Image-to-Video Transfer Learning

• Image-to-video transfer learning is a paradigm that adapts image-language models to extend spatial representations into temporal domains for video understanding.
• It employs frozen feature preservation and modified adaptation strategies to balance computational efficiency with enhanced temporal reasoning.
• The approach supports tasks ranging from fine-grained temporal grounding to coarse-grained video retrieval, with adapter-based methods showing promising results.

Image-to-video transfer learning is a research paradigm in which representations, knowledge, or trained parameters from powerful image models—typically large-scale image-language foundation models (ILFMs) such as CLIP, BLIP, or MDETR—are adapted, extended, or repurposed to support the analysis and understanding of video. The motivation underlying this paradigm is to alleviate the prohibitive data and computational costs associated with training video-language foundation models from scratch, leveraging the generalizable cross-modal semantic representations already captured by image-text models. Approaches in this field are systematically classified by whether they “preserve” frozen features from ILFMs or “modify” these representations and typically support a diverse array of video understanding tasks, including fine-grained temporal localization, video-text retrieval, captioning, and question answering (Li et al., 12 Oct 2025).

1. Foundations: Image-Language Foundation Models and Transferability

Large-scale ILFMs (e.g., CLIP, BLIP, GroundingDINO, LLaVA) are pre-trained on massive, heterogeneous image-text pairs. These models learn robust visual-semantic embeddings; in CLIP, images and text captions are mapped into a shared latent space via contrastive objectives, while BLIP-style models further support text and fine-grained grounding through encoder-decoder or instruction tuning strategies. Because ILFMs exhibit generalization across tasks and domains, their feature spaces provide a de facto universal interface for downstream adaptation.

A central observation is that ILFM representations can be directly reused for video by treating per-frame image features as temporally independent “snapshots,” although such naively aggregated embeddings lack explicit modeling of video dynamics. The core challenge—thus the focus of image-to-video transfer learning—is how best to equip these spatially-strong but temporally-myopic models for video understanding.

2. Transfer Learning Strategies: Preserving versus Modifying Features

The survey (Li et al., 12 Oct 2025) classifies transfer approaches into two broad categories: frozen feature preservation and representation modification.

Frozen Feature Preservation Strategies:

• The image-LLM parameters are fixed; frame-wise features are extracted and temporal modeling is introduced externally.
• Knowledge distillation can supervise a student video model using the ILFM as a teacher, aligning their latent spaces.
• Post-network tuning attaches lightweight modules (temporal attention, pooling, or transformers) atop frozen frame-level features to aggregate temporal signatures.
• Side-tuning employs learnable, additive “side” networks that fuse with ILFM activations but do not alter the core model, enabling temporal reasoning or task adaptation with minimal risk of forgetting.

Modified Feature (Adaptation) Strategies:

• These update or augment ILFM weights to inject temporal/spatiotemporal sensitivity.
• Full fine-tuning involves replacing or modifying the core architecture (e.g., inserting 3D convolutions, temporal transformer blocks) and updating all parameters.
• Partial tuning restricts learning to a subset of parameters or newly injected modules to retain base knowledge.
• Adapter-based approaches (e.g., prompt tuning, LoRA, or light bottleneck layers) add minimal, trainable modules—often before/after self-attention or MLP blocks—that can capture temporal dependencies.
• Prompt tuning introduces learned tokens to modulate representations, steering the model toward video-specific reasoning.
• LoRA implements low-rank parameter updates, formalized as $\Delta W = B \cdot A$, enabling efficient adaptation.

The primary trade-off is that “frozen” approaches maximize efficiency and knowledge retention, whereas “modified” strategies afford richer temporal modeling, often at the cost of greater compute and potential forgetting.

3. Application Landscape: Video-Text Learning Tasks

Image-to-video transfer learning enables a broad set of video tasks, which the survey categorizes by granularity:

Task Class	Examples	Granularity
Fine-grained	Temporal video grounding, OV-MOT, OV-VIS, RVOS	Frame-level, object-level
Coarse-grained	Video-text retrieval, video QA, action recognition, captioning	Global/video-level

• Fine-grained tasks (multi-object tracking, temporal/spatiotemporal video grounding, open-vocabulary segmentation) demand precise temporal or spatial localization, e.g., referring expression motion tracking or spotting moments conditioned on text.
• Coarse-grained tasks abstract over the entire clip, requiring classification, open-ended description, or answering natural language queries over the full video content.

The ability of ILFMs to provide strong spatial (object/scene) cues is exploited by transfer learning methods to bootstrap more data- and compute-efficient video models that can ground events or summarize action semantics.

4. Empirical Findings and Comparative Performance

Experimental analyses reveal that the choice of transfer approach (frozen vs. adaptive) is highly task-dependent:

• Fine-grained tasks such as spatio-temporal grounding and open-vocabulary object tracking benefit from temporal modules tightly integrated with spatial features (e.g., adapter-based or auxiliary model methods), achieving higher IoU/vIoU and tracking/association scores.
• Coarse-grained tasks like video-text retrieval and action recognition show that LoRA-based or partial fine-tuning methods yield higher recall and overall accuracy, leveraging both pretrained representation and temporal adaptation.
• The incorporation of external auxiliary features (e.g., optical flow, teacher supervision) further augments localization and temporal sensitivity.
• The survey notes that adaptation methods outperform purely “frozen” approaches for temporally sensitive metrics, while maintaining competitive parameter efficiency. Conversely, frozen-feature methods maximize retention and resource efficiency but offer weaker temporal reasoning.

5. Methodological and Practical Challenges

Image-to-video transfer learning encounters several unresolved challenges as catalogued in the survey:

• Domain gap: Static image–trained ILFMs do not natively encode temporal dependencies, necessitating sophisticated modules to bridge the semantic gap introduced by video dynamics.
• Lack of unified frameworks: Most existing methods are task- or dataset-specific, lacking generalizability or modularity for multi-task adaptation.
• Catastrophic forgetting: Full fine-tuning approaches risk overwriting spatial–semantic alignment, requiring careful constraint or regularization.
• Resource bottlenecks: Training video-LLMs from scratch is computationally expensive; parameter-efficient transfer learning mitigates but does not entirely remove the barrier.

A plausible implication is that advances in unified parameter-efficient architectures, prompt sharing, or shared adapter designs could enable more scalable and robust video understanding across diverse tasks.

6. Emerging Directions and Prospects

The survey (Li et al., 12 Oct 2025) identifies several promising avenues for future research:

• Unified paradigms: Joint parameter-efficient designs that can port a single ILFM to a suite of video-language tasks via modular prompts or adapters.
• Multi-model fusion: Combining visual, textual, and motion-specific pretrained models through advanced cross-modal fusion (e.g., transformers, cross-attention, or graph-based fusion).
• Advanced fusion for temporal modeling: Cross-modal transformers or hierarchical interaction schemes for spatio-temporal alignment across frames.
• Data- and computation-efficient tuning: Further refining LoRA, prompt tuning, or side-tuning to minimize adaptation resource costs.
• Fine-grained annotation efficiency: Leveraging self-supervised or weakly-supervised objectives to compensate for the dearth of labeled video data.

A plausible implication is that as model scales and video data volumes increase, image-to-video transfer learning will remain crucial, but solutions will increasingly prioritize modularity, shareable components, and robust multi-task capability.

7. Mathematical Formalisms and Evaluation Metrics

Representative equations highlighted in the survey include:

• LoRA adaptation update: $\Delta W = B \cdot A$, with $B \in \mathbb{R}^{d \times r}$, $A \in \mathbb{R}^{r \times k}$, $r \ll \min(d, k)$.
• Video IoU for spatio-temporal grounding: $$vIoU = \frac{1}{|T_u|} \sum_{t \in T_i} \textrm{IoU}(\hat{b}_t, b_t)$$, $tIoU = \frac{|T_i|}{|T_u|}$, where $T_i$ and $T_u$ are interval intersections/unions and $\hat{b}_t$, $b_t$ are predicted and ground-truth boxes.

These formalisms allow for quantitatively benchmarking localization, grounding, and retrieval performance across transfer learning regimes.

Conclusion

Image-to-video transfer learning leverages the generalizability and cross-modal alignment of image-language foundation models to efficiently extend their capabilities into the video domain. Methods are distinguished by whether they retain the spatial semantics of the ILFM unchanged or adapt them for temporal reasoning. Empirical evidence demonstrates that, with the appropriate temporal modules or adapters, transferred features can enable competitive accuracy on both fine- and coarse-grained video tasks. Continuing developments focus on parameter efficiency, unified architectures, and richer support for temporally complex phenomena, directly propelling progress in practical video-language learning (Li et al., 12 Oct 2025).