Semi-Automatic Annotation Process

Updated 2 December 2025

Semi-automatic annotation is a hybrid process where automated labeling is refined by selective human intervention to balance efficiency with accuracy.
The pipeline employs methods like confidence-based filtering, active candidate recommendation, and iterative retraining to minimize manual effort.
Empirical studies demonstrate substantial reductions in annotation time and high quality results across domains such as computer vision and language processing.

A semi-automatic annotation process refers to a human-in-the-loop data labeling paradigm in which initial or bulk annotation is carried out by automated or algorithmic means, with targeted, minimal manual corrections, confirmations, or guidance provided by human annotators. The process is designed to combine the efficiency, throughput, and consistency of machine proposals with the accuracy, nuance, and domain knowledge of human supervision. Contemporary semi-automatic pipelines span diverse domains, including computer vision, language processing, and structured data annotation, and rely on different strategies such as tracking, active candidate recommendation, graph-based propagation, and error-aware data triage.

1. Pipeline Architectures and Core Principles

Semi-automatic annotation processes are characterized by a division of labor between machine algorithms and annotators. In a typical pipeline, the following stages are found, though their composition and order may vary:

Manual Seeding: Human annotators initialize labels on either a subset of data (e.g., the first frame in a video, a batch of samples, or anchor instances in an image) (Zhu et al., 2021, Zhang et al., 2021, Adhikari et al., 2020).
Bulk Automated Propagation: Machine learning models, tracking algorithms, or rule-based systems generalize or extrapolate the manual labels to larger portions of the data, producing annotation proposals (Zhu et al., 2021, Jäger et al., 2019, Subramanian et al., 2018, Ince et al., 2021).
Confidence-Based Filtering: Annotation candidates are scored according to spatial, appearance, or model-based metrics; those with high confidence are accepted automatically, while low-confidence or ambiguous cases are flagged or deferred to human review (Zhu et al., 2021, Liao et al., 2022, Huang et al., 20 May 2024).
Human-in-the-Loop Correction: Annotators validate, correct, or supplement machine proposals. Correction mechanisms include direct editing, rapid acceptance/rejection, or confirming model recommendations (Zhu et al., 2021, Zhang et al., 2021, Benato et al., 2020, Adhikari et al., 2020).
Incremental/Iterative Retraining: Corrected annotations update the underlying model or propagate improved rule statistics, leading to better subsequent proposals—a bootstrapping process (Adhikari et al., 2020, Ince et al., 2021, Rahman et al., 11 Oct 2024).
Final Sanity Checks and Export: A final manual or algorithmic quality check is performed (e.g., reviewing the last frame, sampling from the corpus), and completed annotations are exported in domain-specific formats (Zhu et al., 2021, Zhang et al., 2021, Jäger et al., 2019).

This modular structure enables substantial efficiency gains by concentrating human effort only on the most error-prone or difficult-to-automate cases, while maintaining annotation integrity.

2. Algorithms, Scoring Functions, and Models

A wide spectrum of algorithmic approaches underpin semi-automatic annotation processes, tailored to the domain and data modality.

Correlation-Filter Trackers: Used in video scene text and object tracking, trackers such as KCF propagate manually drawn boxes through subsequent frames. A confidence score $S$ combines spatial overlap and appearance similarity:

$S = \alpha\, \mathrm{IoU}(B^{t-1}, B^{t}) + (1-\alpha)\, \exp(-d(f^{t-1}, f^{t}))$

where $d$ is an $L_2$ -distance between appearance features (Zhu et al., 2021).

Detector–Prediction Loops: Iteratively fine-tuned deep models (e.g., YOLO, RetinaNet, Faster R-CNN) generate proposals that are corrected in successive batches by annotators, with subsequent retraining (Adhikari et al., 2020, Subramanian et al., 2018). For example:

$\mathcal{L} = \frac{1}{N_{\text{cls}}} \sum_i L_{\text{cls}}(p_i, p_i^*) + \lambda \frac{1}{N_{\text{loc}}} \sum_i p_i^* L_{\text{loc}} (t_i, t_i^*)$

Semi-supervised and Rule-based Label Propagation: Data points are embedded via autoencoder features and projected (e.g., t-SNE), with graph-based semi-supervised methods (LapSVM, OPF) propagating labels from seed points; confidence estimates determine which labels require manual intervention (Benato et al., 2020).
Collaborative Recommender Systems: In graph annotation, e.g., scene graph relationships, rule-based recommenders score candidate edge labels using historical co-occurrence statistics:

$P(u, i) = \sum_b n_{u, b} \times n_{b, i}$

where $u$ is a subject–object pair, $i$ a predicate, and $b$ relevant attribute values (Zhang et al., 2021).

Error-Aware Triage Networks: Allocation of annotation effort is driven by model error predictors $d^{EAT}$ , which estimate the risk of model misannotation. An example error-aware loss is:

$L_{d} = \sum_{(x, y)} \left[ -\delta_t\log d^{EAT}(x) - (1-\delta_t)\log(1 - d^{EAT}(x)) \right]$

with triage to expert or model annotator based on calibrated risk (Huang et al., 20 May 2024).

3. Pseudocode and Implementation Patterns

The literature provides high-level pseudocode for key process steps:

function SemiAutoAnnotate(video_frames, τ, α):
    B_*^1 = manual_label(F₁)
    for i in 1..n_text:
        T_i ← InitTracker(F₁, B_i^1)
        f_i^1 ← ExtractAppearance(F₁, B_i^1)
    for t in 2..N:
        for i in 1..n_text:
            (B_i^t, R_i^t) ← T_i.track(F_t)
            f_i^t ← ExtractAppearance(F_t, B_i^t)
            S_i^t ← computeScore(B_i^{t−1}, B_i^t, f_i^{t−1}, f_i^t, α)
            if S_i^t ≥ τ:
                T_i.update(F_t, B_i^t)
            else:
                B_i^t ← manual_correct(F_t)
                T_i ← InitTracker(F_t, B_i^t)
                f_i^t ← ExtractAppearance(F_t, B_i^t)
    for i in 1..n_text:
        if misaligned(B_i^N):
            B_i^N ← manual_correct(F_N)
    return {B_i^t}

(Zhu et al., 2021)

In data triage:

for t = 1…|X| do
    al_score = d^AL_{t−1}(x)
    eat_score = d^EAT_{t−1}(x)
    bi_score = (al_score)^η * eat_score
    if bi_score ≥ threshold and remaining_budget ≥ c_H:
        y = expert_label(x)
    else:
        y = f_{t−1}(x)

(Huang et al., 20 May 2024)

The paradigm is to combine initial manual seeding, automated proposal and scoring, confidence-based filtering, and rapid user validation.

4. Parameterization, Tuning, and Configuration

Optimal performance and workload reduction depend on key parameters:

Confidence Thresholds ( $\tau$ ): Empirically set by validation to balance recall (low $\tau$ ) against false positives (high $\tau$ ) (Zhu et al., 2021, Shao et al., 2019). Higher $\tau$ increases recall but may require more correction.
Appearance vs. Spatial Balance ( $\alpha$ ): Weighted fusion of IoU and appearance similarity; set via a grid search (e.g., $\alpha = 0.5$ ) (Zhu et al., 2021).
Batch Size and Iterative Granularity: Batch size in iterative pipelines determines the trade-off between model learning speed and annotation correction rate (Adhikari et al., 2020). Smaller batches lead to faster convergence in model accuracy.
Feature Embedding/Projection Method: Autoencoder dimension, t-SNE perplexity, and graph regularization parameters for label propagation approaches are dataset-specific (Benato et al., 2020).

These controls are universally exposed to annotators and researchers for process adaptation and cross-domain generalization.

5. Quantitative Benefits and Efficacy

Empirical results across domains consistently support large efficiency and quality advantages:

Reduction in Annotation Effort: Reduction of total annotation time from months to hours in scene text video (369 videos in 72 h vs. >200 person-months; (Zhu et al., 2021)), up to 75.9% in video object annotation (Adhikari et al., 2020), and over 90% reduced manual effort in semi-automatic vehicle tracking (Liao et al., 2022).
Annotation Quality: Near equivalence or superiority to full manual annotation, e.g., above 90% frame-level IoU with manual annotation for semi-automatic trackers (Zhu et al., 2021), mAP >99% in guided object annotation (Subramanian et al., 2018), and only single-digit error rates in language/graph tasks (Rocha et al., 2017, Lancioni et al., 2016).
Annotator Throughput: 3–4× faster annotation speeds (20.8 s/image for the semi-automatic vs. 65.5 s/image for manual in object detection (Subramanian et al., 2018)), and up to 100× reductions in storage requirements in large-scale tracking (Liao et al., 2022).

6. Domain-Specific Adaptations and Generalization

Domain-specific adaptations include:

Video and Multi-Camera Tracking: Trajectory proposal via tracking and re-identification models, with human-assisted cross-camera association (Zhu et al., 2021, Liao et al., 2022).
Scene Graph and Structured Text: Rule-based label and edge proposal with manual correction for scene graphs or procedural text graphs (Zhang et al., 2021, Dufour-Lussier et al., 2012).
Crowdsourcing and Web-Scale Image Annotation: Integration of automatic detection prefilters with web-based human validation (Shao et al., 2019).
Error-Aware Triage: Explicit assignment of "hard" cases to experts and "easy" cases to models under budget constraints for high-quality semi-automatic annotation in NLP and multi-label tagging (Huang et al., 20 May 2024).

These tailored strategies are underpinned by a common design principle: combine automation for scale and repeatability, with targeted, efficient human correction focused where the potential for error or ambiguity is highest.

7. Limitations, Evaluation, and Open Problems

Typical limitations of current semi-automatic annotation processes include:

Drift and Tracking Failures: Even with confidence monitoring, trackers may drift due to occlusions, blur, or new instance appearance. Online detection, correction, and final frame manual checks are required to mitigate error propagation (Zhu et al., 2021).
Domain Transfer: Performance and labeling interface generalization depend on the underlying feature representation and manual intervention; pure rule- or feature-based propagators may face synonymy and ambiguity in linguistic domains (Rocha et al., 2017, Lancioni et al., 2016).
Parameter Sensitivity: Quality depends on proper setting of thresholds, clustering parameters, and model update schedules; insufficient calibration can increase manual correction workload (Adhikari et al., 2020, Jäger et al., 2019).
Limited Evaluations: Several publications lack full-scale human user studies and rely on simulated correction or indirect efficiency metrics; formal studies correlating human time, error, and downstream model performance are an open research need (Zhang et al., 2021).

Continuous integration of more robust models, richer interactive UIs (prompt-based segmentation, graph correction, or projection-guided labeling), and active learning schemes represent ongoing areas for methodological improvement and expansion to new domains.

References:

"Tracking Based Semi-Automatic Annotation for Scene Text Videos" (Zhu et al., 2021)
"GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph" (Zhang et al., 2021)
"Iterative Bounding Box Annotation for Object Detection" (Adhikari et al., 2020)
"Semi-Automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking" (Liao et al., 2022)
"One-Click Annotation with Guided Hierarchical Object Detection" (Subramanian et al., 2018)
"Semi-Automatic Data Annotation guided by Feature Space Projection" (Benato et al., 2020)
"Selective Annotation via Data Allocation" (Huang et al., 20 May 2024)
"Semi-automatic annotation process for procedural texts" (Dufour-Lussier et al., 2012)
"Semi-automatic definite description annotation: a first report" (Rocha et al., 2017)
"Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: the eXtended Revised AraMorph" (Lancioni et al., 2016)
"LOST: A flexible framework for semi-automatic image annotation" (Jäger et al., 2019)