Domain Adaptation in Vision-Language Models (2023–2025): A Comprehensive Review
Domain Adaptation in Vision-Language Models (2023–2025): A Comprehensive Review
Overview
Recent research (2023–2025) has increasingly focused on adapting large Vision-Language Models (VLMs) to new domains and tasks with minimal supervision. A core trend is to leverage the rich “world knowledge” encoded in large-scale VLMs (e.g. CLIP, Flamingo, PaLI-X) and reason through language to improve zero-shot and few-shot generalization . Methods draw on techniques like intermediate language inference (generating textual explanations or descriptions as an intermediate step), reinforcement learning (RL) optimization (using reward signals to fine-tune multimodal policies), and instruction tuning (multi-task fine-tuning with natural language prompts) to transfer knowledge across visual domains. Application domains include visual question answering (VQA), image captioning, open-vocabulary recognition (classification/detection/segmentation of novel classes), and broader vision-language reasoning tasks. The table below summarizes key representative works from major venues (CVPR, ICCV, ECCV, ICLR, ICML, NeurIPS, ACM MM, TPAMI) in 2023–2025, highlighting their motivation, approach, results, and noted limitations.
Representative Works (2023–2025)
| Work Venue (Year) | Motivation | Model & Method | Training Setup & Key Results | Limitations / Future Directions |
|---|---|---|---|---|
| CIGAR CVPR 2023 | Address unsupervised domain adaptive object detection where prior graph-based UDA methods ignore language info. Improve detection robustness across domains by using semantic label knowledge. | Proposes a Cross-Modality Graph Reasoning framework: constructs a visual feature graph and a linguistic (label) graph, and performs iterative cross-graph reasoning to enrich object representations with semantic context . Also introduces a discriminative feature selector to choose informative visual nodes. | No target labels used; train on labeled source + unlabeled target. The linguistic graph is derived from class labels (text) and aligned with visual graph via a matching loss . Achieved improved mAP on cross-domain detection benchmarks over visual-only adaptation methods . | Relies on predefined class labels as language knowledge, which may not cover more complex domain shifts. Future work could explore using richer language descriptions or captions for finer-grained domain adaptation. |
| RISE ICCV 2023 | Enable domain generalization by distilling the generalizable semantic knowledge of a large VLM into a smaller model. Leverage concise language descriptions to capture domain-invariant concepts . | Regularized Invariance with Semantic Embeddings (RISE): uses a CLIP teacher (image & text encoders). The student model’s image features are regularized to align with the teacher’s text embeddings of the corresponding image description . Introduces absolute and relative distance losses to guide this alignment. | Trained on multiple source domains’ images with their text descriptions (captions). No target-domain data needed (zero-shot domain generalization). Outperforms prior state-of-the-art DG methods on benchmarks (PACS, OfficeHome), showing that text-informed distillation improves robustness to unseen domains . | Requires a descriptive sentence for each image (may come from captions or human annotation), which might not be available for all data. The method focuses on classification tasks; extending it to tasks like detection or more fine-grained domain shifts (where a single sentence may not capture all variability) remains an open challenge. |
| SelTDA CVPR 2023 | Tackle data-scarce VQA domains (e.g. medical or knowledge-based VQA with very few Q&A pairs). Avoid overfitting and loss of reasoning skills when fine-tuning on small datasets by exploiting unlabeled images . | Self-Taught Data Augmentation (SelTDA): employs the large VQA model itself as a teacher to generate new questions & answers for unlabeled images. The VLM is prompted to produce likely Q–A pairs from an image alone (no human annotation) . These pseudo-labeled Q&A pairs are then used to augment the fine-tuning data. | Procedure: fine-tune a VLM on the small target VQA set to get a teacher, use it to auto-generate Q&A on additional unlabeled images, then continue fine-tuning on the augmented set . Showed improved accuracy and robustness on specialized VQA tasks – e.g., better handling of adversarial questions and cross-domain transfer – compared to standard fine-tuning . Notably retained numeric reasoning skills despite narrow fine-tuning . | The quality and diversity of generated questions depend on the teacher VLM; if the teacher has biases or blind spots, it can generate uninformative data. Future work could integrate an LLM to generate more diverse or challenging questions, or apply SelTDA to broader tasks like image captioning with limited text data. |
| PODA ICCV 2023 | Introduce “prompt-driven” zero-shot domain adaptation, removing the need for any target-domain images during training. Motivation: some deployment domains (styles/conditions) have no available training data, but we can describe them in words . | Prompt-driven One-shot Domain Adaptation (PØDA): uses a natural language prompt describing the target domain (e.g. “sketch-style images with black outlines”). A pretrained CLIP model guides an affine feature transformation (Prompt-driven Instance Normalization) that shifts source image features toward the target domain distribution indicated by the prompt . | The model is trained on source-domain labeled data; during adaptation, it optimizes feature normalization parameters so that CLIP’s embedding of those features aligns with CLIP’s embedding of the target-domain prompt . Demonstrated on semantic segmentation (and also tested on detection and classification): using only a text description of the target domain, PODA achieved significant performance gains on target-domain tasks, even outperforming some one-shot (single-image) unsupervised adaptation methods . | The approach assumes the user can provide an accurate textual description of the target domain’s style/appearance. If the prompt is imprecise or the domain has aspects not easily described in words, performance may suffer. Additionally, complex domain shifts (beyond global style, e.g. new object appearances) may require more than an affine feature shift. Future work might allow iterative refinement of prompts or use multiple prompt descriptions for more complex domains. |
| DALL-V ICCV 2023 | Solve source-free video domain adaptation for action recognition by leveraging knowledge outside the source/target data. Motivation: Without source data, prior video adaptation relied only on target self-supervision (temporal consistency), which is limited . Instead, use the “world knowledge” in large pre-trained VLMs to bridge the gap . | Domain Adaptation with Large Language-Vision models (DALL-V): an intuitive, parameter-efficient method that distills a Large VLM’s “web” prior into a student video model . The large VLM (e.g. CLIP) provides pseudo-labels or features for target video frames, capturing high-level concepts robust to domain shift. These serve as soft supervision alongside the frozen source model’s outputs. The student network is trained to integrate both signals. | Training is source-free: only the trained source model and unlabeled target videos are used (source data itself is not accessible). The large VLM is applied to each target frame (or snippet) to produce textual or feature predictions, which guide the student. DALL-V achieved state-of-the-art action recognition accuracy on cross-domain video benchmarks, outperforming previous self-training and consistency-based SFVUDA methods by a notable margin . | Using a large VLM (CLIP) at test-time for every frame can be computationally heavy, though DALL-V minimizes added parameters. Also, CLIP may ignore fine-grained motion details important for actions. The method currently addresses classification; future work could extend it to temporal reasoning or detection in videos and explore using language descriptions of entire video sequences (not just frames) to improve temporal coherence. |
| ULDA CVPR 2024 | Current language-driven zero-shot DA methods require knowing the domain ID or training separate models per domain, hurting scalability . ULDA seeks a single model that adapts to many target domains without explicit domain labels, using language as a unifying modality . | Unified Language-driven Domain Adaptation (ULDA): a framework with three components – (1) Hierarchical Context Alignment (HCA): aligns features with domain-specific text at multiple visual levels; (2) Domain-Consistent Representation Learning (DCRL): enforces semantic correlations across regions; (3) Text-Driven Rectifier (TDR): uses target domain text to rectify feature biases . Instead of separate models per domain, one model handles all, guided by textual domain descriptors. | Uses simulated “target text” (descriptions of each domain’s characteristics) and unlabeled images from each domain during training. Achieved competitive or superior performance to approaches that require domain IDs or domain-specific models . In evaluations across multiple domain shifts (e.g. cartoons, sketches, etc.), ULDA’s single model matched the accuracy of multiple specialized models, proving its generalization ability . Importantly, it adds no extra inference cost, since all adaptation happens in feature space during training. | Assumes that a text description is available for each domain. If domain characteristics are hard to summarize or unknown, the approach might struggle. Also, ULDA was demonstrated on fairly distinct visual domains with provided descriptors; more subtle domain shifts (e.g. different camera sensors) or continuous domain variation might require extending the method (possibly integrating an LLM to generate domain descriptions automatically). |
| PracticalDG (SCI-PD) CVPR 2024 | Address “hybrid” domain generalization, where test data may contain both known (source-like) and unknown domain samples. Aim to transfer the zero-shot robustness of large VLMs to lightweight vision models that can run efficiently . | Perturbation Distillation (PracticalDG): introduces Score-Class-Instance level perturbations (SCI) to distill knowledge from a frozen VLM into a smaller model . By perturbing the VLM’s outputs at multiple levels (logit scores, class tokens, feature instances), the student learns to handle variations beyond source domains. The student’s features are thus encouraged to inherit the VLM’s domain-invariant representations while remaining compact. | Trains on multiple source domains (for known classes) and leverages CLIP’s zero-shot predictions to simulate “unknown” classes or domain variations. Achieved state-of-the-art on open-set domain generalization benchmarks, significantly improving H-score (harmonic mean of accuracy on seen vs unseen domains) compared to prior methods . The student network retains strong zero-shot recognition of novel classes after training, thanks to the VLM guidance. | The distillation is task-specific (focused on classification in the paper) – the method’s effectiveness on other tasks (detection/segmentation) remains to be verified. Moreover, the approach requires careful tuning of perturbation magnitudes at each level; excessive perturbation could degrade relevant features. Future work might automate this or extend perturbation-based distillation to sequential and multimodal tasks. |
| Frozen-VLM Prompt Tuning CVPR 2024 (Tang et al.) | Solve source-free domain adaptation (SFDA) in classification without updating a large model. Leverage a frozen multimodal foundation model (e.g. CLIP) as a stable teacher, and adapt using prompts instead of full finetuning . | Frozen VLM + Unsupervised Prompt Learning: the large VLM is kept fixed. A learnable text prompt is optimized on unlabeled target data to “describe” the target domain in a way that corrects the source model’s biases . This prompt effectively customizes the VLM’s zero-shot classifier to the target domain. Then, knowledge from the customized VLM is distilled into a separate target model (student network) for deployment . | Uses the source-pretrained model’s outputs and the VLM’s prompt-tuned predictions to generate pseudo-labels for target images. Achieved notable gains in SFDA benchmarks (classification tasks), outperforming methods that adapt by updating model weights. The approach is efficient since the heavy VLM is only used during adaptation; the final deployed model is a smaller student. | This method assumes the foundation VLM has strong coverage of the target domain – if the target domain is very distant from VLM’s training data, even prompt tuning might not yield reliable pseudo-labels. Additionally, prompt learning on each new target domain may require careful hyperparameter tuning. Scaling to continuous domain shifts or handling multiple simultaneous target domains would be interesting extensions. |
| VL2V-ADiP CVPR 2024 (Addepalli et al.) | Improve domain generalization in image classification by combining a multimodal teacher and a unimodal student. Leverage rich vision–language features of a teacher VLM while maintaining a simpler vision-only student model for deployment. | Vision-Language to Vision Aligned Distillation: Align the teacher’s vision and text embeddings with the student’s visual features. Concretely, the method projects the teacher’s image features and text features into the student’s feature space, forcing the student to learn representations that are compatible with both modalities . By doing so, the student model internalizes some of the teacher VLM’s multimodal knowledge (via aligned semantic features) without needing a text encoder. | Trained on multiple source domains (for DG) with a large VLM (like CLIP) as teacher. The student (e.g. ResNet or ViT) after distillation showed improved robustness to new domains, outperforming baseline students and earlier distillation methods. On DomainBed benchmarks, this approach improved accuracy on unseen domains by aligning with the VLM’s semantic space . It effectively closed much of the gap between a standard student and a CLIP zero-shot classifier on those tests. | The approach still requires precomputing teacher features and text embeddings for training, which can be memory-intensive for large datasets. The student benefits from multimodal alignment, but ultimately remains a vision-only model; some multimodal-specific reasoning (e.g. understanding textual cues) might not fully transfer. Future work could examine partial text-encoder distillation or extending this idea to detection/segmentation students, which was not covered in the paper. |
| CoLA NeurIPS 2023 | Harness multiple VQA models’ strengths via an LLM “brain.” Observation: different VQA or captioning models have complementary skills, but simple ensembling is suboptimal . Idea: use a large language model to coordinate multiple VLMs by exchanging information in natural language . | Coordinated LLM (CoLA): an LLM acts as a controller that queries several pretrained VLMs. For a given visual question, CoLA prompts VLMs to (1) describe the image, (2) propose candidate answers, etc., in natural language. The LLM then reasons over these outputs (chain-of-thought) and decides the final answer . Two modes: CoLA-FT – the LLM is instruction-tuned on such multi-agent reasoning; CoLA-Zero – uses prompting with no finetuning. | Evaluated on tasks like VQA, knowledge-based VQA (OK-VQA), visual entailment, and spatial reasoning. CoLA-FT achieved new state-of-the-art results on several benchmarks , outperforming any single model, thanks to the LLM’s ability to integrate visual cues and commonsense from multiple experts. Even the zero-shot CoLA (no training) was competitive in few-shot settings . The LLM successfully learned to issue subtasks to vision models and aggregate their responses via natural-language reasoning. | This approach requires multiple models in the loop at inference, which can be slow and resource-heavy. Moreover, the LLM’s coordination is only as good as the prompts and the quality of the VLM outputs – it may sometimes propagate errors from one model to the final answer. An open challenge is making such architectures end-to-end trainable (currently the LLM and VLMs are largely fixed) or distilling the whole pipeline into a single efficient model (addressed partly by VPD, below). |
| CoDA ECCV 2024 (Gong et al.) | Enhance unsupervised domain adaptation for semantic segmentation under multiple severe adverse conditions (e.g. rain, fog, night). Noted that adapting to all conditions at once is hard – models “hallucinate” on the hardest domain (night) if trained on all, but underfit others if trained on one . Solution: an intermediate-domain curriculum inspired by chain-of-thought . | Chain-of-Domain Adaptation (CoDA): instead of one big jump from source to very challenging target, CoDA inserts intermediate domains (e.g. synthetic images with gradually increasing fog density or darkness) and adapts in a stepwise fashion . A Severity-Aware Visual Prompt Tuning (SAVPT) mechanism provides learnable visual prompts that adjust the model for each intermediate severity level . This is analogous to prompting the model with “hints” for easier domains first, then harder ones. | Implemented for weather adaptation (daytime to nighttime segmentation, via dusk as intermediate, etc.). Without using target labels, CoDA achieved better segmentation mIoU in the hardest conditions compared to direct adaptation . The model progressively learned domain-invariant features through the chain of intermediate domains , outperforming prior UDA methods that attempted one-shot adaptation. | CoDA relies on being able to generate or simulate intermediate domain data. In their setup, they used adverse-condition simulators; however, not all domain shifts have obvious simulation (what is the “in-between” of two distinct real domains?). The method also introduced additional tuning complexity (deciding intermediate stages and training schedule). Future work could explore using generative models or diffusion models to automatically create intermediate domains, and extend the idea to classification or detection tasks. |
| LISA CVPR 2025 | Traditional segmentation models require explicit target labels or categories and cannot handle implicit queries (e.g. “segment the largest fruit”) . LISA aims to perform reasoning segmentation – segmenting based on a complex language instruction involving reasoning or world knowledge, in a zero-shot/few-shot way . | Large Language-Instructed Segmentation Assistant (LISA): built on a multimodal LLM that can output both text and pixel masks. Architecture: it extends a vision-language model by adding a special token whose embedding the model learns to output as a segmentation mask . Essentially, the model first uses its language generation ability to reason about the instruction and image (like a dialogue with itself), then produces a mask via the learned embedding-as-mask paradigm . | Trained primarily on ordinary segmentation data (no reasoning) plus a small new dataset of “image-instruction-mask” samples for complex cases (only 1k samples). Demonstrated zero-shot segmentation of novel concepts described implicitly, and with only 239 reasoning-specific examples for fine-tuning, it further improved . LISA can handle queries that require real-world knowledge or relational reasoning (e.g. “the object that can hold water” -> segment a cup) better than baseline segmenters. | LISA shows a new capability, but is limited by the base multimodal model’s visual understanding – it sometimes fails if the query requires very detailed perception or if the reasoning goes beyond its training knowledge. The provided reasoning data is small; scaling up instruction-mask pairs (perhaps via simulation or weak labels) could further improve performance. Moreover, evaluating “reasoning segmentation” lacks established benchmarks (the authors created a small benchmark); more comprehensive evaluations are needed to identify failure modes. |
| Visual Program Distillation (VPD) CVPR 2025 | Complex visual questions (e.g. “Who invented the instrument on the right?”) require decomposing into sub-tasks (recognition, knowledge retrieval, etc.). Prior works used an LLM to generate programs calling multiple specialist models, but this is slow and error-prone . VPD aims to distill multi-step reasoning and tool-use into a single VLM, combining the advantages of programmatic reasoning with the efficiency of one model . | Visual Program Distillation: an instruction-tuning framework that first uses an LLM to generate possible reasoning programs (a sequence of steps, e.g. describe image -> lookup knowledge -> answer). It executes these with pre-trained tools and selects a correct program by verifying the answer . Then, it converts the program and steps into a natural language narrative and fine-tunes a VLM on this “reasoning trace” so that the VLM learns to perform the entire reasoning internally . The VLM (built on PaLI-X, a large vision-text model) thus learns to output the final answer directly in one forward pass, implicitly executing the program. | VPD-trained PaLI-X achieved state-of-the-art on complex reasoning benchmarks like MMBench, OK-VQA and A-OKVQA (knowledge-based VQA), TallyQA (counting), spatial reasoning (POPE), and even multimodal hate speech detection . It significantly improved capabilities like counting and spatial understanding versus the base model . Human evaluators also found VPD’s answers more factual and consistent than those of the baseline. Furthermore, a case study on a content moderation task showed VPD can adapt a model to a new application domain with very limited data by teaching it the “program” (set of steps) needed . | A potential drawback is the reliance on high-quality LLM-generated programs during training – generating and validating these programs for each query can be costly, and errors in this stage could mislead the VLM. Also, the distilled model’s interpretability is limited (it internalizes the reasoning, so we no longer see explicit step-by-step solutions). Future work might explore ways to retain interpretability (e.g. have the VLM output a self-explanation) or extend VPD to domains like robotics (where the “programs” involve actions in the physical world). |
| RL-CoT Agent NeurIPS 2024 | Standard instruction-tuned VLMs can describe and reason about images, but they don’t naturally function as decision-making agents for multi-step tasks (e.g. navigation, interactive question answering). Simply prompting for actions often fails to yield optimal policies . The goal here is to train a VLM to be an agent that plans and acts in an environment, by using reinforcement learning with chain-of-thought . | RL-CoT (Reinforcement Learning with Chain-of-Thought): a framework that wraps a VLM in a decision loop. At each time-step, the VLM is prompted with a task description and asked to generate a chain-of-thought (CoT) outlining its reasoning before proposing an action . The text action is then parsed and executed in an environment, the agent receives a reward, and the VLM is updated via policy gradient (e.g. proximal policy optimization) to reinforce good outcomes . CoT generation helps the model explore intermediate reasoning steps rather than jumping directly to an action . | Evaluated on several multi-step vision-language tasks (the paper reports experiments enabling a 7-billion-parameter VLM to perform competitively in interactive scenarios). The RL-fine-tuned model showed substantially better decision-making, even outperforming strong commercial baselines like GPT-4V on certain tasks . Notably, ablation confirmed that having the model generate chain-of-thought was critical – removing the CoT step led to a significant drop in performance , as the model then struggled to reason through the consequences of its actions. | This approach extends VLMs to agentic behavior but at the cost of requiring an interactive training setup and potentially a large number of trial-and-error episodes (which can be slow or expensive, especially with large models). There’s also a safety concern: letting a model generate its own plans and act (even in simulation) could produce unpredictable behaviors; careful reward design is needed to avoid unintended solutions. Future research may investigate integrating human feedback (RLHF) to further align the agent’s actions with human expectations, and applying RL-CoT to physical robotics or web interaction domains. |
Table: Recent works (2023–2025) on domain adaptation and cross-domain reasoning in VLMs, with their motivations, methods, results, and limitations.
Trends, Innovations and Gaps
Several clear trends emerge from these works:
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Leveraging Pre-trained VLM Knowledge: Many methods use large foundation models (like CLIP or Flamingo) as a source of general knowledge or robust features. This “noisy student/teacher” paradigm (e.g. RISE, DALL-V, PracticalDG) highlights that VLMs’ language-aligned features are surprisingly domain-invariant and useful for transfer . Even without fine-tuning, VLMs provide rich semantics (often via text embeddings) that smaller models or adapters can inherit. This has driven SOTA results in domain adaptation by effectively fusing hand-crafted domain adaptation techniques with learned language priors.
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Language as the Bridge: Almost all approaches place language at the center of domain transfer. Some use natural language descriptions of domains or tasks (PODA’s prompts, ULDA’s text descriptors) to inform the model of the target conditions . Others go further, generating intermediate language representations – e.g. CoLA’s multi-agent dialogue or VPD’s distilled reasoning traces – effectively using language as a common currency between vision and reasoning models. The success of these methods underscores that expressive language representations can guide visual models to focus on high-level concepts and ignore domain-specific noise . A related innovation is the use of chain-of-thought (CoT) style reasoning in vision contexts (CoLA, CoDA, VPD, RL-CoT). This demonstrates that breaking a complex visual task into text-based steps (either explicitly or implicitly) often yields better generalization and problem-solving, mirroring the gains seen in pure NLP.
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Instruction Tuning and Multi-Task Learning: Several works adapt the instruction-following paradigm from NLP to vision. Instead of training on one narrow task, models like LISA and VPD are instruction-tuned on a variety of prompts (questions, commands) with corresponding outputs (segmentation masks, reasoning steps) . This broadens the model’s abilities and improves zero-shot transfer to new tasks. Notably, multimodal instruction tuning is emerging as a way to achieve general-purpose vision-language models that can be adapted with minimal data. For example, VPD’s single model achieved SOTA across diverse benchmarks after being tuned on generated multi-step instructions . The community is increasingly building and using multimodal instruction datasets (as seen with works citing Oogiri, SEED-Bench, etc.) to this end.
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Reinforcement Learning and Interaction: A newer trend is incorporating RL to fine-tune VLMs for sequential decision making (e.g. the NeurIPS 2024 work). This is an innovation because it treats the VLM not just as a passive predictor but as an agent that can plan and act, bringing vision-language models closer to embodied AI. The use of CoT in RL fine-tuning is particularly novel – it combines logical reasoning with trial-and-error learning, and initial results show strong gains . This opens a path to deploying VLMs in interactive or open-world environments (robotics, dialog systems) where domain shifts occur over time and learning from feedback is crucial.
Despite progress, several gaps and challenges remain:
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Fine-Grained vs. Abstract Understanding: While language guidance helps models focus on essential semantics, there is still a gap in fine-grained perception. Some works note that large VLMs like CLIP struggle with granular details (e.g. distinguishing very similar visuals) , and models sometimes hallucinate or overlook small visual differences . Current adaptation techniques address style or high-level category shifts well, but ensuring the model retains sensitivity to subtle visual cues in the new domain is hard. Future research may explore combining language-driven adaptation with techniques that preserve low-level visual fidelity (perhaps via generative modeling or high-resolution feature alignment).
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Data and Annotation Bottlenecks: A recurring assumption is the availability of some form of side information – be it a textual domain description (PODA, ULDA), a few exemplar images (one-shot settings), or an existing captioning model. Truly unsupervised adaptation without any meta-data remains tough. Approaches like SelTDA and CoDA mitigate this by generating synthetic data (questions or intermediate images), but these rely on the source model’s quality. A potential direction is using powerful generative models (image or text) to automatically produce richer descriptors or new training examples for target domains (e.g., generate realistic images in the target style along with pseudo-captions). This could reduce the need for human-provided prompts.
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Unified Models vs. Specialized Pipelines: We see a split between methods that create specialized pipelines (multiple models coordinated by an LLM in CoLA, or separate teacher-student pairs in distillation approaches) and those aiming for a unified model (VPD’s single model, instruction-tuned to do many tasks). The pipeline approaches can be powerful but are complex and hard to deploy; the unified models are elegant but require huge training efforts or may not yet match the modularity of pipelines. An open challenge is how to get the best of both – perhaps modular training followed by model merging or distillation (as VPD attempts) – to achieve models that are both versatile and efficient.
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Evaluation and Benchmarks: As the field progresses, there’s a need for comprehensive benchmarks that test cross-domain and reasoning abilities together. Datasets like MMMU (Massive Multidiscipline Multimodal QA) have started to reveal shortcomings of current models – for instance, GPT-4V (2023) barely achieves ~56% on college-level exam questions across various domains . Likewise, new benchmarks for reasoning segmentation or embodied tasks are in their infancy. Gaps in evaluation mean we might not be fully aware of models’ brittleness. Going forward, we expect more challenging benchmarks (combining visual domain shifts, open-set classes, and reasoning-intensive queries) to drive the next wave of research.
In summary, 2023–2025 has been a period of rapid innovation in domain adaptation for VLMs. Researchers are melding language, vision, and learning paradigms (supervised, self-supervised, and RL) in creative ways. We see a trajectory toward models that can understand an image, explain it, adapt to new styles or contexts, and even solve complex tasks in a new domain – all without extensive re-training. Achieving this consistently remains an open problem, but the approaches reviewed here lay important groundwork. Moving forward, addressing the noted gaps – especially improving fine-grained cross-domain accuracy, reducing reliance on ancillary data, and unifying model capabilities – will be key to pushing the field closer to robust, general-purpose multimodal intelligence.
Sources: The information above is synthesized from numerous recent publications, including conference papers in CVPR, ICCV, ECCV, NeurIPS, and others , as well as survey analyses that contextualize these advances. Each cited work addresses a facet of the broader vision-language domain adaptation challenge, contributing to the trends and insights discussed.