Guided Attentive Interpolation (GAI)

• Guided Attentive Interpolation (GAI) is an attention-based method that interpolates between feature domains to maintain semantic fidelity and smooth transitions.
• It is applied in few-shot forgery detection, semantic segmentation, and text-to-image diffusion, enhancing accuracy, spatial coherence, and sample quality.
• GAI replaces traditional blending techniques by using learned attention weights for guided mixing, leading to improved data efficiency, transfer, and fidelity preservation.

Guided Attentive Interpolation (GAI) encompasses a family of techniques that utilize attention-based interpolation to enhance semantic alignment, smoothness, and diversity in supervised learning and generative tasks. Initially developed in distinct fields—few-shot forgery detection, efficient semantic segmentation, and text-to-image diffusion—GAI systematically leverages learned affinities or attention weights, rather than simple geometric or embedding-based mixing, to interpolate between diverse feature domains, generations, or visual concepts. These methods consistently outperform naïve linear interpolation in embedding or pixel space, providing principled approaches to address transfer, data scarcity, and fidelity preservation in contemporary deep learning systems (Qiu et al., 2022, Cheng et al., 3 Jan 2026, He et al., 2024).

1. Key Principles and Unified Perspective

All variants of Guided Attentive Interpolation replace classical mixing procedures—such as pixelwise blending, feature upsampling, or embedding interpolation—with guided mechanisms that operate within, or directly inform, the attention modules of neural networks. This paradigm is motivated by several common challenges:

• Semantic misalignment: Geometric or embedding interpolation often fails to respect the semantic structure of the source domains, leading to artifacts or loss of detail.
• Insufficient context or diversity: Classic interpolation schemes do not capture long-range dependencies or preserve rare, domain-specific features when data is scarce.
• Poor generalization to novel domains: Models relying on abundance in the training data (majority classes, base prompts) underperform in settings requiring adaptation to previously unseen or minority domains.

By directly interpolating within the attention space—whether at the feature map, token, or key-value level—GAI enables data-efficient transfer and smooth, context-aware transitions, yielding better performance and sample fidelity.

2. Methodologies and Formulations

Few-shot Forgery Detection

In the context of few-shot forgery detection, GAI creates synthetic samples by adversarially blending rare “minority” forgery examples ($x^{\mathrm{minor}}$) with “majority” samples ($x^{\mathrm{major}}$), optimizing a spatial interpolation tensor $\alpha \in [0,1]^{H \times W \times 3}$. The objective combines:

• Minority guidance: Cross-entropy loss pushes the teacher network to classify interpolated samples as the minority class.
• Majority suppression: A restraining loss discourages the student from predicting the original majority label.
• Smoothness: Total-variation loss on $\alpha$ maintains plausible visual quality.

The procedure alternates forward/backward passes through the teacher and student, updating $\alpha$ to generate $$x^{\mathrm{adv}} = \alpha \odot x^{\mathrm{major}} + (1-\alpha) \odot x^{\mathrm{minor}}$$ (Qiu et al., 2022).

Feature Upsampling for Semantic Segmentation

In segmentation, GAI interpolates between coarse (semantic) and fine-grained (detail-rich) feature maps. Each high-resolution position $p$ attends over a criss-cross neighborhood in the upsampled coarse feature map via:

• Query/key/value projections: Queries are extracted from the concatenated fine and upsampled coarse features, while keys/values derive from the coarse features.
• Criss-cross attention: Affinities $A_{p,i}$ are computed as dot products along shared rows/columns, reducing computation while enhancing spatial-semantic alignment.
• Weighted aggregation: The upsampled feature at each location is computed as a weighted sum over the attended values.

The process yields feature maps both semantically enriched and spatially coherent, outperforming bilinear upsampling and other flow-based methods (Cheng et al., 3 Jan 2026).

Attentive Interpolation for Text-to-Image Diffusion

In text-conditioned diffusion models, GAI operates either at the inner (key/value mix) or outer (output mix) level within cross-attention modules:

• Inner interpolation: Blends $K_{\mathrm{mix}} = (1-t)K_1 + tK_2$, $V_{\mathrm{mix}} = (1-t)V_1 + tV_2$ before attention, with $t$ sampled from a Beta distribution for smooth transitions.
• Outer interpolation: Attends independently to both sources, then linearly combines the resulting outputs.
• Self-attention fusion: Blends interpolated cross-attention output with self-attention, using a learned scalar $\lambda$.
• Prompt guidance (PAID variant): Introduces time-scheduled weights for prompt embeddings, facilitating warm-up and controlled prompt composition.

This mechanism delivers sharper, more consistent interpolated samples between conditional prompts compared to naïve embedding-space interpolation (He et al., 2024).

3. Architectural Realizations

The following table summarizes core GAI architectural strategies across different domains:

Domain/Task	GAI Mechanism	Key Implementation Details
Few-shot Forgery Detection	Image-space, adversarial interpolation	Per-pixel $\alpha$, teacher-guided optimization
Semantic Segmentation	Feature-space, cross-layer attention	Criss-cross, dimensionality-reduced affinities
Text-to-Image Diffusion	Inner/outer attention interpolation	Key/value mixing, Beta-scheduled blending, fusion

In all cases, careful selection of guidance networks, loss terms, and sampling strategies is critical for effective and robust interpolation.

4. Empirical Findings and Ablation Studies

Few-shot Forgery Detection

• GAI boosts minority class accuracy by 2–4 percentage points over oversampling or mixup baselines, e.g., from 75.14% to 78.89% on Group1_FSG (ACC_minor).
• Adaptive per-pixel $\alpha$ and teacher-driven optimization are both essential; fixed $\alpha$ or simple mixup yields marked degradation.

Semantic Segmentation

• On Cityscapes, using two GAI modules with a ResNet-18 backbone achieves 78.8% mIoU at 22.3 FPS, outperforming bilinear, CARAFE, and flow-alignment upsampling.
• Single GAI module ablations show coupled use of high- and low-res features as query yields maximal gain; criss-cross attention is favorable in efficiency-accuracy trade-offs.

Text-to-Image Diffusion

• Attentive interpolation reduces FID (28.4 to 24.7) and average LPIPS by 12% versus linear embedding interpolation.
• User studies report 76% preference for GAI-based interpolations for smoothness and consistency.
• Inner interpolation favors conceptual blending, outer preserves spatial layout; optimal fusion weights (λ in 0.2–0.5) balance guidance and quality.

5. Practical Guidelines and Limitations

• Use per-element or tokenwise interpolation weights in GAI for maximal flexibility and domain adaptation; scalar mixing tends to underexploit available information.
• Teacher networks or guidance heads must be well-calibrated and pretrained whenever minority/novel domains are the interpolation target.
• For diffusion and generative settings, sample interpolation weights from smooth distributions (e.g., Beta) to avoid visual artifacts and abrupt conceptual jumps.
• GAI's computational overhead is often manageable (e.g., <25% additional FLOPs in real-time segmentation), but further optimization may be required for embedded or large-scale deployments.

Limitations include residual interpolation artifacts for extremely distinct domains, hyperparameter sensitivity (especially in the Beta schedule), and potential ineffectiveness when the teacher or guidance head is poorly adapted or undertrained (Qiu et al., 2022, Cheng et al., 3 Jan 2026, He et al., 2024).

6. Extensions and Future Directions

Future research directions suggested by GAI's current trajectory include:

• Generalization to other modalities: Applications in video (with temporal attentive interpolation), depth estimation, and optical flow are plausible, leveraging cross-layer or cross-modal semantic alignment (Cheng et al., 3 Jan 2026).
• Hybrid and adaptive attention structures: Learning or dynamically inferring sparse attention patterns, beyond criss-cross or global modules, may improve efficiency and context capture.
• Classifier-free and multi-way guidance: Combining GAI with classifier-free or multi-prompt guidance, potentially using Dirichlet priors for multi-way interpolation, can enhance generative control (He et al., 2024).
• Integration with ensemble or self-supervised frameworks: Using GAI as a module in broader ensembles or semi-supervised settings may further improve transfer to novel, low-resource domains.

A persistent challenge is optimizing the guidance and mixing schedule for domain-specific smoothness and transitive consistency, especially as the diversity and semantic drift among source domains increases.

7. Related Techniques and Distinctions

GAI can be contrasted with traditional data augmentation, geometric interpolation, or mixup strategies as follows:

Approach	Interpolation Level	Guidance Source	Semantic Fidelity
Classic mixup	Embedding or pixel-space	None	Low to medium
Bilinear upsampling	Coordinate grid	Pixel locations	Generally low
GAI	Attention/module-level	Teacher, features	High

GAI's unique property is the explicit use of guiding networks or features to adaptively determine interpolation weights, thereby capturing transferable characteristics across domains and ensuring sample realism and semantic coherence.

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References:

• Few-shot Forgery Detection via Guided Adversarial Interpolation (Qiu et al., 2022)
• Cross-Layer Attentive Feature Upsampling for Low-latency Semantic Segmentation (Cheng et al., 3 Jan 2026)
• AID: Attention Interpolation of Text-to-Image Diffusion (He et al., 2024)