Frozen Vision-Language Models

• Frozen vision-language models are multimodal systems that keep large pretrained backbones fixed while training small, task-specific modules.
• They facilitate efficient training and robust zero-shot performance across applications like object detection, semantic segmentation, and medical imaging.
• Recent work demonstrates significant compute savings and scalability, though challenges remain in fine-grained detail retention and multimodal integration.

Frozen vision-LLMs (VLMs) are a class of multimodal models in which large, pretrained visual and/or language foundation components are kept fixed—i.e., their parameters are not updated—while lightweight task-specific modules are trained atop these frozen representations. This paradigm leverages the generalizable knowledge and high-capacity features learned during pretraining on massive image–text datasets, providing both computational efficiency and robust zero-shot adaptability for a variety of downstream vision–language tasks. The frozen VLM approach has led to a series of advances in object detection, semantic segmentation, robust multimodal understanding, probabilistic uncertainty estimation, and efficient deployment across domains such as medical imaging and robotics.

1. Architectural Principles and Frozen Backbone Utilization

The central innovation of frozen VLMs is the decoupling of representation learning and task adaptation. Foundational visual encoders (typically vision transformers or CNNs pretrained via contrastive or multimodal objectives, e.g., CLIP) and language encoders (LLMs, e.g., BERT or Qwen) are kept frozen. Only small downstream modules—such as detection/classification heads, cross-modal transformers, or adapters—are trained for the target application (Kuo et al., 2022, Ma et al., 2022, Wang et al., 2023, Upadhyay et al., 2023). This design confers the following advantages:

• All fixed weights preserve the broad knowledge and locality-sensitive features gained from pretraining (e.g., spatial boundaries, open-vocabulary semantics).
• Task adaptation is parameter- and compute-efficient—fewer layers to update results in faster training, less risk of overfitting, and easier scaling to high-capacity backbones.
• In heterogeneous or evolving deployment environments, downstream modules can be retrained for new tasks or vocabularies without expensive end-to-end retraining of the entire foundation model.

A typical architectural workflow:

Stage	Component	Frozen?	Example Implementation
Representation Extraction	Visual Backbone	Yes	CLIP-ViT-L/14, MoCo v3, DINO
	Language Encoder	Yes	BERT, CLIP text encoder
Feature Fusion / Adapter	Cross-modal module	No	Tranformer fusioner, detector head
Task Head	Downstream head	No	Open-vocab classifier, segmentation head

2. Model Design Patterns and Fusion Modules

Frozen VLM systems employ a range of adapter and fusion strategies to align visual and linguistic spaces:

• Text-embedding based region classification replaces the FC layer of classical detectors, computing cosine similarity between frozen region features and text embeddings for open-vocabulary predictions (Kuo et al., 2022).
• Transformer-based cross-modal fusion integrates visual and language tokens in designated fusion modules (e.g., Fusioner), where both frozen representations are projected to a joint latent space and updated via self- and cross-attention (Ma et al., 2022).
• Deep fusion via visual expert modules (as in CogVLM) augments every transformer block in the (frozen) LLM with trainable QKV matrices and feedforward subnets for visual features, enabling deep integration at all layers while maintaining NLP task generalization (Wang et al., 2023).
• Q-Former-based alignment bridges frozen image encoders and LLMs by mapping fixed visual tokens into a learnable query-based intermediary, either for direct feeding to the LLM decoder or for conditioning intermediate LLM latent states (Choraria et al., 2023).
• Multi-encoder fusion instead of single vision encoder: BRAVE consolidates the outputs of multiple frozen encoders with distinct inductive biases via a multi-encoder querying transformer (MEQ-Former), robustly mitigating “blindness” and hallucination phenomena (Kar et al., 10 Apr 2024).

3. Performance, Scalability, and Efficiency

Frozen VLMs demonstrate strong scaling properties and high performance on open-vocabulary, cross-domain, and zero-shot tasks:

• Object Detection: F-VLM achieves substantial improvements—+6.5 mask AP for novel categories on LVIS—by training only a lightweight detector head atop frozen CLIP backbones. Increasing backbone capacity (e.g., from R50 to R50x64) further boosts detection AP without growing the number of trainable parameters (Kuo et al., 2022).
• Semantic Segmentation: Fusioner, using frozen vision/language backbones and an efficient fusion transformer, surpasses previous SOTA on PASCAL-5i and COCO-20i open-vocabulary benchmarks and retains robustness in synthetic compound settings (Ma et al., 2022).
• Training Efficiency: By updating only adapter heads, frozen VLMs achieve dramatic compute savings (up to 226× less compute (Kuo et al., 2022)); training iterations required for convergence drop by an order of magnitude.
• Energy and Latency: FrEVL demonstrates that precomputing frozen embeddings yields ~2.3× inference speedup and 52% reduction in energy consumption, supporting deployment in resource-constrained or real-time applications (Bourigault et al., 6 Aug 2025).

4. Bayesian Extensions and Probabilistic Embeddings

Recent research highlights that deterministic frozen embeddings lack credible uncertainty estimates. To address this:

• ProbVLM and GroVE retrofit frozen VLMs with probabilistic adapters. These models map input images and text into distributions in embedding space rather than fixed points, capturing epistemic and aleatoric uncertainty. For example, GroVE fits a Gaussian Process Latent Variable Model (GPLVM) post-hoc atop frozen representations, enabling downstream tasks (retrieval, VQA, active learning) with well-calibrated uncertainty and improved sample selection (Venkataramanan et al., 8 May 2025, Upadhyay et al., 2023).
• Probabilistic embeddings help model many-to-many semantic correspondence, enable active learning sample selection, and provide principled performance estimates in ambiguous or safety-critical scenarios.

5. Domain Extensions and Hybridization

The frozen VLM paradigm has been successfully extended and hybridized across multiple domains:

• Medical Imaging: M-FLAG fixes the language encoder (medical BERT variant), optimizes only the vision encoder and a projector, and regularizes latent geometry with an orthogonality loss. This results in up to 78% parameter savings and SOTA on classification, segmentation, and detection—even with 1% fine-tuning data on RSNA segmentation (Liu et al., 2023).
• Robotics and Multimodality: Modalities beyond vision (e.g., inertial measurement unit data) can be mapped into the same frozen vision latent space by training a modality-specific encoder with a frozen vision backbone as target, using contrastive (infoNCE) objectives. This allows for scalable extension to arbitrary modalities without full model retraining (Tavassoli et al., 2023).
• Time Series and Structured Symbolic Integration: Frameworks such as Time-VLM and SG-VLM incorporate frozen VLMs as feature extractors in complex pipelines, fusing multimodal embeddings (from images, text, and temporal features or symbolic scene graphs) to improve forecasting or video question answering. These settings highlight the compatibility and modularity of the frozen VLM paradigm for hybrid approaches (Zhong et al., 6 Feb 2025, Ma et al., 15 Sep 2025).

6. Open Questions, Limitations, and Future Directions

Despite the efficiency and high generalization capability, frozen VLMs introduce several challenges and limitations:

• Loss of Fine-Grained Detail: Studies show that standard practice of freezing vision encoders (e.g., CLIP) impedes recovery of pixel-level detail, leading to suboptimal performance on low-level or fine-grained segmentation tasks unless the visual backbone is partially adapted (e.g., via pixel value prediction) (Gou et al., 7 Aug 2024).
• Integration Bottlenecks and Brittleness: In complex VLM pipelines, vision features can be preserved through fusion and projection modules but may be “washed out” at the final language generation stages, with the LLM component exhibiting a strong prior that overrides visual evidence (Fu et al., 9 Jun 2025). Prompt formulation has only limited ability to alleviate this, as the LLM’s inherent language biases dominate. Performance on vision-centric tasks can drop to near-chance, making proper multimodal integration at the language decision layer a central challenge.
• Parameter Redundancy and Efficient Inference: Empirical analyses (e.g., ShortV) reveal that many transformer layers are redundant for visual tokens. Freezing computations for these tokens in low-contribution layers yields up to 50% FLOPs reduction on large-scale models like LLaVA-NeXT-13B with essentially no loss in accuracy (Yuan et al., 1 Apr 2025).
• Task Suitability: Frozen embedding approaches perform best when downstream tasks closely match foundation model pretraining objectives (contrastive image-text alignment). Tasks requiring information absent from the frozen embedding—such as OCR, numerical counting, or spatial reasoning—may necessitate customized adapters, hybridization, or partial fine-tuning (Bourigault et al., 6 Aug 2025).
• Probabilistic and Adaptive Extensions: While post-hoc probabilistic adapters produce calibrated uncertainty, inference can be expensive (e.g., GP-based systems as in GroVE). Future research aims for faster, equally reliable uncertainty estimation methods, as well as modular strategies to dynamically select or fuse visual embeddings (as in BRAVE’s future directions).

7. Theoretical Underpinnings and Scaling Laws

Information-theoretic analysis formalizes the conditions under which frozen embeddings are effective, with downstream performance gaps bounded by the difference in conditional entropy between the full and the frozen-embedding information (Bourigault et al., 6 Aug 2025):

$$\Delta_\text{perf} \leq C \cdot [\mathcal{H}(Y|\mathbf{v},\mathbf{t}) - \mathcal{H}(Y|V,T)]$$

where $\mathcal{H}(\cdot)$ denotes conditional entropy, and $C$ depends on the loss and model capacity. As long as the frozen embeddings preserve almost all the discriminative information relevant for $Y$, downstream performance will closely approximate end-to-end fine-tuned models.

Scaling laws observed empirically show that, within the frozen VLM paradigm, improvements to frozen backbone capacity transfer directly to detection and VQA performance, even when the number of trainable task parameters is held fixed (Kuo et al., 2022, Kar et al., 10 Apr 2024).

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In summary, frozen VLMs represent a principled, efficient solution for open-vocabulary, multimodal understanding and generation. By retaining fixed, high-capacity pretrained visual and/or language backbones and training only lightweight task adapters or fusion modules, these systems enable efficient adaptation, robust zero-shot transfer, and modular extensibility across tasks and domains. Ongoing research is focused on bridging their limitations in integration, detail retention, and uncertainty calibration, while extending their reach to increasingly diverse and multimodal real-world scenarios.