Mark-Based Visual Prompting

Updated 23 June 2026

Mark-Based Visual Prompting is a method that overlays sparse, user-specified marks (points, boxes, scribbles) on images to spotlight regions for improved model interpretation.
It employs diverse encoding strategies and fusion techniques—such as prompt token insertion and feature modulation—to integrate these marks into multimodal vision models.
Empirical studies reveal gains like an 8–15% VQA improvement and enhanced spatial reasoning, although performance is sensitive to marker design details.

A mark-based visual prompt is a discrete annotation—such as a point, box, or scribble—overlaid on an image to highlight specific regions of interest for multimodal LLMs (MLLMs), vision-LLMs (VLMs), or vision transformers. Unlike pixel-level (mask) or soft, learned prompts, mark-based prompts are sparse, user-driven, and minimal, enabling intuitive and efficient region specification for tasks ranging from visual question answering (VQA), fine-grained visual grounding, robotic control, and perception benchmarks. Current research incorporates a variety of encoding strategies, fusion methods, and training regimes to harness mark-based concepts across domains. Empirical studies demonstrate substantial gains in localization accuracy, spatial reasoning, and compositional understanding, while also revealing pronounced sensitivities to marker design and benchmarking practices.

1. Definitions, Taxonomy, and Formalism

A mark-based visual prompt is a human-specified, graphical annotation superimposed onto an image. Formally, such a prompt VP is parameterized as VP = (S, A, θ), where S denotes the visual mark shape (e.g., point, bounding box, scribble), A the attribute set (color, thickness, transparency, style), and θ the geometric parameters (e.g., location, scale, orientation). The induced binary region is specified by a function φ_S(x; θ), and can be summarized as Ω_p = { x ∈ D : φ_S(x; θ) = 1 } (Xu et al., 14 Nov 2025). Representative types include:

Point prompts: sets of pixel coordinates or 2D keypoints (often for segmentation or referential VQA).
Box prompts: axis-aligned rectangles, parameterized by (x₁, y₁, x₂, y₂) or their center, width, and height.
Scribble prompts: free-form strokes or curves, rasterized to binary mask overlays.

Unlike pixel-level segmentation masks, mark-based prompts are sparse; in contrast to learned, soft-pattern perturbations, they remain discrete and interpretable (Wu et al., 2024).

Typical encoding approaches are as follows (Wu et al., 2024, Cai et al., 2023):

Prompt Type	Discrete Encoding	Embedding Strategy
Point	Binary mask at point locations	Coordinate embeddings via E_pos
Box	Binary mask of rectangle	Box embedding via E_box
Scribble	Rasterized thick-line mask or sequence of points	Sequence embedding with averaging

The formalization extends to multi-mark settings, enabling compositional question answering (e.g., “What is the color of the object circled on the left, and its relation to the square on the right?”), and can be mathematically expressed by partitioning the input image into discrete or labeled regions.

2. Marker Rendering, Fusion Techniques, and Pipeline Integration

Mark-based prompts are rendered over images using controlled overlays (e.g., alpha-blended boxes, colored dots, thick or dashed borders). Various methods exist for fusing marks with visual features:

Input concatenation: The annotation mask or rendered overlay is appended as one or more additional channels to the original RGB image before passing it to the vision encoder (e.g., ViT) (Wu et al., 2024, Zhang et al., 2024).
Prompt token insertion: Encoded mark embeddings are inserted as special prompt tokens prepended to the patch token sequence, yielding Z_v = p₁,...,p_K, v₁,...,v_T.
Feature modulation/gating: Marks are projected to gating vectors, which modulate patch-level visual features (v_t → v′_t = v_t ⊙ g) to accentuate marked regions (Wu et al., 2024).
Direct overlay: Users (or the program) render marker pixels at specified coordinates; the resulting marked image is passed through frozen or fine-tuned vision backbones (e.g., CLIP, frozen ViT) (Cai et al., 2023, Yang et al., 2023).
Marker-as-image: Marks are rasterized to mask images, processed in parallel or merged with the main image in multi-scale feature encoders (e.g., MoV in EarthMarker) (Zhang et al., 2024).

Prompted images and/or concatenated features are projected to the multimodal LLM input space, often followed by traditional transformer-based cross-modal fusion (standard cross-attention, sometimes parameter-efficient via LoRA). Some models additionally associate mark attributes (color, thickness) with textual descriptions to harmonize multimodal alignment (Xu et al., 14 Nov 2025).

3. Mark-Based Prompting in Multimodal Applications

Mark-based visual prompting has been adopted across a broad spectrum of tasks:

VQA, referring expression comprehension, and entity disambiguation: Overlays of alphanumeric marks or bounding boxes (e.g., SoM prompting) enable precise region referencing and dramatically improve fine-grained grounding without model fine-tuning (Yang et al., 2023).
Robotics and embodied AI: Systems such as VP-VLA and MOKA employ structured overlays (points, boxes, crosshairs, grid cells) to decouple high-level planning from low-level execution; marks (e.g., crosshairs/boxes) serve as spatial anchors for control policies, with auxiliary grounding losses enforcing model attention to these cues (Wang et al., 23 Mar 2026, Liu et al., 2024).
Autonomous driving perception: Marker-based pipeline in MPDrive overlays numeric labels directly onto instance regions or coordinates, fusing these with original images to strengthen instance-level spatial reasoning under VQA and planning (Zhang et al., 1 Apr 2025).
Emotion recognition: Set-of-Vision (SoV) prompting combines boxes, numbers, and landmarks to boost zero-shot face count and emotion classification accuracy in VLLMs (Zhang et al., 2024).
Remote sensing: EarthMarker deploys box and point prompts, fused as multi-scale visual tokens using a shared encoder, to bridge domain gaps and enable fine-grained interpretation of satellite and aerial imagery (Zhang et al., 2024).
Segmentation: Point-based mark prompting remains foundational, with models such as SAM utilizing both inclusion and exclusion points, and point coverage/spread directly correlating with segmentation fidelity (Quesada et al., 2024).

4. Benchmarking, Empirical Results, and Attribute Sensitivity

Comprehensive benchmarks—including VP-Bench and SoM-Bench—formalize mark-based visual prompting using thousands of images, with systematic variations in shape, color, thickness, and style (e.g., 355 (shape, attribute) combinations; 16 marker variants in sampling-based studies) (Xu et al., 14 Nov 2025, Feng et al., 19 Dec 2025). Evaluation protocols typically involve zero-shot or few-shot prompting, with multi-choice, open-ended, and spatial localization metrics:

Perception metrics: Accuracy, IoU (region overlap), enumeration/counting performance, and recall/precision for region-specific queries.
Downstream utility: Task-dependent outcome when prompting supports real-world applications (e.g., medical image analysis, GUI recognition, emotion classification).

Key empirical findings include:

Advantage / Effect	Reported Quantitative Gain	Source
VQA accuracy rel. improvement	8–15% over text-only or box-only	(Wu et al., 2024)
Reduction in object hallucination	10–20% decrease	(Wu et al., 2024)
SoM prompt vs baseline (REC@0.5)	25.7% → 86.4%	(Yang et al., 2023)
SoV emotion rec. zero-shot acc boost	+11 pts absolute (44.44%→55.33% GPT-4V)	(Zhang et al., 2024)
Human-vs-auto point prompt (mIoU)	–29% gap; up to +68% recouped by fine-tuning	(Quesada et al., 2024)
Robotic control (success rate)	+5–8.3% absolute improvement	(Wang et al., 23 Mar 2026)
Segmentation (Blur Reverse Mask)	+3–4.6% over RedCircle/crop; max +12.5%	(Yang et al., 2023)

Benchmarks reveal acute sensitivity to marker details: Changing color, size, or placement can cause substantial swings in accuracy (Δacc_marker up to ±10%), and leaderboards for small datasets are unstable under bootstrap resampling (Feng et al., 19 Dec 2025). Standardization and reporting over multiple attribute combinations are therefore recommended.

5. Design Principles, Best Practices, and Ablation Insights

Performance, grounding, and interpretability depend critically on the choice of marker type and visual attributes:

Shape: Regular geometric shapes (bounding box, oval, arrow) outperform irregular ones (scribble, point) for VP perception (Xu et al., 14 Nov 2025).
Color: High-contrast, saturated colors (bright red, green, blue) measurably increase prompt recognition and perception accuracy by +8–12% (Xu et al., 14 Nov 2025).
Thickness: A thin-to-medium line (1–3px) preserves context and avoids occlusion; overly thick markers degrade performance (Xu et al., 14 Nov 2025).
Prompt coverage: Maximizing coverage efficiency—using minimal, strategic points for maximal region span—directly correlates with segmentation and VQA quality (Quesada et al., 2024).
Textual alignment: Augmenting image markers with explicit textual description (e.g., “the red box outlining…” in the instruction) confers up to +29% additional accuracy depending on region type (Xu et al., 14 Nov 2025).
Automation: For domain adaptation or precision, fine-tuning prompt encoders on curated (image, mark) pairs via dice-loss minimization can bridge the performance gap between automated and human-driven prompting (Quesada et al., 2024).

Ablation studies confirm that inclusion points—placement and coverage—govern the majority of segmentation accuracy, while exclusion points have a much smaller impact (Quesada et al., 2024). In robot vision, crosshair marks outperform simple points for spatial grounding, especially when coupled with auxiliary losses (Wang et al., 23 Mar 2026).

6. Limitations, Instabilities, and Future Research Opportunities

Despite its broad adoption and demonstrated improvements, mark-based visual prompting exhibits several notable limitations:

Marker-induced instability: Even subtle changes in marker design (color, label placement, radius) can cause nontrivial shifts in performance or model ranking, demanding that benchmarks report confidence intervals and evaluate across diverse marker attributes to ensure result reliability (Feng et al., 19 Dec 2025).
Occlusion and collision: In multi-object scenes, marks may overlap interior regions, potentially confusing the visual encoder. Automated strategies for optimal, unambiguous placement remain an open challenge (Yang et al., 2023).
Domain transfer: Robust transfer of mark-based prompt designs to new domains (e.g. remote sensing, medical imagery) requires dedicated strategies, such as cross-domain training pipelines and pseudo-image mark encoding (Zhang et al., 2024).
Fine-grained vs. sparse marking: While mark-based prompts maximize usability, some fine-grained tasks demand pixel-level delineation, motivating hybrid schemes (e.g., blur reverse mask) or learned patch overlays for non-biased attention steering (Yang et al., 2023, Rezaei et al., 2024).
Real-time constraints: Rendering complex overlays or performing large-scale segmentation can incur significant pre-processing overhead.

Future directions include generalizing 2D prompts to 3D or temporal video domains, dynamically adapting marker types for task or context, learning soft-proxy marks for arbitrary vision encoders, and integrating chain-of-thought visual reasoning templates for multi-step, compositional tasks (Yang et al., 2023, Xu et al., 14 Nov 2025).

7. Impact, Broader Implications, and Standardization

Mark-based visual prompting has transformed region-level interaction with vision-LLMs and multimodal systems. Its practical advantages include:

Reducing semantic gaps between visual and textual spatial information (e.g., “marker #3” vs raw coordinates), enhancing compositional reasoning and referential disambiguation (Zhang et al., 1 Apr 2025).
Enabling intuitive human–model interaction with off-the-shelf or frozen encoders—often without requiring any model fine-tuning (Yang et al., 2023, Zhang et al., 2024).
Providing a scalable, lightweight means to produce high-quality supervision data for further model distillation and policy learning (Liu et al., 2024).

The methodology has catalyzed benchmark creation (VP-Bench, SoM-Bench, RSVP), the formalization of mark parameterization, and the establishment of best-practice guidelines for prompt design and evaluation (Xu et al., 14 Nov 2025, Feng et al., 19 Dec 2025, Zhang et al., 2024).

Proposals for future work stress standardized prompt attribute sets and rigorous benchmark reporting—including variability and confidence intervals—to ensure that empirical results meaningfully reflect cross-model or cross-domain progress, rather than artifacts of marker implementation (Feng et al., 19 Dec 2025).