Visual Prompts: Methods & Applications

Updated 27 November 2025

Visual prompts are explicit visual cues inserted into images or videos, steering model predictions via pixel-level or feature-space modifications.
They can be manually engineered or automatically generated using segmentation, detection, and prompt learning techniques to enhance task performance.
Applications span object localization, video synthesis, and interactive systems, with proven metrics showing improved accuracy and reduced error rates.

A visual prompt is an explicit, instance-specific visual signal inserted into or overlaid on an image (or video) to steer the behavior of a visual or multimodal model, with the aim of guiding visual attention, grounding, or reasoning. Unlike textual prompts, which manipulate the token stream of LLMs, visual prompts operate in the pixel space, latent feature space, or at the level of discrete visual tokens. Visual prompting enables fine-grained and spatially localized instruction, introduces task-specific or region-specific cues, and is foundational to a variety of recent advances across computer vision, vision-language modeling, video synthesis, and interactive visual systems (Wu et al., 5 Sep 2024).

1. Visual Prompt Definitions and Taxonomy

A visual prompt in modern models is any auxiliary visual cue that, when applied to an image or video, alters the downstream representation or prediction in a controllable way (Wu et al., 5 Sep 2024). Formally, visual prompts fall broadly into these categories:

Free-form prompts: Arbitrary painted cues (scribbles, arrows, circles, icons), generally user-drawn and not schema-constrained.
Structured prompts: Parameterized as discrete tokens (e.g., <ROI> + coordinates), binary/soft masks, or learnable vectors/masks, with explicit semantic mapping to model input or internal features.
Pixel-level prompts: Single pixels or small regions selected/masked at input resolution or substituted with learnable prompt embeddings at precise locations.
Soft, learnable prompts: Continuous perturbations injected into the image, patches, or intermediate feature map, often realized by concatenation or addition of low-rank embeddings to visual tokens.

Table 1 summarizes the taxonomy:

Type	Representation	Example Use Case
Bounding-box prompt	[x₁,y₁,x₂,y₂] or mask	Object referring, localization
Marker-based prompt	Circle, arrow, icon overlays	Free-form attention steering
Pixel/patch prompt	Masked or substituted patches	Pixel-wise focus, visual tokens
Soft/learned prompt	Learnable vector or mask	Task transfer, few-shot learning

Distinction: Visual prompts are distinct from text prompts due to their insertion modality (visual/feature space vs. token space), their spatial specificity, and their potential to fuse human intention with the model’s perceptual pipeline (Wu et al., 5 Sep 2024, Wu et al., 2022).

2. Generation and Injection of Visual Prompts

Manual Engineering: Human users may overlay explicit cues (e.g., circles, arrows, rectangles) without requiring model retraining (Wu et al., 5 Sep 2024, Denner et al., 28 Aug 2024, Zhu et al., 4 Jan 2025).

Automatic Prompt Generation:

Segmentation-based prompts: Off-the-shelf segmentors (SAM, OpenSeeD) auto-generate object or region masks, ranked and injected as prompts for grounding or object-centric QA (Wu et al., 5 Sep 2024).
Detection-based prompts: Pre-trained detectors yield box and class tags; annotation modules can be trained to select, score, and inject prompts (Wu et al., 5 Sep 2024).
Multi-step toolchains: Sequential invocation of segmentation, detection, or inpainting tools to produce rich composite prompting for complex instruction-chaining (Wu et al., 5 Sep 2024).

Prompt Insertion Mechanisms:

Pixel-space overlays: Prompts directly rendered on the RGB input, e.g., a red circle to prompt a specific target (medical images, emotion recognition, remote sensing) (Denner et al., 28 Aug 2024, Zhang et al., 3 Oct 2024, Zhang et al., 18 Jul 2024).
Feature-space augmentation: Prompt embeddings concatenated or added to visual token sequences, or used to initialize/modify specific positions (patches, special prompt tokens) in the backbone (Wu et al., 2022, Xu et al., 2023).
Transformers/MLLMs: Prompt tokens or augmented visual features are inserted as leading "special" tokens in transformer encoders, e.g., via [P; F], where P is a prompt block and F is the sequence of conventional image patch embeddings (Wu et al., 5 Sep 2024, Xu et al., 2023).

3. Prompt Learning, Adaptation, and Optimization

Prompt Learning Objectives:

Cross-entropy losses: For classification/regression or VQA tasks, visual prompt parameters are optimized to minimize prediction error on downstream labels (Wu et al., 2022, Xu et al., 2023).
Contrastive losses: Used for visual grounding, e.g., learning prompts that best distinguish desired regions or concepts among distractors (e.g., CRG loss formulations) (Wu et al., 5 Sep 2024, Rezaei et al., 5 Jun 2024).
Self-supervised learning: For attention guidance, learnable prompts are optimized to steer a model's self-attention toward desired spatial locations via supervision over attention maps, e.g., using KL divergence to a Gaussian target centered at the prompt insertion point (Rezaei et al., 5 Jun 2024).

Prompt Tuning Paradigms:

Universal prompts: Single, image-agnostic additive perturbations (Δ) learned with only prompt parameters updated, backbone frozen (Bahng et al., 2022, Wu et al., 2022).
Instance-adaptive prompts: Progressively updated prompts that recycle activations or outputs from prior layers (e.g., ProVP) or blend image-specific and language-grounded cues (Xu et al., 2023, Kunananthaseelan et al., 2023).
Task-specific, few-shot adaptation: Prompt parameters adapt a model to new tasks or domains with minimal examples, often matching or exceeding performance of supervised linear probing and with superior few-shot/data efficiency (Wu et al., 2022, Xu et al., 2023, Kunananthaseelan et al., 2023).

In-context learning and compositional prompting:

Prompt-augmented examples can simulate in-context learning by presenting few-shot exemplars with associated visual prompts, either as concatenated image+prompt pairs or as merged multimodal representations (Wu et al., 5 Sep 2024).

4. Applications Across Domains

Vision-LLMs (VLMs), Multimodal LLMs (MLLMs):

Visual prompts serve as spatial selectors, focus amplifiers, or annotators for region-specific reasoning, object referring, compositional question answering, and emotion recognition (Wu et al., 5 Sep 2024, Zhang et al., 3 Oct 2024).
Injecting prompts (e.g., boxes, ellipses, scribbles) in radiology improves clinical region focus, AUROC in disease detection, and diagnostic explainability (Denner et al., 28 Aug 2024, Zhu et al., 4 Jan 2025).
In remote sensing, prompt boxes and points enable multi-scale, fine-grained localization and recognition in dense, high-resolution imagery (Zhang et al., 18 Jul 2024).

Image and Video Generation:

Visual action prompts (VAPs) constructed as skeleton overlays control high-DOF action-to-video generation, striking a balance between geometric precision and cross-domain adaptability, outperforming text or low-level control signal prompts in fine-grained video synthesis tasks (Wang et al., 18 Aug 2025).

Object Detection and Tracking:

In object tracking, explicit visual prompts (multi-scale, spatio-temporal) and CLIP-refined prompt maps reduce distractor interference and enhance instance-aware tracking, demonstrated in competitive performance on standard benchmarks (Shi et al., 6 Jan 2024, Chen et al., 27 Sep 2024).
In open-set detection, learned visual prompt vectors (in feature space) allow adaptation to novel unseen categories without needing new manual text prompts, outperforming context and offset prompt baselines in mAP (Chen et al., 2023).

Image Editing:

Visual prompts instantiated as before–after image pairs (Visual Instruction Inversion) can invert visual transformations into text-based editing instructions for diffusion models, enabling "one-shot" semantically grounded image editing (Nguyen et al., 2023).

Captioning and Retrieval:

Visual prompts constructed via retrieved textual information and fused in embedding space enrich lightweight captioning models (ViPCap), outperforming conventional text-only retrieval augmentation (Kim et al., 26 Dec 2024).

Emotion and Counting:

Set-of-Vision prompting, combining spatial bounding boxes, numeric labels, and landmarks, enables zero-shot VLLM-based face counting and per-person emotion recognition with substantial gains over prior prompt styles (Zhang et al., 3 Oct 2024).

5. Empirical Findings and Comparative Analysis

Several benchmark studies provide quantitative evidence of the efficacy and trade-offs of different visual prompting strategies.

Prompt Type	Key Advantages	Empirical Highlights
Bounding-box prompts	Precise localization	High accuracy for object referring QA
Marker prompts	Free-form, flexible, user-friendly	~10–15% higher on region-specific QA [SoM, ViP-LLaVA]
Soft learned prompts	Few parameters, strong task transfer	5–8% error reduction over box on segmentation VQA
Pixel/patch prompts	Fine spatial specificity	EVP outperforms linear probe by +2.2% (Wu et al., 2022)

Robust prompt design choices—such as multi-shape augmentation, prompt transparency tuning, and data-driven prompt generation—increase model accuracy, robustness to distribution shift, and interpretability across medical, remote sensing, general vision, and multimodal benchmarks (Zhu et al., 4 Jan 2025, Denner et al., 28 Aug 2024, Zhang et al., 18 Jul 2024).

6. Challenges, Limitations, and Future Directions

Key challenges in visual prompting include:

Vision–language misalignment: Novel or out-of-distribution prompts may be misinterpreted without prompt-aware training or pre-alignment, leading to hallucinations (Wu et al., 5 Sep 2024).
Scalability: Manual prompt engineering is not sustainable for video, 3D, or dense tasks (Wu et al., 5 Sep 2024, Wang et al., 18 Aug 2025).
Multi-object compositionality: Simultaneous focus on multiple regions increases error/hallucination rate.
Prompt diversity limits: Reliance on fixed prompt types (e.g., heatmaps) constrains generality (Zhang et al., 19 Jun 2025).

Notable future research trajectories:

Unified prompt representations: Development of shared embedding spaces that flexibly integrate boxes, circles, masks, and soft prompts for plug-and-play adaptation (Wu et al., 5 Sep 2024).
3D and multimodal prompts: Generalization of prompting mechanisms to 3D pointclouds, video, and audio-visual scenarios (Agent3D, RACCooN) (Wang et al., 18 Aug 2025, Wu et al., 5 Sep 2024).
Safety/robustness: Use of adversarial prompting to probe and mitigate vulnerabilities and bias, especially jailbreaking risks in LLMs (Wu et al., 5 Sep 2024).
Prompt generators and selection: Learned prompt engines (AutoV) that retrieve effective prompts per instance, improving over heuristic or random selection (Zhang et al., 19 Jun 2025).
Prompt-aware training curricula: Incorporating compositional reasoning and multi-step visual instruction into MLLM pipelines, e.g., via chain-of-thought prompting with spatial anchors (Wu et al., 5 Sep 2024).

7. Interpretability, Visualization, and Human-in-the-Loop Prompting

Visual prompts provide transparent, interpretable cues for both the model and human user, enabling explicit spatial grounding and controllable explainability (Denner et al., 28 Aug 2024, Zhu et al., 4 Jan 2025, Zhang et al., 18 Jul 2024).
Layer-wise attention analysis (e.g., LeGrad) confirms that injected visual markers can focus model attention on clinically or contextually critical regions.
Human-style prompting and attention visualizations bridge model behavior with human intent, critical for interactive AI systems and user-facing applications (Zhu et al., 4 Jan 2025, Zhang et al., 3 Oct 2024).

Visual prompting constitutes a rapidly evolving paradigm for adapting, steering, and interpreting modern vision and vision-LLMs, with applications spanning from precise medical diagnosis to domain-general visual reasoning, video generation, and interactive multimodal systems. Its continued advancement relies on principled integration of prompt design, automatic generation, alignment, and informed evaluation across diverse vision–language tasks (Wu et al., 5 Sep 2024, Wu et al., 2022, Kunananthaseelan et al., 2023, Zhu et al., 4 Jan 2025, Wang et al., 18 Aug 2025, Rezaei et al., 5 Jun 2024, Kim et al., 26 Dec 2024, Xu et al., 2023, Chen et al., 2023, Bahng et al., 2022, Zhang et al., 18 Jul 2024).