Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement

• URL: http://arxiv.org/abs/2609.01148v1
• Date: Tue, 01 Sep 2026 12:24:33 GMT
• Title: Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement
• Abstract summary: We propose EyeControl, a MLLM-driven retouching executor that enables visual focus enhancement under weak user intent.<n>With only a few clicks or coarse strokes, EyeControl directs visual attention to the intended region, effectively "dotting the eye" of the image.<n>We also introduce an operation-consistency constraint to improve coordination between global and local adjustments, achieving more natural and coherent retouching.
• Score: 85.49085209742798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image retouching is commonly formulated as enhancing overall visual quality through color adjustment, but in practice, it also serves to emphasize visual focus by guiding viewers' attention toward a specific subject or region. Achieving such focus-oriented retouching is inherently challenging, as it requires well-coordinated global and local adjustments to manipulate perceptual saliency while maintaining visual naturalness. This intricate process typically demands substantial professional expertise. In this study, we propose EyeControl, a MLLM-driven agent with a diffusion-based retouching executor that enables visual focus enhancement under weak user intent. With only a few clicks or coarse strokes, EyeControl directs visual attention to the intended region, effectively "dotting the eye" of the image. The core idea is to explicitly link the weak user intention with the target editing region and the corresponding tonal adjustment operations during retouching. To achieve this, the system first interprets the intent and image content to infer the visual focus and generate structured intent guidance for the retouching executor. Second, the retouching executor is encouraged to respond more strongly to the target region, explicitly aligning its attention map with a designed pseudo-intent map. We also introduce an operation-consistency constraint to improve coordination between global and local adjustments, achieving more natural and coherent retouching. Additionally, we contribute ControlArt-Bench, a high-quality evaluation dataset for visual focus enhancement. Extensive evaluations demonstrate that EyeControl yields perceptually appealing results with stronger intent alignment. Code can be found in https://github.com/DragonisCV/EyeControl.

Related papers

• Tinted Frames: Question Framing Blinds Vision-Language Models [29.78944164519993]
  Vision-Language Models (VLMs) have been shown to be blind, often underutilizing their visual inputs even on tasks that require visual reasoning.<n>We quantify how framing alters both the amount and distribution of attention over the image.<n>We introduce a lightweight prompt-tuning method using learnable tokens that encourages the robust, visually grounded attention patterns observed in open-ended settings.
  arXiv  Detail & Related papers  (2026-03-19T17:53:09Z)
• A Saccade-inspired Approach to Image Classification using Vision Transformer Attention Maps [0.9332987715848716]
  We draw inspiration from the human visual system to create smarter image processing models.<n>Using DINO, a self-supervised Vision Transformer, we explore a saccade inspired method to focus the processing of information on key regions in visual space.<n>This selective-processing strategy preserves most of the full-image classification performance and can even outperform it in certain cases.
  arXiv  Detail & Related papers  (2026-03-10T12:54:55Z)
• PerTouch: VLM-Driven Agent for Personalized and Semantic Image Retouching [54.3683137773426]
  We propose a unified diffusion-based image retouching framework called PerTouch.<n>Our method supports semantic-level image retouching while maintaining global aesthetics.<n>We develop a VLM-driven agent that can handle both strong and weak user instructions.
  arXiv  Detail & Related papers  (2025-11-17T05:39:15Z)
• RetouchLLM: Training-free Code-based Image Retouching with Vision Language Models [76.79706360982162]
  We propose RetouchLLM, a training-free white-box image retouching system.<n>It performs interpretable, code-based retouching directly on high-resolution images.<n>Our framework progressively enhances the image in a manner similar to how humans perform multi-step retouching.
  arXiv  Detail & Related papers  (2025-10-09T10:40:49Z)
• Focusing by Contrastive Attention: Enhancing VLMs' Visual Reasoning [79.34909830834464]
  Vision-Language Models (VLMs) have demonstrated remarkable success across diverse visual tasks, yet their performance degrades in complex visual environments.<n>We show that visual complexity strongly correlates with attention entropy, negatively impacting reasoning performance.<n>We propose Contrastive Attention Refinement for Visual Enhancement (CARVE), a training-free method that extracts task-relevant visual signals through attention contrasting at the pixel level.
  arXiv  Detail & Related papers  (2025-09-08T09:20:04Z)
• Control and Realism: Best of Both Worlds in Layout-to-Image without Training [59.16447569868382]
  We present WinWinLay, a training-free method for layout-to-Image generation.<n>We propose two key strategies, Non-local Attention Energy and Adaptive Update, that collaboratively enhance control precision and realism.<n>WinWinLay excels in controlling element placement and achieving photorealistic visual fidelity, outperforming the current state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-06-18T15:39:02Z)
• Align and Surpass Human Camouflaged Perception: Visual Refocus Reinforcement Fine-Tuning [18.13538667261998]
  Current multi-modal models exhibit a notable misalignment with the human visual system when identifying objects that are visually assimilated into the background.<n>We build a visual system that mimicks human visual camouflaged perception to progressively and iteratively refocus' visual concealed content.
  arXiv  Detail & Related papers  (2025-05-26T07:27:18Z)
• Control-oriented Clustering of Visual Latent Representation [3.9838014203847862]
  We study the geometry of the visual representation space in an image-based control pipeline learned from behavior cloning.<n>Inspired by the phenomenon of neural collapse, we show a similar law of clustering in the visual representation space.<n>We show such a law of clustering can be leveraged as an algorithmic tool to improve test-time performance.
  arXiv  Detail & Related papers  (2024-10-07T14:21:51Z)
• Top-Down Visual Attention from Analysis by Synthesis [87.47527557366593]
  We consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision. We propose Analysis-by-Synthesis Vision Transformer (AbSViT), which is a top-down modulated ViT model that variationally approximates AbS, and controllable achieves top-down attention.
  arXiv  Detail & Related papers  (2023-03-23T05:17:05Z)

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.