of scientific ideas, arguments, results, and conclusions must be carried out by human authors.
- Accountability – Authors remain fully accountable for the accuracy, originality, and integrity of their manuscripts.
Note: Non-compliance with these guidelines will be treated as academic misconduct and may result in the removal of published work.
Other Tracks at Computer Vision Conference 2027
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