Efficient Multimodal Encoders: A Comparative Review (2020-2025)
AR
Aneek Roy
•20 min read•Updated: Jan 2026
Abstract
This post surveys the rapid evolution of efficient encoders for multimodal (Vision-Language) models. We analyze key contributions from ICML, ICLR, NeurIPS, CVPR, and ECCV over the past 5 years, highlighting the transition from large-scale frozen transformers to dynamic, token-pruned architectures. We discuss methodologies including "Frozen" LLM adaptation, Q-Former bridges, Reinforcement Learning-based token selection, hybrid CNN-Transformer architectures, and attention-based token pruning.
The scaling laws of deep learning have historically driven performance improvements through massive parameter increases. However, the deployment of multimodal models on edge devices requires a fundamental shift towards efficiency. Since the introduction of CLIP in 2021, the research community has achieved approximately a 54x reduction in parameter count for comparable zero-shot accuracy on specific benchmarks.
The following figure summarizes the aggregate progress observed across major conferences.
85x
TTFT Speedup
~0.8G
Min FLOPs (2025)
RL+MoE
Emerging Trends
61
Papers Reviewed
Figure 1. Key efficiency metrics derived from the surveyed literature (2020-2025).
2. The Efficiency Landscape
To visualize the trade-off between computational cost and model performance, we map key papers onto a FLOPs vs. Accuracy plane. The ideal trajectory moves towards the top-left corner (High Accuracy, Low Compute).
Figure 2. Pareto frontier of efficient encoders. Blue: NeurIPS, Cyan: ICML, Light Blue: ICLR, Green: CVPR, Orange: ECCV.
Note: Newer papers (2025) are represented by larger nodes.
3. Methodological Categorization
Click on a category to explore papers using that methodology.
Frozen Foundations
Methods like BLIP-2, Frozen, and Flamingo leverage pre-trained LLMs without updating their weights. They introduce lightweight bridging modules (e.g., Q-Former, Perceiver Resampler) to align visual features, drastically reducing training costs.
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Dynamic Computation
Recent works like VisionThink (2025) and FastV (2024) utilize RL or attention-based methods to dynamically adjust the visual token budget based on complexity, departing from static architectural pruning.
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Hybrid Architectures
MobileViT, FastVLM, and TinyViT combine the local feature extraction of CNNs with global attention of Transformers, achieving mobile-friendly efficiency without sacrificing accuracy.
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Token Compression
ToMe, SparseVLM, and LLaVA-Mini reduce visual tokens via merging, pruning, or compression. These methods achieve 50-77% FLOPs reduction while maintaining task performance.
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4. Literature Review61 Papers • Searchable
5. Conclusion
The trend towards efficiency in multimodal learning has shifted from simple parameter reduction to intelligent computation. While 2021-2023 focused on architectural bottlenecks (Perceiver, MobileViT, TinyViT), 2024-2025 has introduced dynamic, data-centric approaches where the model decides where to look and how much compute to expend. Key innovations include attention-based token pruning (FastV), hybrid vision encoders (FastVLM), and RL-driven adaptive resolution (VisionThink).