Meta-Learning to Teach Semantic Prompts for Open Domain Generalization in Vision-Language Models
Authors: Shirsha Bose
Mainak Singha
Ankit Jha
Souradeep Mukhopadhyay
Biplab Banerjee
Venue: Accepted by TMLR ()

Abstract: Open Domain Generalization (ODG) addresses the challenges posed by domain and category shifts between labeled training sources and unlabeled target domains. Current state-of-the-art methods struggle with the limitations of traditional CNN backbones, leading to reduced generalization and increased error rates in detecting target open samples without prior knowledge. Additionally, recent CLIP-based prompt learning approaches fail to distinguish between known and unknown classes effectively, resulting in suboptimal performance. To address these challenges, we propose MetaPrompt, which leverages the semantic strengths of the vision-language model CLIP and the ''learning-to-learn'' capabilities of Meta-Learning to achieve robust generalization across domain and category shifts. Our framework introduces three key innovations: First, we approach ODG as a multi-class classification problem that includes both known and novel categories, designing novel prompts capable of detecting unknown class samples across multiple domains. These prompts are trained using Meta-Learning with momentum updates, enabling smooth and accurate differentiation between known and unknown classes. Second, we introduce a novel domain-agnostic semantic attention-based prompt alongside domain-focused prompts to enhance robustness in classifying unknown classes across various domains. Finally, we incorporate an unsupervised contrastive loss during episodic Meta-Training, which reinforces the boundaries in the metric space between known and unknown classes, thereby enhancing ''unknown'' class awareness in the prompts. MetaPrompt has demonstrated its superiority through extensive testing on diverse datasets, excelling in both closed and open-set DG scenarios and consistently outperforming existing solutions.

Published in Transactions on Machine Learning Research (03/2025)
Meta-Learning to Teach Semantic Prompts for Open Do- main Generalization in Vision-Language Models
Shirsha Bose
shirshaboseess@@aalllco
Technical University of Munich
Mainak Singha
mainakkiingha..tttl@maaaaaa
Indian Institute of Technology Bombay
Ankit Jha
ankitjhal
LNM Institute of Information Technology Jaspur
Souradcep Mukhopadhyay
Indian Institute of Science
Biplab Banerjee
meubiplab@gmail.co com
Indian Institute of Technology Bombay
Reviewed on OpenReview: /ttps://openreeet net/ per/fo id NEL
Abstract
Open Domain Generalization (ODG) addresses the challenges posed by domain and category shifts between labeled training sources and unlabeled target domains. Current state-of-the- methods struggle with the limitations of traditional CNN backbones, leading to reduced generalization and increased error rates in detecting target open samples without prior knowledge. Additionally, recent CLIP-based prompt learning approaches fail to distinguish between known and unknown classes effectively, resulting in suboptimal performance. To address these challenges, we propose META PRoMPT, which leverages the semantic strengths of the vision-language model CLIP and the "learning-to-lenn to achieve robust generalization ion across domain and category shifts. Our framework int duces three key innovations: First, we approach ODG a multi-class classification problem that includes both known and novel categories, dosigning novel prompts capable of de- lecting unknown class samples across multiple domains. These prompts trained using Meta-Learning with momentum updates, enabling smooth and accurate differentiation be- tween known and unknown classes. Second, we introduce a novel domain-agnostic semantic attention-basel prompt alongside domain-focused prompts to enhance robustness clas- sifying unknown classes across various domains. Finally, we incorporate an unsupervised contrastive loss during episodic Meta-Training which reinforces the boundaries in the metric space between known and unknown classes, thereby enhancing "unknown" class awareness in the prompts. METAPROMPT has demonstrated its superiority through extensive testing on diverse datasets, excelling in both closed and open-set DG scenarios and consistently outperforming existing solutions.
capabilitics of Mearnnng
1 Introduction
Advancements in deep learning have significantly transformed computer vision tasks such object detection, segmentation, and classification. However, these models often face challenges with domain shifts, where the

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distribution of test data (target domain) differs from the training data (source domain). Techniques such as domain adaptation (DA) and domain generalization (DG) have been developed to address this. Domain adaptation (DA) Li et al.) 2002];; ;; Baktashmotlagh et al. (2013); Singha et al. (2023) focuses on adapting models to now domains using labeled source data and unlabeled or sparsely labeled target data, employing techniques like feature aligument and adversarial training Ganin & Lempitsky (2015a). On the other hand, domain generalization (DG) Li et al. (2019a) 2020 Wang et al. (2021) aims to learn generalized models from multiple source domains to ensure robust performance on unseen target domains without accessing their data during training. Despite these advancements, traditional DA and DG approaches are limited in recognizing novel category samples. These models typically assume that the number of categories remains constant during both training and inference stages. Consequently, they tend to misclassify novel category samples as known training categories during inference, which is impractical for open-world sample recognition. Open domain generalization (ODG) addresses this issue by considering both known category samples and unique samples within its training framework, making it highly relevant for rcal-world applications like autonomous driving and remote sensing. However, research largeting ODG remains relatively scarce. Methodologies such as DAML [Shu et al. (2021) and MEDIC Wang et al. (2023b) integrate meta-learning techniques to enhance classifier reliability for training classes in the context of ODG. Nonetheless, these approaches encounter challenges when dealing with diverse open-world data and limited adaptability. This paper aims to explore and address these challenges, proposing novel strategies to improve the robust ness and adaptability of models in open-world environments.
Vision- Lasionn-annuage models such as CLIP Radford et al. (2021 and ALIGN Jia al. (2021) have emerged robust solutions for various computer vision tasks. CLIP; in particular, leverages vast image and text datasets to create comprehensive embedding spaces through multi-modal contrastive training, excelling in zero-shot and few-shot learning scenarios. Despite its success, CLIP faces challenges in domain generalization due to overfitting and the limitations of traditional prompt tuning, which often fails to enforce cross- -oss-nomain generalization. This shortcoming underscores the need for more robust prompt lcarning strategics adaptable to diverse and unknown domains. In recent research on open domain generalization (ODG), most methods focus on either the one-against-all classifier approach or the threshold-depeendet approach, which determines whether a sample is out of distribution based on specific thresholding of the probability scores. Even the latest CLiP-based prompt engincering methods, such as CoOp Zhou et al. (2022b), CoCoOp Zhou et al.) (002a)), STYLIP Bose el al. (2024), and MaPLe Khattak et al. 2023a), fail to adequately address the ODG problem due to the misalignment of text prompt embeddings with the concept of novel or unknown classes. CLIPN Wang et al. (2023a) introduces a negative concept by introducing 'no' words for text inputs of CLIP that capture the corresponding negation semantics of images for out-of-distributiin (OOD) tasks.
To address these limitations, we propose a novel methodology using meta-learning to make the prompt embeddings aware of the textual term 'unknown', enabling them to detect or segregate open samples from closed counterparts. This approach aims to enhance the robustness and adaptability of vision-language models in handling open-domain environments, contributing to the broader field of domain generalization. Our motivation of using CLIP for ODG stems from the need to address these limitations by leveraging its multi-scale features. We aim to generale semantically aware tokens for prompt, learning. This facilitates better alignment between image and prompt characteristics to enhance the model's ability to handle open and closod-sct classification tasks. To achieve this, we propose METAPROMPT, model trained in a meta- training up inspired by model-agnostic meta-learning (MAML) Finn et al. (2017). Inspired by ODG-CLIP Singha et al. (2024), where a pretrained stable-diffusion model is leveraged to generate open-set samples and perform prompt learning to classify novel classes as 'unknown', we focus on robust prompt tuning and improved visual feature extraction in METAPROMPT. This dual focus ensures balanced performance across known and unknown classes, providing a cohesive solution to the challenges of ODG and setting a new benchmark for generalization in unseen and diverse domains. We state our contributions as follows:
We introduce a novel meta-learning- based prompt learning method, METAPROMPT, for open domain generalization.
We leverage the multi-scale image features of the pre-trained CLIP to generate the BatchNorm statistics. Additionally, propose the semantic attention block (SemAtt) that helps generate more semantically meaningful prompt tokens and improves clustering between open-set and closed-set classes
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Our extensive experiments showcase the efficacy of METAPROMPT for the ODG task six benchmark datasets. We also surpass the state-of-the-art closed-set domain generalization methods by significant margin.
2
Related Works
2.1 Open-Set Recognition (OSR)
The OSR challenge, which aims to effectively identify novel-class samples using only training exemplars from closed-set classes, has become a focal point of research [Beddale & Boult (2016); : Kong & Ramanan Pal ct al. (2023 Vaze et al. (2021)- In this pursuit, generative OSR approaches have emerged These methods augment the training set with artificially synthesized categories, enhancing the model's ability to generalize and recognize novel-class samples during evaluation Du et al.
2221 0:
promising strategies.
(2022) Ge et al. (2017); Zhou et al. (0011)). Moreover, recent advancements in vision-language models, such as CLIP, have presented new opportunities for tackling the OSR challenge. CLIPN, for instance, introduces a novel negalive prompt learning stralegy leveraging CLIP's capabilities Wang et al. (2023a). This approach has demonstrated superior performance compared existing alternatives Fort et al. (2021); Ming et al.) 2022), highlighting the potential of vision-language models in open set recognition tasks.
Open-Set Domain Adaptation (OSDA): Open Set Domain Adaptation (OSDA) operates in trans- ductive cross-domain environment similar to Open Set Recognition (OSR), adopting an OSR-like scenario. Typically, OSDA addresses the presence of out-of-distribution samples by either randomly combining input images or using GAN-based models to generate pseudo-open data Kong & Ramanan 2021 Mancini et al. (2020b). However, these generated images often lie on a lower-dimensional manifold open space and lack rich semantics. Discriminative models, on the other hand, employ metric learning, reconstruction loss, or the confidence of closed-set classifiers to identify open data Chen et al. (2021): Yoshihashi et al.) (2019) CLIPN Wang al. (2023a) introduced novel negative prompt learning strategy for OSR using CLIP, sig- nificantly outperforming other vision-language model-based mothods Fortt et al. (2021); Ming et al. (0022 Nonetheless, these models struggle to handle distribution shifts between lest and training domains.
Open Domain Generalization: Open Domain Generalization (ODG) presents more complex challenges than Open Set Recognition (OSR) and Open Set Domain Adaptation (OSDA) due to its inductive nature and lack of information about the larget domain. ODG in supervised learning requires models to generalize across unseen target distributions without prior exposure to the target domain during training, pushing the boundaries of model generalization capabilities. Early domain generalization (DG) research focused on domain adaptation (DA) models to address domain distribution discrepancies. Domain augmentation reduces these discrepancies by generating synthetic domains and integrating them into the existing domain set, enhancing model generalization. The field has advanced with diverse DG strategies, including self- supervised, ensemble, and meta-learning.
The notion of ODG was first introduced in Shu et al. (2021) extending domainn-ugmentted meta-learning.
MEDIC Wang et al. 2023b) proposes comprehensive approach, including domain and class-wise gradient matching, to establish a balanced decision boundary across closed-set classes ODG-Net Bose el al. (2023)
synthesizes images using disentangled style and content information and trains an open-set classifier. Recent studics by Zhu & Li (2021) and Yang et al.] (2022) have expanded the ODG framework to include single- source domain scenarios. Recently, [Singha et al. (2024) has introduced new language-guider paradigm to solve the ODG problem in the vision-language modeling (VL.)) perspective using an unknown class prompt.. This is the first work in ODG with VLM using CLIP, and our proposed method also counterparts the VLM with a meta-learning method. These developments highlight the ongoing evolution of ODG methodologies as researchers strive to improve models' ability to generalize without prior target domain knowledge.
2.2 Meta Learning
Meta-learning approaches have been effectively addressing the learning of deep learning models under data- scarce scenarios and can be classified into metric-based Vinylls et al. @ et. al. (2017).
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model-based MishrrsMMshra et al. (2018 Graves et al. (0144) and gradient based learning methods Ravi & Larochelle (2016); Nichol et al. (2018). Model-agnostic meta-learning (MAML) Finn et al. (2017) is the gradient-based learning approach in which the model can adapt to new tasks with minimal tuning after being trained on various tasks. In Domain generalization, meta-learning is efficiently utilized to tackle the domain shifts. Li al. (2018) proposes MAML--based domain generalization (MLDG)), where the model is trained on different source domains and tested on unseen target domains. Zhao et al. (2023), utilized meta prompt learning, where the parameters regularised after every update make the model generalizable to unseen domains. Li et al. (2023) GRAM learns prompt initialization and gradient regulating function for cross-domain generalization.
2.3 Vision Language Models and Prompt Learning
Multi-modal vision-language models (VLMs) have significantly advanced image recognition tasks with min- imal task-specific fine-tuning. VL.Ms such as CLIP Radford et al. (2021) and VisualBERT Ti al. (2019b)
use advanced language models like BERT Devlin et al. (0018) and GPT Radford et al. (2018) alongside con- volutional neural networks and vision transformers Vaswani et al.
(011)) for visual analysis. Traditionally,
VLMs relied on manually crafted textual prompts, which can be complex and tiresome. Prompt learning
methods Zhou et al. (202bb a): Bulat & Tzimiropoulos (2023) ; Khattak et al. (2023a) have gained traction
for tailoring prompts effectively for downstream tasks by treating token embeddings as learnable variables constrained by image features.
Recently, CLIP has been employed to tackle challenges in domain adaptation (DA) Singha et al. 2023 and domain generalization (DG) Zhang al. 22021 Bose al. (2024). However, CLIP's text encoder, trained with positive instances, fails to recognize instances with negation. To address this, CLIPN [aan et al. (2023a) introduces "no-text encoder" that assimilates out-of-distribution (OOD) samples. ODG-CLIP Singha et al. (2024) assigns OOD samples from diverse unseen domains to the "unknown" class and applics prompt learning, generating open-set samples using stable-diflusion controlled by positive and negative prompts. In our proposed METAPROMPT, we aim to perform open-set DG with meta-learning, focusing on learning domain-agmostic features for prompt learning.
BNS
Support Sel
Att
Dog
C2
Car
I Sem
CLS
Input
Query Set
Bird
Ceao
I Ienc
T Penc
Non Trainable
CLS
Unknown
Trainable
Bärd, Dog, .. Unknoum
Intermediate Layers
Figure 1: The architecture of our proposed METAPROMPT where Innc and Tene denote the image and text encoders from the frozen pre-trained CLIP, respectively. We extract the BatchNorm features from the BatchNorm Statistics block (BNS). Also, we perform the prompt learning using the context tokens Cм and the semantic token T Sem produced from the semantic attention block (SemAtt, shown Fig 2

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3 Methodology
This section first discusses the mathematical notation and goals of open domain generalization (ODG), After- wards, we explain our proposed methodology and network optimization in Sections 3.2 and 3-3 respectively.
3.1 Preliminaries
ODG aims to develop a model that can generalize to new or unseen target domain using data from M different source domains. Let X' denote the input space and y represent the label space, We denote the samples from the source domains as {DS' J where DS = {(05" .955 ) pxy. Here, I E X S Rd represents an input image, y E y denotes the class associated with x, and pxy denotes the joint distribution in the i-th source domain. Each source domain DS is partitioned into a support set D and a query set DS' que- The support set sup is used for learning the model parameters: D = {(15 doup" " The sup
query set D is used for ovaluating the model's performance: D que = [(xfs S.e )77 The goal is learn a model f X' + y that performs well on a new or unseen target domain D = ={x]] oz) 11 with joint distribution denoted as phy- Also, source and larget domains follow different distributions such pxy # pky. The key challenge in training our METAPROMPT is that pxy is unknown.
3.2 Proposed Methodology
In Figure we present an overview of our proposed METAPROMPT We utilize the frozen pre-trained encoders of CLIP Radlord al. (2021]. Specifically, the image and text encoders are denoted Tone and Temes respectively. Motivated by the success of CLIP in domain generalization ZaaiaattiinZnngggg et al. [202]) : Bose et al.
(2024h and domain adaptation Ge et al. (2022) Singha et al. (2023) tasks, our METAPROMPT framework is designed to train few learnable layers on top of the pre-trained CLIP encoders for the detection of out-of-distribution (OOD) samples under varying target distriibution.
Image and text encoding: For input images x, the pre-trained CLIP's image encoder lene provides the latent image features [enc(a). Additionally, we engineered the pre-trained CLIP image encoder to extract intermediate features from its layers, denoted as (Jenc) and pass through the Semantic Attention block SemAu- We then concatenate both the latent image features and output of Sem through operator & which are subsequently passed through a learnable linear layer, denoted as G, for contrastive training with the text features to achieve domain generalization.
The text encoder of CLIP compriscs transformer layers, as proposed in Vaswani et al. 2017 1 which process the imaage-onddiioned features, as depicted in Figure [ The text features are obtained from the pre- trained text encoder of CLIP by passing M context tokens [C1, C2, CM] along with the semantic token Teem and the class tokens [CLS]. Preciscly, Tene processes the input vector collectively represented (C1,C2,- Au][TTem][CLS) conditioned on the corresponding input image feature (z)e As discussed, we utilize the intermediate layers of the pre-trained image encoder Tene, generating attentive features, which we further leverage for prompt learning, as detailed in the subsequent paragraphs.
Domain Agnostic Semantic Attention Sem To learn more abstract domain agnostic semantic fea- tures, we propose the semantic attention block, denoted as Sem in Figure | which takes the intermediate features of the pre-trained image encoder of CLIP. The detailed understanding of our proposed domain agnostic semantic attention block is shown in Figure 2 which consists of upsampling layers for dimension matching between the features of intermediate layers of pre-trained CLIP image encoder Lene followed by the convolutional layer. After that, we use flatted the obtained convolution maps via global average pooling layer GAP, which finally served at three instances, i.e., final visual latent space, semantic loken Tuam for prompt learning, and discovery loss CRep- We define the semantic token Teem in Equation D where it takes the features obtained from the GAP layer and passes through a single lincar layer Ω. It is essential to note that the multi-scale features from the intermediate layers of the image encoder of pre-trained CLIP Iene played crucial role in dismantling the domain agnostic properties across the seen and unseen classes of the
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target domains.
T Teem 22 At
(1)
Style-guided Prompt Learning: Here, we discuss the generation of prompt tokens conditioned to the input image features. We divide our prompt generation method into two steps: i) incorporating BatchNorm (BN) fealures, i.e., style and content information from the input image x, and fi) adding of semantic token Teem for the final prompt generation. As shown in Figure ! the BatchNorm statistics (BNS) module extracts the (uM (M) corresponding
multi-scale features from intermediate layers of CLIP's image encoder Lenc with M < L. Therealter, we pass these multi-scale BN features to the learnable layers of the meta networks a for generating the M context tokens [C1, C2. CM]]; mathematically defined in Equation 2
Cm = C== =[NBS(((]enee) (t)) / j=1 = 05(1s, o.) - L.M
(2)
where m E M1. These style-guided context tokens CMs are further appended with the attention-based semantic token Teem obtained from the Sem block, defined in Equation and class tokens [CT.S]- On accumulating these tokens, we pass them through the frozen text encoder of pre-trained CLIP Tenes which generates the text encoding for contrastive learning with the image encoding P(x). The generated text encoding learns the prompts concerning the known classes and open-set unknown samples, which we called the new class "unknown".
A Algorithm 1: Pseudocode for METAPROMPT
Upazrpling
Convelation Layer
1: Input: 0, Osup: Oques a, a1 d2
2: Output: Trained Model weights 0
3: while no converge do Sample: DS and D que sets from D
5: for episodes do
lapu
6: predsup = 0(D sup)i
7: A Oeup f 01 x V (predsup: GT
Fini Vosal
Latel Spece
8: predque = ( sup):
9: Oqne f- 02 x Volg Le (preequee GTque);
Figure 2: Detailed on the semantic attention block SemAtt, which generates the semantic token Teem from the intermediate layers pre-trained CLIP's im- age encoder, Tene- and GAP denote the concatena- tion operator and global average pooling, respectively.
end for
Momentum Update: f +- at + (1 a) quei end while 13: return 0
3.3 Meta-learning for ODG
In this section, we discuss the hcuristic approach to tackle the Open Domain Generalization (ODG) problem using à meta-learning setup. We define the optimization functions, i.e., the contrastive loss LCon and the representation loss CRep, which are integral to our proposed METAPROMPT framework.
3.3.1 Episodic Learning
Episodic learning forms the core of the meta-learning process, where the model is trained on multiple tasks or episodes. Each episode is composed of a support set and a query set. The support set provides the model with examples of tasks, while the query set is used evaluate the model's generalization capability. By alternating between these sets during training, the model learns to generalize across various tasks, enhancing its ability to deal with unseen classes in ODG.

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3.3.2 Functioning of Support and Query Sets in Training MetaPrompt
In the METAPROMPT framework, training is based on the concept of episodic learning, where each episode mimics a lask the model may encounter during testing. An episode consists of two key components: a support set and a query set. The support set serves a small labeled dataset representing specific task, allowing the model to learn underlying patterns and relationships between input images and their corresponding labels during each episode. This training helps the model adapt to new lasks by generalizing from the few examples provided, a capability crucial for open-domain generalization (ODG), where handling unseen or unknown classes effectively is essential. Meanwhile, the query set evaluates the model's performance within the same episode by presenting samples not seen during the current training phase. This evaluation assesses the model's ability to generalize from the support set to new, unseen instances an ability critical in ODG scenarios. By alternating between learning from the support and being evaluated on the query set, the model improves its generalization capabilities across different tasks, better equipping it to handle novel unseen data during actual deployment.
3.3.3 Loss Functions
Contrastive Loss (Ccon): Contrastive loss aligns text and image embeddings within shared latent space, which is crucial for tasks involving both modalities. It ensures that embeddings of similar pairs (e.g.. text and image from the same class) are close together, while embeddings of dissimilar pairs are far apart. In METAPROMPT, this loss is applied to both the support and query sets. The goal is to minimize the distance between embeddings of matching pairs and maximize the distance between non-matching pairs. This encourages the model to develop a robust embedding space that effectively generalizes across different tasks and instances.
Ccon
min E P.
logp(vlæ)+ + (..))E((DS..)
E
- log logp(yll))
(..))E((DS,))
where p(y)æ)) defined in Equation with temperature hyper-parameter.
representing the cosine similarity function and
exp (app
p(ula)= Σ (xpl=((enn((LLeec()))) (en()))))) yEy
Representation Loss (Ceep): The representation loss focuses on enhancing the separability between known and unknown classes. It uses unsupervised contrastive approach to ensure that samples from the same class are clustered together while those from different classes are pushed apart in the latent space. This loss is particularly important in ODG settings, where the model must distinguish between known classes (which it has seen during training) and open-set classes (which are entircly new). By enforcing clear boundarics in the latent space, the representation loss helps the model avoid confusion between these classes
Avup Σ li (L.jEAaup)
Aque
Reep
+ Agguel
Σ hi (G.tA
(5)
|Asup
el ({)))))))
where 4.j log N+M
and < denotes the cosine similarity function. Here, 2, Asup
Σ NNM
and Ague represent the embedding space, positive pairs in the support set and positive pairs between the support and query sets, respectively. The representation loss Leps by enforcing similar samples to cluster together while keeping dissimilar samples apart, further refines the model's ability to distinguish ween known and unknown classes.
Total Loss Protat The total loss function used train the learnable layers of our proposed METAPROMPT is combination of the contrastive loss and the representation loss, as defined in Equation D
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Cooaal = Conn + Re
(6)
where À is a hyper-paraneter that controls the trade-off between the contrastive and representation losses. By minimizing Crotal, the model is trained to not only align the text and image embeddings in the latent space but also to effectively separate known and open-set samples, thereby enhancing its generalization capability in open-domain scenarios.
In Section 3.2 we provide detailed explanation of the meta-training procodure, along with the training algorithm of our proposed META PROMPT,, which illustrates how these loss functions are utilized during the training process.
4 Experimental Evaluations
Datasets: We evaluate our proposed MEtAPROMPS with six benchmark datasets for the open-domain
generalization, PACS Li et al. (2017b), VLCS Li et al. (2017c), Office-Home Venkateswara et al. (2017)
Digits-DG Zhou et al. (2020), Multi-Datan Dataset Shu et al. (2021) and Mini-DomainNet |Png et al. (011 Office-Home Venkateswara et al. Clipart, Product, and Real. PACS Li et al. (017aa) contains R 10K images of seven classes spanning four
(2177) have 15,500 images from 65 classes across four domains, i.c., Art.
domains: Artpaint, Cartoon, Sketch, and Photo. VLCS Li et al. (2017c) combines images from PASCAL
VOC 2007 Everingham et al. (2010). Caltoch Fei-Fei al. 22004p. LabelMe Russell et al. (2008), and SUN Xiao et al. (2010), featuring with live classes Digits-DG Zhou al. (2020) consists of handwritten digit
1998). MNIST-M Ganin & Lempitsky (2015b), SVHN Netzer et al. (2011). datasets MNIST LeCun et al.
2015b). Multi-Dataset (hhu et al. (2021) is made up of Office-31, STL-10, Visda2017, and four domains from the DomainNet dataset. [Peng et al. (2019): Includes 600K images across six domains- piaiin painting, clipart, quickdraw, infograph, and sketch divided into 345 classes, with each
and SYN Ganin & Lempitsky
domain containing 48K to 172K images.
Architecture Details: We present the architecture overview of our proposed METAPROMPT in Figure ! The image encoder is based on ResNet-50,, and the text encoder utilizes a transformer model. The prompts are learned through learnable projector, which inputs the BatchNorm features (1), from intermediate layers of the frozen CLIP image encoder. This learnable projector consists of single linear layer followed by ReLU activation layer to generate context lokens, which are then processed by the text encoder to produce the final text embeddings. In addition to the generation of image conditional context tokens [C1. C2. CM]: we incorporate semantic information of the given input image captured through the Sem.Att module, which generates the semantic token Tsem- The SemAll module uses multi-scale features from the CLIP image encoder (ResNet-50), downsamples them to match the spatial dimensions of the last ResNet-50 layer, and concatenates them, as shown in Figure 2 This concatenated output is processed by single convolution layer, ReLU activation layer, and global average pooling (GAP). The GAP layer's output is then passed through a multi-layer perceptron (MLP) to generate the feature embedding corresponding to the input image. This MLP output generates the semantic token T'em through another learnable single-layer MLP for unsupervised reprosentation loss. It creates the final visual latent space by elementwise addition with the Re latent space. Including the semantic token Teem leverages global semantic content information for contrastive learning with the final latent visual embedding.
4.1 Meta-training step
We adopt the MAML-based meta-training strategy Finn et al. (0017) for the overall training of our proposed METAPROMPT model. As described in Section 3.2 the overall paramcters of METAPROMPT are denoted as 0 + a0 + (1 a rue, where 0, and 0g represent the parameters associated with the training models for the support and query sets, respectively. a, represents the varying parameter that determines the shift of towards 8aup or Qqee during the momentum update. During the optimization of META for each episode, we randomly sample six classes with & samples per class, four of which come from the known classes, and two are treated pseudo unknown classes, referred to 'unknown'. For episodes, we
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optimize METAPROMPT following the training steps outlined in Algorithm 1 For experiments we have kept a 0.5.
The parameter update for 0g involves training prompt tokens to make the pre-trained CLIP model aware of the concept of 'unknown', facilitating the detection of outliers or open-set samples during inference. Meanwhile, the parameter update for 0g adjusts the prompt for the seen classes during meta-training. The overall model parameters 0 are updated as a momentum update of 0, and 0g- Consequently, our model parameters 6 converge, enabling the model to perform domain generalization in the presence of outliers effectively. The values of a1, 02 (learning rates defined in Algorithm|] are set as 10-?, and 10 respectively, Additionally, we set the value of X (defined in Equation G to 0.5. The number of context tokens M is set to four. All experiments are conducted with three different seeds, and the results are reported.
4.2 Comparison to the Literature
This section compares our METAPROMPT with the benchmark baselines for both the open-set DG and closed-set DG. In the case of open-set DG, we consider three types of state-of-the-art baseline methods and compare the performance of our proposed METAPROMPT with them. In type-1, we denote the performances of conventional CNN- (ResNet-18 specifically) ODG methods such as Cumix Mancini et al. (2020a) MixStyle Zhou et al. (2021b), DAML Shu et. al. (2021), ODG-Net [ooe et al. (2023), and MEDIC Wang et al. (023bb). In type-II, we evaluate the efficacy of our METAPROMPS against zero-shot CLIP combined
with statc-of-tho-art ODG methods like OpenMax Bendale & Boult 2016) and OSDA Panaroda Busto &
Gall (2017). Finally, in type-III, we compare our METAPROMPT with CLIP- "LIP-based prompt learning methods
such as CoOp Zhou et al. (002b),, CoCoOp Zhou et al. (2022a), , MaPLe Khattak et al. (2023a), LASP Bulat & Tzimiropoulos (2023), STYLIP Bose et al. (2024), CLIPN Wang et al. (2023a), and ODG-CLIP Singha ct al. (2024). To classify the OOD samples, we employ a confidence-driven approach with CoCoOp, MaPLe, PromptSRC, and STYLIP, whereas CLIPN uses a learnable "no" text encoder to identify OOD instances. In contrast, ODG-CLIP considers all novel class samples as single 'unknown' class.
Additionally, we perform experiments in a closed-set setling using the leave-one-out domain protocol, i.e.,
M - domains for training and Mh domain for testing. We compare our METAPROMPT against non- CLIP-based mothods such as SWAD Cha et al.] (2021b), EoA Arpit et al. (2022) DandelionNet Hu et al. 2023), and ODG-Net Bose el al. (2023). We also compare our CIeTAPROMPT with zero-shot CLIP and other prompt learning- based approaches.
Table 1: Comparative analysis for PACS, VLCS, Office-Home, Digits-DG, Multi-Datasct, and Mini- DomainNet in ODG setting on average ACC and HM over all the domain combinations following leave- one out protocol. We report the best results in bold.
Methods
Verme
PACS
VLCS
OffleeHome
DigiteDDC
Multi-Datawe
Mini--omain Domain Net
Averzuge
Acc
HM
Ace
HM
Acc
HM
Asc
HM
Auc
HM
Acc
HM
Acc
HM
Cumin Mansini et al.
ECCV220
07.85
43.05
02:46
50.11
51.67
49.40
58.15
54.20
42.18
46.91
50.27
30.36
02.09
45.81
MiaSto
1CLB'21
63.35
45.00
52.30
53.02
49.52
50.43
53.67
45.66
CVPR
05.49
51.88
53.55
56-15
55:31
53.75
51.29
ODG
TMLR'2
55.81
57.24
59.75
55.83
MEDIC
Wanng ot
1CCV
89.81
53.05
07.25
65.75
60.26
07.01
83.28
50.74
58.13
55.20
66.11
60.30
CLIIP
Rudietl el
95.16
76.77
91.84
72.94
81:45
05:02
77.08
61.95
77.58
$1.50
84.05
02:40
CLIP
Upponaaas Dammaa Boali 2006
93.45
70.15
92.09
73.67
83. 10
61.54
76.93
62.78
75.34
73.25
81.89
60.10
83.05
09.96
C1.PP +. DSDD (Palmeis Inar A TAI 2012
92.02
75.00
90121
20.80
82.55
67:35
80.55
05.70
75.45
75.22
80.00 10.00
75.62
85.75
71.33
CoOp
220y ot his (op4r0
22CV/22
75.77
26.87
02.02
35.25
73.85
36.21
38.54
34.51
0R.63
44.34
61.13
05.34
71.72
41.65
22PR
85.76
82.05
89.47
87.01
75.35
34.38
62:77
33.50
64.84
47.57
06.63
56.30
71.45
43.25
MaPL
CVPR'2
93.97
70.47
25.06
70.54
74.67
79.62
48.68
LASP (omuug
CVPR'S
91.37
14.21
Prompr5100 (101005100
1CCV
91.53
43.32
42.7
80.21
35540
75.34
65.51
T3.00
79.89
45.13
STYLIP ai 5122
WACV24
60.74
90.75
65.66
84.75
60.97
80.59
58.15
70.58
71.30
90.22
85.25
02:77
CLIPN (ragge i. HeAe
JCCV225
96.24
45.00
84.82
50.72
84.05
42:95
81.70
40.56
77.16
62.00
77.85
66.92
83.64
52:27
0DGG-C (oxi-CTii-siggiss.. (inggs ar 1091
CVPR
99.53
99.70
35.71
86.05
98.52
96.05
91.55
75:27
81.60
94.000
95.68
91.15
94.25
90.81
Mito-MoPLe
92.30
84.44
89.42
73.06
86.35
78.61
91.28
71.60
81.70
78.75
70.45
60.51
85.27
76.14
Meto STYLIP
U5.67
55,79
93.25
75.87
01.57
87.35
92.15
72.73
54.23
53.44
96.77
78.08
90.61
81.47
Mid-ODG-CLIP
98.78
90.05
96.75 72
88.44
66.66
98:29
92.94
80.10
86.51
92.53
96. 43
95.58
95.14
92.45
METAPROMPT- + Stable Dillucio
96.15
99.05
55
92.85
07.5 58
98.55 80
05.27
65.69
88.65
94.11
96. 19
98.79
05.06
94.72
MesAPRoserr (wmrs)
89.79
16
80.06
86.653
14.55 73
96.52
94.36
12.60
Open-set DG: In Table 1 we present the performance comparison of our METAPROMPT against state-
th-the- methods for classifying seen and unknown classes in domain generalization. We find that MREAPRoMPT shows significant improvement over non-CT.IP based methods in terms of accuracy and
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Table 2: Closed-set domain generalization (leae--needommiin-ott) performance on the PACS, VLCS, Office- Home, Digits-DG, and Mini-DomainNet datascts. We report the best results in bold.
Methods
Venue
PACS
VLCS
OfficeHome
Digit-DG
MiniDomainNet
Average
SWAD Cha ot al.
2021a
21
88.10
79.10
70.60
79.27
CNN
EoA Arpit et aL 2022
22
88.00
79.10
72.50
80.07
Dandelion Uh et
2023
ICCV'23
89.20
81.60
70.40
80.40
ODG Net Buse el a..
TMLR'23
90.66
79.85
72.92
86.75
50.16
76.07
CLIP Radford et al
2021
94.89
82.14
78.40
64.39
78.73
79.73
CoOp Zhou et al. 2222b
IJCV'22
97.11
63.34
81.33
77.11
72.30
82.23
CoCoOpiZhon Zhou et 27722
CVPR'22
96.54
85.02
81.05
79.36
71.51
82.70
MaPI.e Khafink
21/23n
CVPR'23
97.72
83.52
73.87
84.42
LASP Budat 12imiropouke
2023
ICCV'23
97.02
87.25
84.13
79.92
70.67
83.80
STYLIP [ose ot [/214
WACV'21
98.17
87.21
85.94
81.62
80.43
86.67
PromptSRC Khalink 2023b
ICCV'23
98.02
86.34
83.89
82.40
76.10
85.35
ODG-CLIP Singha 2024
CVPR'21
99.83
95.71
96.91
96.38
96.65
97.10
METAPROMPT (ours)
99.17
95.92
97.35
96.56
97.20
97.24
HM scores al least by 28.25% and 32.30% respectively. Built on top of pre-trained CLIP, we evaluate METAPROMPT against zero-shot CLIP, CLIP + OpenMax, and CLIP + OSDA, noting a rise up in accu- racy and HM scores at least of 9.71% and 21. 21.24%. In comparison to recent prompt learning approaches, i.c., CoOp, CoCoOp, MaPLe, LASP, and PromptSRC and STYLIP, which is not mainly based on ODG solutions, our METAPROMPY putperfrmms them by at least 29.83% in terms of a harmonic score of closed and open-set recognition. The most closely related methods to our METAPROMPT are CLIPN and ODG-CLIP. CLIPN uses trainable "no" text encoder to distinguish outliers from seen samples. However, it does not directly focus on the ODG problem, and ODG-CLIP is the first CLIP-based VLM,, specifically for ODG, solved by using an unknown class prompt. METAPROMPT surpasses the considered baselines with a significant margin of 0.1 1..11% 1% and 2.24% on closed-set accuracy and HM scores, respectively, on average over all datasets. When, we combine our method METAPROMPT with the synthetic data generated by the Stable Diffusion process as used ODG-CLIP, we see an increase in the overall accuracy of the closed-set and the open-set setups. This is due to the diverse generation of open and closed set samples that further helps in meta-learning of the prompts to understand the openness.
Closed-set DG: We report the generalization performance of our METAPROMPE for the closed-set envi- ronment in Table 2 We observe a similar trend in performance when comparing METAPROMPT against CNN-based methods: SWAD by 17.97 %,, EoA by 17.17+ %,, DandelionNet by 16.84+%, and ODG-Net by 21.17+%. In addition, METAPROMPT surpasses zero-shot CLIP, CoOp, CoCoOp, MaPLe, LASP, STYLIP, PromptSRC, and ODG-CLIP by 17.5%, 15%, 14.5% 12.8%, 13.4%, 10.6%, 11.9% and 0.1%, respectively, on the average over the five datasets of DomainBed benchmark. We also find that META outperforms the referred statc-of-the-art methods on the VLCS, OfficeHome, Digit-DG, and Mini-DomainNo datasets at least by 0.18%, 0.44%, 0.18% and 0.55%, respectively. The enhanced performance in METAPROMPT can be attributed to improved visual feature extraction and our novel method of prompt learning, which promotes a more cohesive alignment. between image and prompt characteristics. By simultaneously tackling both open-set and closed-set classification tasks, we enhance the discriminative nature of the embedding space. This dual focus ensures the model performs well in distinguishing known classes (closed-set) while effectively identifying and handling unknown classes (open-set). This results in a well-balanee and cohesive performance across open-set and closod-sct scenarios, synergy often absent in alternative models.
4.3 Ablation Studies
Importance of different learnable components: We ablate our proposed METAPROMPT to evaluate the effectiveness of its components: representation loss Leep: the domain-agnostic semantic attention module Auem) and the semantic token (Teem), as shown in Table 3 Omitting Cee results in an average HM score of 75.16% across five datasets. Including both Red and Sem but excluding Toems improves the score by 2.54%. This highlights the significance of LRep in enhancing the model's ability to distinguish between known and unknown classes.
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Table 3: Ablation of our proposed METAPROMPT with its components on the five benchmark datasets, where w/o denotes the META METAPROMPT PROMPT without the respective component, We report best scores in bold.
Components
PACS
VLCS
OfficeHlome
Digit-DDG
M-Dataset
M-DomainNet
Average
Acc
HM
Acc
HM
Acc
HM
Acc
HM
Acc
HM
Acc
HM
Acc
HM
w/o CRey
93.39
82.35
90.32
72:21
87.68
50.33
91.16
67.49
80.72
73.24
88.46
88.57
88.66
77.40
w/o Teom
95.20
84.49
92
75.35
89.94
83.52
92.35
68.36
84.41
76.78
90.28
90.18
90.74
79.78
w/o Semar
96.43
88.33
92.54
78.97
90.36
86.75
92.43
71.78
92.35
with Tes only
94.56
80.71
90.41
72.03
85.60
$1.34
92.04
72.35
80.31
79.27
89.25
86.25
88.70
78.66
MRTA PROMPT (Full)
98.87
99.89
95.94
89.79
96.83
97.57
93.15
80.06
86.63
91.76
94.75
96.52
94.36
92.60
100.0
1.00
Digits-DG
PACS
Digits DG
97.5
VLCS
Mulb Dataset
VLCS
Multi Dataset
Officeltome
DfficeHome
Harmonic Mean
90-
92.5
Accuracy
90.0
80
87.5
85.0
82.5
MMPLe
Meta-STYLIP
Ours
Meta-MAPLe
Meta-STYLIP
Ours
Figure 3: Performance comparison (Left: Accuracy, and Right: Harmonic Mean) of our proposed
METAPROMPT with the meta-learning setup in the MAPLe Khattak et al. (2023a) and STYLIP Bose et al. h024h.
Next, we include aomain-agnostie semantic information from the Sem block, resulting in 3.5% improve- ment in classifying open samples compared to the previous setting. Finally, training MEtaPRoMP with all componenue results in a significant boost open-domain generalization. We achieve the highest HM scores across all settings, with improvements of 11.6%, 11.1%. 10.8%, 8.3%, and 11.6% on the PACS, VLCS, Of- ficeHome, Digit-DG, and Multi-Datasee benchmarks, respectively. These results suggest that incorporating reprosentation loss, semantic tokens, and domain-agnostic semantic information enhances the classification of in-distribution and out-of-distribution samples, effectively bridging the gap between the two.
Comparison with prompt-learning based methods under meta-training setup: METAPROMPT performs consistently better than MaPLe and STYLIP across the five datasets (PACS, VLCS, Office Home, Digits-DG and Multi-Dataset) as shown in Figure 3 This is possible since the SemAtt block generates the Tsem prompt, which encodes the global sernantic information and, after being mela-trained, can determine the 'known' or 'unknown' samples. This further influences the overall latent space of the image features towards fine-grained open-set detection. Since in other methods, there latent space of the image and the prompt embeddings, thus their performance is worse compared to model. In Table 1 we show complete quantitative analysis of the efficacy of our meta-training setup over the hods MaPLe, STYLIP, and ODG-CLIP, We see that when ODG-CLIP is trained in our meta-training setup, the overall closed and open set accuracy increases compared to the performance of ODG-CLIP without any meta-training. This leads to the conclusion that our meta- vooda-trainng setup useful to make the learnable prompts distinguish between the close and open samples.
schemes associated with the
Ablation with varying number of training shots: Our proposed METAPROMPT is trained with fewer samples using meta-leannn veta-learning-a beased approach. To evaluate the efficacy of METAPROMPT based the number of training samples, we conduct experiments with varying numbers of training samples. In Figure we report the accuracy and harmonic mean for different numbers of shots, i.e., 5, 10, 15, and 20, on the PACS, VLCS, OfficeHome, Digit-DG, and Multi-Dataset. We observe that the generalization performance increases with the number of training shots.
A
Zero-shot openness performance: In Table we show the zero-shot (ZS) performance of our METAPROMPHS for detecting unknown classes into clusters of their type, assuming the availability of label
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Accuracy (PACS)
Accuracy (VLCS)
95
Accuracy (Office Home)
Harmonk Mean (PACS)
Harmonic Mean (VLC.S)
Harmonic Mean (Office Home)
95.0
Acturary Hamoniic Mean
Accuracy Hamonic Mosn
02.5
90.0
85
05.0
80
80
82.5
75-
12.5
17.5
12.5
Shots
Shots
Shots
Accuracy (Multi Dataset]
Harmonic
Accurscy Htrmmnic Meean
Accuracy Harmanic Maan
85
85
80
80
75-
70
(Digits
Hamoniic Mean (Digits DG)
5.0
7.5
10.0
17.5 Shots
12.5
15.0
20.0
5.0
7.5
10.0
12.5 Shots
15.0
17.5
20.0
Figure 4: Ablation of our proposed METAPROME varying the number of shots for the five datasets, i.e., PACS, VLCS, OfficeHome, Digit-DG, and Multi-Datasets.
information for the unknown classes. The results indicate that our method without meta-training performs poorly compared to our method with meta-training, highlighting the efficacy of the meta-learning approach in detecting and segregating unknown classes using learnable prompts. We also report the per-class open set accuracy performance of MEtAPROMPE without the representation loss Leep: observing that unknown class samples fail form well-clustered groups the metric space, resulting poorer performance compared METAPROMPT with both meta-training and representation loss.
Table 4: Ablation of our METAP for the Zero-Shot open-set accuracy of each class over the PACS, VLCS, OfficeHome, Digit-DG, and Multi-Dataset. The model is trained in two configurations: once without the Meta-Trar Heea-Training algorithm and once without the representation loss L denoted as w/o.
Methods
PACS
VL.CS
OfficeHome
Digits-DG
Multi-Dataset
METAPROMPT w/o meta-training
27.66
19.78
22.67
17.75
23.67
METAPROMPT w/o LRep
56.64
43.32
53.76
31.49
46.61
METAPROMPT (Full)
98.24
82.49
93.37
70.16
91.99
t-SNE Visualization for open-sample clustering: In Figure 5 we present the (-SNE visualizations of the metric space for the complete METAPROMPE compared to METAPROMPT without the representation loss Rep- The left panel shows the t-SNE obtained using METAPROMPT without Lnep, while the right panel depicts the t-SNE from the complete METAPROMPE with Leep- These visualizations highlight the efficacy of Lnep in separating unknown classes into distinct clusters, thereby improving the detection of out-of-distribution samples.
Computational Complexity: We provide insights over the computational complexity of our proposed META PRoMPR method. We have demonstrated the efficacy of METAPROMPH in comparison to state-of-
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known
1
Figure 5: Comparing the t-SNE visualization obtained from METAPROMPT under the setting of without (left) and with (right) use of the representation losses defined in Equation
the-art ODG methods. Additionally, provide analysis of the computational overhead inherent in our METAPROMPT approach for the OfficeHome dataset in the Table 33) below. This shows METAPROMPT have the least number of parameters with the lowest GFLOPS compared to the current state-of-art methods. Thus reducing the overall training time and testing time.
Method
Parameters (#M)
GFLOPs
Training time (min)
Testing time (min)
ODG-CLIP
142
253.6
65
1.3
Meta-ODG-CLIIP
156
255.4
70
1.3
Ours
12
47.5
27
0.6
Table Comparison model methods in terms parameters, GFLOPs, training time, and testing time.
5
Conclusion and Future Scopes
This paper presents novel meta-leerring-heee based open-domain generalization method named METAPROMPT, We leverage the pro-trained CLIP model and perform prompt lcarning constrained on the input image to detect out-of-distribution instances. In METAPROMPT we extract features from the intermediate layers of CLIP's image encoder to learn BatchNorm statistics, aiding in the adaptation to unseen target domains by learning domain-agnostic fcatures. Additionally, we obtain semantic attention tokens from these intermediato layers generate semantic-aware context tokens for text embeddings. These tokens are then concatenated with the final image features from CLIP for contrastive training with the text embeddings. Combined with meta-learning, this condt-condiiioned, semantic-aware prompt learning setup excels in generalizing open-set classification for diverse unseen target distributions. Empirically, we report results for both open-set and closed-set DG scenarios with six benchmark datasets. Our proposed PROMPH surpasses all the state- of-thc-art methods by a significant margin. In future work, we aim to extend our approach to challenging tasks such as open-set domain generalization for dense prediction and object detection.
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17


Reviews:
[{"review_id": "PJmyrHJsuw", "reviewer": "Reviewer_sRXY", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "### Strengths\n1. The innovative framing of ODG as a meta-learning problem that generates optimal prompts using visual embeddings for distinguishing known and unknown classes.\n2. Achievement of state-of-the-art (SOTA) performance on both open-set and closed-set DG tasks across six benchmarks, with robust comparison to prior methods.\n3. Clear presentation of methods, figures, and results, making the paper accessible and easy to follow.\n\n### Weaknesses\n\n1. Certain assumptions are presented as axiomatic without adequate justification or references, including:\n   - The claim that BN statistics capture style and content information (Section 3.2).\n   - The assumption that context tokens are inherently domain-agnostic (Section 3.2).\n\n2. Ambiguity regarding the semantic attention module:\n   - Why is it described as domain-agnostic?\n   - The use of \"attention\" is unclear, as no explicit attention mechanism appears in the module design.\n\n3. Missing ablations for critical design components:\n   - Relevance of multi-scale features shown in Figure 2. How does performance change without them?\n   - Contribution of context tokens. What happens if only `Tsem` and the `CLS` token are input to the text encoder?\n\n4. Various clarifications and typos require attention:\n   - The meaning of Ω in Equation 1 and z in Equation 5 is not defined.\n   - Figure 2’s text mentions upsampling for dimension matching, but the figure illustrates only downsampling layers.\n   - Page 11, line 4: Improvements of 11.6%, 11.1%, etc., lack clarity regarding the baseline for comparison.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper introduces MetaPrompt, a novel meta-learning approach for Open Domain Generalization (ODG) using the vision-language model CLIP. It optimizes prompts in the text encoder through meta-learning to enhance generalization across domain and category shifts. The main contributions are:\n\n1. Innovative use of contrastive loss to align text and vision encoders and to cluster vision embeddings of similar classes.\n2. Proposal of proxy text prompts that utilize visual information via semantic attention and batch normalization (BN) statistics, eliminating the need for paired vision-language datasets.\n3. Formulation of ODG as a meta-learning problem to generalize to unseen classes and domains during testing.\n4. Extensive evaluation of the proposed method on both open-set and closed-set domain generalization across six benchmark datasets, accompanied by comparisons with relevant baselines and ablations.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "1. Provide explicit justifications or references for the assumptions in Weakness Point 1.\n2. Conduct ablation studies to substantiate the design choices mentioned in Weakness Point 3.\n3. Address the clarifications in Weakness Point 4.", "broader_impact_concerns": "None", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "GfJRHnkgFR", "reviewer": "Reviewer_j971", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "**Strength**\n- The paper provides a good walkthrough of the components of its proposed method.\n- The ablation studies regarding each of these components are provided.\n\n**Weakness**\n\nI have concerns about the clarity in demonstrating where the improvements over ODG-CLIP truly come from. Specifically:\n\n- The performance gains compared to ODG-CLIP is minimal. For instance, ODG-CLIP reported that their accuracy of 94.23±0.19 for Table 1 setting, while this method (MetaPrompt) achives 94.36, which lacks statistical significance. Similarly, closed set generalization performance is also incremental, only showing 0.14 improvement compared to ODG-CLIP.\n- Not significantly outperforming SOTA method is not necessarily a critical issue if the method has advantages in other aspects such as data efficiency or training efficiency, which I do not see any of them. Maybe the authors could elaborate how efficient this method is compared to ODG-CLIP.\n- I would like to see how the performance changes compared to ODG-CLIP, isolating the two components that author introduces, which are (1) different architecture/loss design and (2) training method (meta-learning). There does not seem to be a clear rationale for why the proposed loss (representation loss) is particularly well-suited for a meta-learning framework, as it could be optimized by using synthetic data for unknown samples as in ODG-CLIP. I would like to know if these two components equally contribute in improving the performance of ODG-CLIP. Specifically, I would like to see the performance of\n\n    - Proposed method (MetaPrompt)'s architecture/loss + learning with synthetic data as in ODG-CLIP.\n    - Proposed method (MetaPrompt)'s training method (meta-learning) + ODG-CLIP's architecture/loss.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The authors present a meta-learning-based approach to address open domain generalization (ODG), building on the strengths of CLIP. This method shares some components with ODG-CLIP [1], such as the use of \"unknown class\" labels and prompt learning. It differentiates with ODG-CLIP in three key points: (1) it employs a different architectural design, (2) introduces a modified loss function (representation loss), and (3) utilizes meta-learning for training, while ODG-CLIP relies on synthetic sample generation through a diffusion model.\n\n[1] Mainak Singha, Ankit Jha, Shirsha Bose, Ashwin Nair, Moloud Abdar, Biplab Banerjee. Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization. CVPR 2024.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "See the weakness above.", "broader_impact_concerns": "I do not have any concerns regarding Broader Impact Concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "cQLVNGPpLw", "reviewer": "Reviewer_NaUB", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper  introduces three key innovations: First, they  approach ODG as a multi-class classification problem\nthat includes both known and novel categories, designing novel prompts capable of detecting unknown class samples across multiple domains. These prompts are trained using Meta-Learning with momentum updates, enabling smooth and accurate differentiation between known and unknown classes. Second, they  introduce a novel domain-agnostic semantic attention-based prompt alongside domain-focused prompts to enhance robustness in classifying unknown classes across various domains. Finally, they  incorporate an unsupervised contrastive loss during episodic Meta-Training, which reinforces the boundaries in the metric space between known and unknown classes, thereby enhancing “unknown” class awareness in the prompts.  MetaPrompt has demonstrated its superiority through extensive testing\non diverse datasets, excelling in both closed and open-set DG scenarios and consistently outperforming existing solutions.\n\nThe author's response resolved my issue. I agree to receive this paper.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper,  the author  introduced  a novel meta-learning-based prompt learning method, MetaPrompt, for open domain\ngeneralization;  They  leveraged the multi-scale image features of the pre-trained CLIP to generate the BatchNorm statistics.\nAdditionally,  they proposed  the semantic attention block (SemAtt) that helps generate more  semantically\nmeaningful prompt tokens and improves clustering between open-set and closed-set classes.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "The author's response resolved my issue. I agree to receive this paper.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents MetaPrompt to improve Open Domain Generalization by combining CLIP’s vision-language strengths with Meta-Learning for better unknown class detection. It introduces novel prompts, a domain-agnostic semantic attention mechanism, and contrastive loss to enhance robustness across domain and category shifts, outperforming existing methods. 

The reviewers highlight the core strengths as 
- the contrastive loss for improving vision-text alignment and cluster similar vision embeddings
- proxy text prompts that utilize semantic attention and BN statistics, eliminating the need for paired vision-language datasets,
- Casting ODG as a meta-learning problem enhances generalization of unseen classes and domains.

The reviewers cited extensive evaluations of six benchmark datasets, including comparisons and ablation studies, which validated its effectiveness. 

In the first round of review, however, reviewers share some concerns regarding
- inadequate justification in Section 3.2 and 
- missing ablations for critical design components
- method exposition issues.

The authors' revision effectively addressed these raised concerns. After the discussions, two of the reviewers are leaning toward acceptance. One reviewer did not respond with a final decision, but based on the comments, the concerns have been resolved. The AE agrees with the reviewers and recommends acceptance.
Decision: Accept (poster)