Hybrid Encoder–Decoder Framework

• Hybrid Encoder–Decoder Framework is an architectural scheme that decouples visual feature extraction from sequence modeling by combining CNNs and transformers.
• It employs frequency regularization using DCT to compress CNN weights, reducing model parameters while highlighting a trade-off in caption accuracy.
• Comparative analysis shows EfficientNetB1 delivers superior performance, whereas MobileNetV2 offers computational efficiency for resource-constrained environments.

A hybrid encoder–decoder framework in contemporary deep learning denotes an architectural scheme that combines distinct encoding and decoding modules, often leveraging heterogeneous model classes or information flows, to map complex inputs to structured outputs across various modalities. Unlike standard encoder–decoder paradigms—which pass representations sequentially from a monolithic encoder to a single decoder—hybrid encoder–decoder frameworks adapt the flow, connectivity, or model composition to better exploit task- and modality-specific characteristics. The paper "Compressed Image Captioning using CNN-based Encoder-Decoder Framework" (Ridoy et al., 28 Apr 2024) presents a prototypical instance in the image captioning domain, integrating frozen pre-trained CNNs for visual feature extraction with a transformer-based encoder–decoder architecture for text generation, and further exploring frequency regularization as a hybrid model compression strategy.

1. Architectural Composition: CNN-Transformer Hybridization

The principal architectural motif is a chained three-stage pipeline:

1. Visual Feature Extraction: A pre-trained CNN (selected from EfficientNetB0, EfficientNetB1, ResNet50, or MobileNetV2) functions in feature-extractor mode. Images are preprocessed to canonical sizes, passed through the fixed weights of the backbone CNN, and condensed into high-level visual feature vectors.
2. Encoder: The extracted visual features are projected into a transformer encoder, which refines and contextualizes the representations. The choice of transformer architecture enables the encoder to model inter-feature dependencies, even though the incoming input is typically a single global representation.
3. Decoder: A transformer-based sequence decoder receives both the encoded image representation and the caption prefix (provided via ground truth during training, autoregressively during inference). The decoder outputs the probability of each successive word:

$$P(S|I) = \prod_{t=1}^{T} P(w_t | I, w_1, ..., w_{t-1})$$

where $S$ denotes the sequence of output words, and $I$ the image input.

This architecture benefits from decoupling feature extraction and sequence modeling and enables modularity—different visual backbones can be plugged into the encoder, which itself is flexible with respect to the textual modeling.

2. Model Compression via Frequency Regularization

To address the significant parameter and compute burden of deep CNNs (particularly AlexNet and EfficientNetB0), the framework incorporates frequency regularization as detailed in [Zhao et al., 2023]. The compression process operates in the following steps:

• Frequency Domain Transform: Convolutional weights $W_{spatial}$ are transformed into the frequency domain $W_{freq}$ using Discrete Cosine Transform (DCT):

$W_{freq} = \text{DCT}(W_{spatial})$

• Parameter Truncation: High-frequency coefficients, commonly carrying less semantic content, are pruned by masking or truncation. Only low-frequency components, determined by a mask $M$, are retained:

$W_{freq}^{compressed} = W_{freq} \circ M$

• Inverse Transform: The compressed coefficients are returned to the spatial domain by inverse DCT:

$$W_{spatial}^{compressed} = \text{IDCT}(W_{freq}^{compressed})$$

• Training Dynamics: The compression threshold or mask can be annealed or dynamically adjusted during training.

The effect is to drastically reduce model parameters and computational burden. However, current experiments show that while parameter reduction is effective (e.g., AlexNet/UNet achieves several orders of magnitude reduction in unrelated work), actual captioning accuracy on AlexNet and EfficientNetB0 drops precipitously (~10% accuracy), making such models impractical for deployment in the studied setting.

3. CNN Backbone Variants and Comparative Performance

Using the COCO2014 dataset, the framework quantitatively benchmarks several CNN backbones as encoders within the hybrid encoder–decoder system. The comparative results appear in the following table:

Model	BLEU-1	BLEU-2	BLEU-3	BLEU-4	ROUGE-1	ROUGE-2	ROUGE-L	METEOR
EfficientNetB0	0.2827	0.1325	0.0588	0.0266	0.4027	0.1469	0.3609	0.2661
EfficientNetB1	0.2890	0.1404	0.0642	0.0286	0.4117	0.1551	0.3718	0.2710
ResNet50	0.2637	0.1217	0.0496	0.0207	0.3765	0.1308	0.3423	0.2437
MobileNetV2	0.2106	0.0640	0.0215	0.0090	0.2903	0.0628	0.2606	0.1794

Findings from these results:

• EfficientNetB1 yields the highest scores across all metrics, demonstrating the advantage of recent, well-optimized architectures for representation quality.
• MobileNetV2 trades off accuracy for computational efficiency; despite performing poorly in absolute terms, it is most resource-efficient and can be justified for edge scenarios where compute is highly constrained.
• The hybrid scheme's decoupled design enables easy benchmarking and substitution of visual extractors without disturbing the caption generation stack.

4. Operational Flow and Implementation Details

The full workflow may be summarized by the following diagram:

$$\boxed{ \begin{array}{c} \text{Input Image} \ \downarrow\, \text{(Pre-trained CNN)} \ \text{Visual Feature Vector} \ \downarrow\, \text{(Transformer Encoder)} \ \text{Encoded Representation} \ \downarrow\, \text{(Transformer Decoder, w/ caption)} \ \text{Generated Caption} \end{array} }$$

Additional implementation properties:

• All CNN backbones are kept frozen (weights non-trainable, as in feature extractor mode) to leverage ImageNet pretraining and prevent overfitting.
• During training ("teacher forcing"), the decoder is fed ground truth captions; at inference, it autoregressively generates tokens until an end-of-sequence marker is produced.
• Model compression steps (if enabled) are applied solely to the CNN weights prior to or during training; all feature encoding and text generation steps remain otherwise unmodified.

5. Trade-offs, Limitations, and Deployment Considerations

Empirical evidence from the paper reveals several trade-offs:

• Performance vs. Efficiency: Using a deeper, more parameter-rich backbone such as EfficientNetB1 provides superior accuracy over more lightweight models like MobileNetV2 but at increased computational cost.
• Model Compression: Frequency regularization currently leads to significant accuracy degradation. The technique holds promise if future architectural or algorithmic advances can better preserve discriminative capacity under sparsification.
• Resource Constraint Suitability: For settings with rigid compute/memory limits but some tolerance for reduced accuracy, MobileNetV2 can serve as a practical choice; otherwise, EfficientNetB1 is optimal if the resource budget permits.

These deployment observations reflect the flexible modularity of the hybrid approach—a core benefit for real-world adaptation and system design.

6. Summary Table: Hybrid Encoder–Decoder Pipeline

Stage	Technique	Remarks
Feature Extraction	Pre-trained CNN (EffNetB1, ... )	Frozen weights, outputs fixed-length feature
Encoding	Transformer Encoder	Refines and contextualizes visual features
Decoding	Transformer Decoder	Autoregressively generates caption tokens
Compression	Frequency Regularization (DCT/IDCT)	Experimental; compresses CNN, not used in prod.
Evaluation	BLEU, ROUGE, METEOR	EffNetB1 best; MobileNetV2 most efficient

7. Implications and Future Directions

The hybrid encoder–decoder framework exemplified here reflects an architectural trend: modular, resource-efficient deep learning pipelines that decouple feature extraction from sequence modeling, thereby enabling flexible adaptation, easy hardware deployment, and rapid benchmarking of component advances. The demonstrated integration of model compression (frequency regularization) also illustrates the growing importance of parameter-efficient design, though further work is needed to avoid catastrophic performance loss. The inclusion of multiple backbone options and careful empirical comparison in the studied framework may guide practitioners in resource-constrained deployment and further research in compressed sequence modeling architectures.

This approach underscores the enduring utility of the hybrid encoder–decoder paradigm for bridging complex data modalities—here, vision and language—via principled, modular deep learning design (Ridoy et al., 28 Apr 2024).