Exploring Cross-Modal Flows for Few-Shot  Learning
Authors: Ziqi Jiang
Yanghao Wang
Long Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Representation Learning; Generative Model; Few-shot Learning;
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: we propose the first model-agnostic multi-step adjustment approach in few-shot learning by learning a cross-modal velocity field.

Abstract: Aligning features from different modalities is one of the most fundamental challenges for cross-modal tasks. Although pre-trained vision-language models can achieve a general alignment between image and text, they often require parameter-efficient fine-tuning (PEFT) for further adjustment. Today’s PEFT methods (e.g., prompt tuning, LoRA-based, or adapter-based) always selectively fine-tune a subset of parameters, which can slightly adjust either visual or textual features, and avoid overfitting. In this paper, we are the first to highlight that all existing PEFT methods perform one-step adjustment and are insufficient for complex (or difficult) datasets, where features of different modalities are highly entangled. To this end, we propose the first model-agnostic multi-step adjustment approach by learning a cross-modal velocity field: Flow Matching Alignment (FMA). Specifically, to ensure the correspondence between categories during training, we first utilize a fixed coupling strategy. Then, we propose a noise augmentation strategy to alleviate the data scarcity issue. Finally, we design an early-stopping solver, which terminates the transformation process earlier, improving both efficiency and accuracy. Compared with one-step PEFT methods, FMA has the multi-step rectification ability to achieve more precise and robust alignment. Extensive results have shown that FMA can consistently yield significant performance gains across various benchmarks and backbones, especially on difficult datasets.

Published as a conference paper at ICLR 2026
EXPLORING CROSS-MODAL FLOWS FOR FEW-SHOT LEARNING
Ziqi Jiang, Yanghao Wang, and Long Chen Department of CSE, The Hong Kong University of Science and Technology zj angbl connect. .kk ywangt.geconnect. ust. hk, Longchen@ust.
photo Swiss Mountain Dog
Image Encoder
Text Encader
(b) Prompt Tuning
(c) LoRA-bused
shared space
(not aligned)
(a) CLIP
(d) Adapter-based
(c) FMA (Ours)
CL.IP Text features of three classes
Text features after few shot adaption
CLIP Image features of three classes
Image features after few shot adaption
Comparisons of the cross-modal alignment process of different methods. (a) The overview pipeline of CLIP (Radford et al., 2021) for zero-shot cross-modal alignment and classification. Some image features and their corresponding text features not well-aligned. (b-d) The alignment process of three typical types state-of-the-art PEFT approaches, which adjust image text features in one single step. arrow shows the adjustment of corresponding features during the adaptation. For difficult classes, the image features still may be far from the corresponding text features by one step adjustment. (e) FMA achieves multi-step cross modal alignment, which succeeds in aligning text-image features for difficult classes.
Figure 1:
ABSTRACT
Aligning features from different modalities, is one of the most fundamental chal- lenges for cross- mooss-modal tasks. Although pre-trained Eision-language models achieve a general alignment between image and text, they often require parameter- efficient fine-tuning (PEFT) for further adjustment. Today's PEFT methods (e.g-, prompt tuning, LoRA-based, or Shapter-based always selectively fine-tune a sub- set of parameters, which can slightly adjust either visual textual features, and avoid overfitting. In this paper, we are the first to highlight that all existing PEFT methods perform one-step adjustment. It is insufficient for complex (or difficult) datasets, where features of different modalities are highly entangled. To this end, we propose the first model -agnosig multi multi-step adjustment approach by learning a cross-modal velocity field: Flow Matching Alignment (FMA). Specifically, to ensure the correspondence between categories during training, we first utilize a fixed coupling strategy. Then, we propose noise augmentation strategy to alleviate the data scarcity issue. Finally, we design an carly-stopping solver, which termi- nates the transformation process earlier, improving both efficiency and accuracy. Compared with one-step PEFT methods, FMA has the multi-step rectification ability achieve better alignment. Extensive results demonstrate that FMA yield significant performance gains across various benchmarks and backbones, particularly challenging datasets.
LLnng Chen the corresponding author. Codes; http:///ghhaaccm///////SSoononnn

---

Published as a conference paper at ICLR 2026
INTRODUCTION
How to align information from different modalities (e-g., text and images), is very important in almost all cross-modality tasks. Well-aligned cross-modality features play a crucial role in a variety of domains, such as achieving the impressive reasoning abilities MLLMs (Hurst al., 2024; Li et al., 2022; Liu et al., 2023b), and realistic generation qualities in AIGC models (Rombach et al., 2022; Esser al., 2024). Generally, realizing good cross-modal alignment means finding a "golden" multimodal distribution where corresponding text and image features are as close as possible in the shared common space, To achieve this goal, many pretrained vision-language models (VLMs) have been proposed, such as CLIP (Radford et al., 2021) I) and ALIGN (Jia et al., 2021). Taking CLIP for example, as shown in Figure ((a), it achieves cross-modal alignment by training image and text encoders jointly with contrastive objective. By now, VLMs can achieve general alignment between image and text, and show promising results on zero-shot tasks, like image recognition.
However, due to the internal complexity of differ- ent modalities, pre-trained VLMs cannot achieve perfect alignment across all scenarios. Therefore, they typically require further fine-tuning steps to rectify features for better alignment. For instance, in few-shot learning, we usually need to fine-tune VLMs with few images from base categories. Since fine-tuning the whole VLM model is computation- ally expensive, numerous parfmeter-efficient fine- tuning (PEFT) methods have been proposed. These PEFT methods can be broadly categorized into three groups: 1) Prompt Tuning (Li & Liang, 2021): Like CoOp (Zhou et al., 2022b) and CoCoOp (Zhou et al., 2022a), prompt tuning methods try to find better text representations (or features) by replacing handerafted text prompts with learnable continuous prompts. Compared with original VLMs, they can be interpreted as applying a "movement" on all text fea- tures to better align them with the corresponding im- age features (cf.. Figure 1(b)). 2) Adapter-based (Houlsby et al., 2019): Such as CLIP-Adapter (Gao et al., 2024), they typically apply a learnable adapter following the VLMs* image encoder. After training, they will rectify the image features towards corresponding text features for better alignment (ef., Figure (cc).. 3) LoRA-based (Hu et al., 2022): These methods (e.g., CLIP-LoRA (Zanella & Ben Ayed, 2024)) add trainable low-rank matrices in both text and image encoders to store the new knowledge while keeping other parameters frozen. As shown in Figure (dd), they will shift both text and image features to be closer (i.e., towards the golden distribution).
Comparison between CoUp and Linear Probing
Linear Probing
+11.2
CoOp Improvement
80
+4.3
-0.3
+6.8
Performance
60
40
+0.1
20
FGVC
DTD
SUN
Imagetet
Food
Pets
Difficult
Simple
Figure 2: Performance of CoOp and linear probing, We experimented with the 16-shot set- ting and chose CLIP RN50 as the backbone.
Generally, the performance gains of PEFT methods stem from two sources: 1) the incorporation of few-shot training data, and 2) the algorithmic capability to align cross-modal features. To evaluate reference baseline. Since linear probing can only learn
the latter, we utilize linear probing a simple linear boundary, it effectively represents the performance gain attributed solely to data access. Consequently, by measuring the margin by which PEFT outperforms linear probing, we can factor out the data's contribution and evaluate PEFT's cross-modal alignment capability.Howeverr However, we observed that their advantages compared with linear probing are not consistent across different datasets. To better illustrate this, we introduce the concept of dataset difficulty: A dataset with lower CLIP RN5O zero-shot performance is considered more difficult. This definition is reasonable because lower zero-shot performance means that the cross-modal distribution is more complicated, thus more difficult to adjust. In this paper, we found that while PEFT methods work pretty well on relatively simple datasets, they fail to generalize to difficult ones. For example, shown in Figure 2, the prevailing PEFT method CoOp can obtain remarkable improvements over the linear probing baseline on relatively simple datasets, like OxfordPets. However, the improvements more challenging datasets, such as FGVCAircraft, are marginal.
In this paper, we attribute this limitation to the highly entangled cross-modal distributions in difficult datasets, which necessitate complex transformations for effective alignment. Existing PEFT methods struggle to model such transformations, primarily because they rely single forward pass for
2

---

Published as a conference paper at ICLR 2026
feature adjustment during inference (we term these PEFT paradigms one-step approaches). This forces the model to predict the target features directly from the input in one step. which is inherently difficult for very complicated cross-modal distributions. Since one-step approaches are difficult to model complex transformations, a natural question is: Can we make multi-step adaptation to realize better cross-modal alignment?
To address this, we turn to the Flow Matching (FM) the-
Class
Class 2.
ory (Liu et al., 2022; Lipman et al., 2022; Albergo & V'andenEEijdden 2022), framework predominantly used image generation. Generally, FM learns a velocity field that transports samples from a source distribution (typi- cally prior noise) to a target distribution (e.g., real images) through multi-step iterative process. The core motivation for this paradigm is that while directly mapping noise into a real image is challenging(ike GAN, VAE), decom- posing the transformation into multiple intermediate states make the problem more tractable. At each step, the velocity field only needs to predict local update based on the current state, which is significantly simpler than directly learning the one-step mapping. Although typi- cally applied to noise-to-data generation, FM theoretically support transformations between any two arbitrary distri- butions. Therefore, if velocity field can be trained to transform image features to corresponding text features, we can utilize its multi-step ability to align the complicated cross-modal distribution. Specifically, we can formulate all encoded image features as the source distribution. For the target distribution, the simplest method is to encode all prompts with category names as target features. For each velocity training step, image and text feature will be sampled independently to compose a training pair. This ve- locity field will transform any given image feature (source distribution) to a corresponding text feature (target distribution), i.e., classification.
Class 2
Class
Class 3
Classet
(a)
(inefficient)
(inaccurate)
t0.5
Figure 3: Explanation of the two chal- lenges. (a) A flow matching example trans- form image features (red) to text features (blue) distribution. Each distribution consists of features of three categories. amples of how trained velocity transforms image feature into text feature using the vanilla flow matching inference strategy.
(b) Two ex-
Unfortunately, there are two potential issues for this straightforward approach. Firstly, although a well-trained velocity field can achieve transformation between two given distributions, it is not guaranteed to preserve local structure (e.g., category correspondence). For example, as shown in Figure 3()), it may transform image features from one class to text features of another class, resulting in wrong classification. Therefore, the first challenge is figure out how to train a velocity field, which can transfer image features from source distribution, not only to the target distribution (near text features), but also close to their right class embeddings. Secondly, in the inference stage, the goal of flow matching for the generation task is to generate features in the target distribution, which are then decoded to real samples. But for the classification task, what we really need is to transfer image features closer to positive embeddings (i.e., ground-truth class embeddings) than other negative ones. This inconsistency suggests that the previous flow matching inference strategy may not be the best practice for classification scenarios. Thus, the second challenge is that how design an appropriate inference method for classification.
To overcome both challenges, propose novel framework for few-shot learning: Flow Matching Alignment (FMA). Specifically, we have three designs:
Coupling Enforcement: Firstly, for any image feature, we choose the one corresponding to its class label to compose a training pair. Theoretically, this strategy can guarantee that the trained velocity field will act like classifier, moving image features towards the correct direction.
Noise Augmentation: However, compared with randomly pairing, this coupling strategy also reduces the number of training pairings dramatically, making it harder to train the velocity field. To solve this issue, we further add a pre-defined noise to the sampled pair during training. This augmentation makes the training data not constrained in a now-dimensional manifold, making the learning process more stable and robust.
Early Stopping Slover: Besides training, vanilla flow matching inference is not suitable in classi- fication due to the inconsistency (challenge #2). When intermediate features are distinguishable
3

---

Published as a conference paper at ICLR 2026
enough for classification, continuing transfer them obtain text features is unnecessary and inefficient. Even worse, we observed that the following inference steps are likely to move them to inaccurate text features, shown in Figure 3(b). This inaccuracy is because even with the previous training strategies, it is still difficult to train a perfect velocity field. To tackle this, we devise an early stopping solver for few-shot learning. Concretely, instead of arriving at the target distribution. We allow the model to output intermediate features for classification. This early stopping strategy can not only reduce the inference time when the features are good enough for classification, but also reduce the risk of transferring to wrong text features.
Additionally, FMA can two sets of features in the shared space, and it is agnostic to the specific methods used for image or text feature extraction. Thus, FMA can be easily adapted to different methods, ranging from zero-shot CLIP to various PEFT methods. To demonstrate the effectiveness and generality of FMA, we have integrated it with various backbones. Extensive results different backbones and benchmarks have demonstrated consistent performance gains. In summary, our contributions are threefold:
as a plug-and-play module for mult-step rectification. It only requires
We propose a new perspective analyze existing few-shot PEFT approaches: all these methods are one-step methods, and they usually struggle with difficult datasets.
We are the first to explore the potential to adapt the multi-step recitification ability of flow matching for better few-shot learning performance. Based on this, we propose FMA, a novel plug-and-play multi-step rectification framework.
Extensive results demonstrate the superiority and robustness of FMA in the few-shot classification.
RELATED WORK
Few-Shot Learning in VLMs. Few-Shot Learning (FSL) is task to learn from few exam- ples (Wang et al., 2020; Yue et al., 2020). Currently, the most common scenario in FSL is to fine-tune pre-trained VLM (Radford al., 2021; Jia al., 2021) with limited data for classification. Be- cause directly fine-tuning VLMS is computationally expensive, numerous PEFT methods have been proposed. Current techniques for adapting the VLMs can be broadly classified into four paradigms: Prompt Tuning, Adapter-Based and LoRA-Based. The Prompt Tuning approaches (Zhou et al., 2022a;b; Lee et al., 2023; Bulat & Tzimiropoulos, 2023) involves learning textual or visual prompts that are subsequently inserted into the input or middle layers of the pre-trained VLM encoders for a few-shot adjustment. LoRA-Based methods (Zanella & Ben Ayed, 2024; da Costa et al., 2023; Kim et al., 2024; He et al., 2022), instead, insert small number of trainable parameters (e.g.. LoRA ) within the encoders themselves. Adapter-based methods (Zhang et al., 2022: Gao et al., 2024), append trainable layer like MLP to the frozen image text encoders, and they do not require gradient backpropagation across the encoders, which is more computationally friendly.
Diffusion Model (DM) and Flow Matching (FM). Recently, diffussin model has developed into the most powerful framework in generative models (Ho et al., 2020; Song et al., 2020a). It gradually degrades the data by adding noise, then learn the reverse process. Then Score-SDE (Song et al., 2020b) points out that the learning process of DM is actually solving stochastic differential equations (SDE), which can be further interpreted as probabilistic ordinary differential equations (ODE). Consequently, FM (Liu et al., 2022; Lipman et al., 2022; Albergo & Sanden-Fijndedd 2022) extends this by learning a velocity field build transformations between any two distributions. Different from previous one-step generative models, such as GAN (Goodfellow et al., 2014) and VAE (Kingma & Welling, 2013), FM generates data through multi-step rectification. So far, FM has gained great success in variety of domains, such as text-to-image generation (Esser et al., 2024; Geng et al., 2025; Albergo et al., 2023), image editing (Kim et al., 2025; Kulikov et al., 2024; Rout et al., 2024), video generation (Jin et al., 2024; Polyak et al., 2024), and on. However, although FM can achieve transformation between two arbitrary distributions, most approaches tend to set one as the prior noise distribution. Recently, several works (He et al., 2025; Liu et al., 2025) try transform from text distribution to image distribution directly for generation. In this paper, we explore the potential of whether FM is suitable for supervised tasks like few-shot classification.
4

---

Published as a conference paper at ICLR 2026
Training
t
[0.1]
F e
X
Fix Coupling
Noise
Aug
uig
LpM
photo Swiss
Mountain Dog
x
X
X1
Inference
to
En
11-h
X
X1
X2h
Fg
u
Uig
doys
XMh
Classification
Figure 4: Overview Flow Matching Alignment (FMA). The main idea of the training stage is to learn a velocity field, which can transform image features to corresponding text features. Two designs are proposed: coupling enhancement and noise augmentation. During the inference, FMA applies an early-stopping solver that output intermediate features for classification.
METHOD: FLOW MATCHING ALIGNMENT FOR FEW-SHOT LEARNING
Formulation.
We consider the N-way K-shot classification problem: Given a training dataset consisting of N classes, each class with K examples. the goal is to train classification model that can accurately predict the class of any test image from these N classes.
General Framework. The overall pipeline of our proposed FMA is shown in Figure 4. Specifically, FMA consists of three steps: 1) Encoding all text and images into features the shared common space. 2) Training a velocity field to transform image features to text features for better alignment. 3) Using the trained velocity field transform the feature of the given test image for classification.
3.1 FEATURE EXTRACTION
C. where I3, C* represent an image and its corresponding Given the training dataset D (I', Ji=1, class label, the simplest way to encode them into the shared space is using pre-trained VLM Fs, like CLIP. Specifically. CLIP contains an image encoder E, and a text encoder Er. The image (source) distribution is obtained by encoding all images into a feature representation by: where d is the dimension of the feature. Here we omit the superscript for simplicity. For the text (target) distribution, CLIP first transforms all label C' into text descriptions T using their class name and a handcrafted template like "a photo of {class"". Then the text encoder will encode these descriptions into text features by: = (T') with the same dimension the image feature. Notably, our method is agnostic to the feature extraction process. While we use CLIP zero-shot as example, other methods like CoOp can also be used to extract image and text features. In the experiment section, we discuss the influence of different feature extraction methods on FMA.
Frr E IRd
After obtaining all image and text features, FMA trains velocity field to perform further alignment. For simplicity, we denote distributions Po and p1 as the collections of image features and text features.
3.2 TRAINING STAGE
Typically, flow matching training, we randomly sample an image feature xo ~ Po and a text feature P1 to compose training pair. Following Rectified Flow (Liu et al., 2022), transfer trajectory is defined as linear interpolation between 2g and & on time E [0, 1]: 2; = ts + (1 - t) The conditional velocity field u, (2xl2) is defined the derivative of the trajectory with respect to dæs.
time t: 0x(2. 2) di 2o. The marginal velocity field uf (axx) be learned by solving
5

---

Published as a conference paper at ICLR 2026
simple least square regression problem between (2el2) and un
LPM (0) = [ u| (z+) -2- =o)ll²
(I)
where the distribution Pt to denote the collection of all intermediate features Iy. However, the learned velocity field learned uy (zx) cannot guarantee class-level correspondence because is an estimation of oz (24), expectation of the (xx(2)) over all possible text features
ve (x())) = / oy (x)l=)) Po (e(x)l=)p1()) dz.
Pt (x+)
Consequently, the learned ug will drive an image feature towards the average of all text features, which usually results in incorrect classification.
Coupling Enforcement. To solve this issue, propose coupling enforcement. Specifically, for given image feature To. we exclusively sample its corresponding ground-trute text feature že. Due to the sparsity in the high-dimensional manifold, their transfer trajectories can be considered as mutually non-crossing when the dataset is small. This means for given 27, there exists only one potential text feature: 2 in Eq. (2). Consequently, the marginal velocity is equivalent to the conditional one:
Up (z²) = (z)))
(3)
This means we are secretly using the conditional velocity field uy (z)(Ze) to move the image feature to the direction 2, Because 2, corresponds to the right category, 0, (2+)) can then transform the image feature to the correct text feature in the target distribution. For more details, refer to Section 4.
While this coupling enforcement strategy is intuitive, it induces another new challenge: data scarcity. Since we only pair one image feature with its target text feature, the model is trained on only N potential data compared with randomly pairing, where N the number of classes the dataset. As a result, large portions of the velocity field's domain remain unsampled, preventing it from providing reliable instructions to move image features.
1 of the
Noise Augmentation. We design another strategy. noise augmentation, solve this problem. injects a dimendependent Gaussian noise into the intermediate features I during the training process. Adding random noise ensures the distribution ię does not collapse to low-dimensional manifold (Song & Ermon, 2019), thus learning more accurate velocity estimation. Concretely, the noise schedule is chosen time-dependent sequence. Inspired by Schrödinger bridge (Liu et al., 2023a), we sample the new noise-augmented features â, from a Gaussian distribution:
ft N (āzl L (1- ol))))
Here (xx) T. is the standard deviation of 24 E Rdd (x:) = Di u(ax)))/dd where ((xx)= = Σi= 2 is the mean of xt. zi E R is the i-th dimension of xt. After obtaining &e. the new ground truth (ft)) is the direction pointing to the target text feature =.: (+r]zzz)) = 1-t Then Eq. (1) can be applied for training u (x:). The training is summarized in Algorithm 1.
3.3 INFERENCE STAGE
After learning a velocity field wg( (xz), we use it to transform To for classification. Vanilla flow matching inference process adapts an ODE solver, such as the Euler Method, iteratively transform 1 is the integration stepsize.
To into a text feature Ij by M steps: 2++h = + h u (Iz). Here h = M Ji an approximation of sample from P1 and used for classification: Calculate its cosine similarity with the text features of all classes, then select the class with the highest similarity as the prediction.
However, this inference method may lead to an inconsistency issue, as we mentioned before. Specifi- cally, shown in Figure 5(a), with L approaching 1, the distance between intermediate features at and corresponding text features becomes smaller, which means they are indeed moving towards to the target distribution. However, the classification accuracy using 2, first increases, then decreases. This indicates that although the coupling enhancement strategy is adapted, some features may still move towards text features of incorrect classes. To better illustrate this, show an example in Figure 5(b) where this inference method leads to a classification error. As the transformation continues, Zt becomes closer to the incorrect text feature than to the correct one. In this situation, the vanilla inference method, which use ii for classification, will lead to incorrect results.

---

Published as a conference paper at ICLR 2026
Early Stopping Solver (ESS). To solve this problem, We propose a simple yet effective inference strat- egy: utilizing an early stopping solver (ESS) to terminate the transformation when the intermediate features are dis- criminative for classification. Specif- ically, instead of integrating over the full time interval [0, 1], we fix the step- size h as a constant and choose a con- stant number of inference steps M. (3rg) then transforms an initial im- age feature 20 to an intermediate fea- ture p.p at the final time π = h- M, as
1.2
Distance Accurary
77.0
Correct Class
76.5
1.30
Incorrect Class
1.-
Euclidean Distance
76.0
Distance
0.8
75.5
Accuracy
1:25
75.0
0.6
1.20
74.5
0.4
74.0
1.15
0.2
73.5
1.10
0.00
D.75
0.50
Time Step (t)
Time Step (t) (b)
Figure 5: (a) Red line: Average distance to target figure at different timesteps. Black line: Accuracy using features at different timesteps for classification. (b) At different timesteps, the distance between the intermediate features and the correct/incorrect text feature.
shown in Algorithm 2. Subsequently, Li is used for classification. No- tably, the optimal strategy to find a sample-specific t for each input im- age feature, and we left this as a promising direction for future research.
THEORETICAL ANALYSIS
In this section, we provide rigorous derivation of FMA. Given image (source) distribution po and text (target) distribution pt. FMA attempts to find a velocity v. (x4)), which can transform a given image feature 2o E Po to its corresponding text feature through: da, (tt))dt..
Because the flosed-form of (x(2)) is infeasible to calculate, a neural network cannot be used to learn it directly. Therefore, apply the conditional flow matching a text embedding = E pi, first we need to define its conditional probability path (Taa=)).
proxy task. Specifically, consider
However, defining pt (3re [2) )) in FMA is not as simple that in generative flow matching. In generation, given any z, (zoo is simply an identical prior distribution. Nevertheless, in FMA, the different represent different categories, yield different conditional probability paths po(xoo (2). Therefore, how to define (o(zu]a) is the first problem in FMA.
Coupling Enhancement. Actually, the Coupling Enhancement strategy is secretly defining conditional probability path po(zol2). Mathematically, it defines (zol/)) as follow:
K
po(zolz) = Σ 8(x2).
(5)
1=1
Where of means an image feature of the category z, and 8(-) represents the Dirac function. Then (e(z)(2) over time E (0, 1] can be defined as a linear interpolation between 2p and 2:
pe(xalz) 5(tz + - t)=u) = Nttt + (1 2)=o0 0).
(6)
Proposition 1 (Identity). Consider intermediate feature Ir, and z. E p1 is its corresponding text feature. Then the marginal velocity field is identical to the condition velocity field of x;;
Ve (xx)) = ve(xxllz))
Where vx(x) is defined as Eq. (2) and Uy (xal(e))
= dzg
2e - To- The proof is given in Appendix A.
dt
Proposition (Classification). Given any image feature zo and its text embedding ze P1, if conditional velocity field 0, (2,,22,) is defined to generate pr (Z))),), and vx(x,) is defined by Eq. (2). Then have: 21 ze. where Tn is estimated by: dr, (x.)dt..
This means vy (xx) theoretically can guarantee that the transferred feature xn is exactly the correct text embedding 2,, The proof is given in Appendix A.
Noise Augmentation. This strategy can be viewed as an extension to Eq. (6) if we consider a non-zero variance in the conditional probability path p=(2))2), which will naturally yield the result in
7

---

Published as a conference paper at ICLR 2026
Eq. (4). According to Zhu al. (2025), non-zero variance probability path (namely Schrödinger bridge) can augment the training trajectories, thus boosting the performance. However, will also make the training procedure more difficult due to the random noise.
EXPERIMENTS
Datasets and Models. We evaluated FMA on the few-shot classification task. Specifically, We conducted experiments on 11 benchmarks, including Aircraft (Maji et al., 2013), DTD (Cimpoi et al., 2uro).EuroSAT (Helber et al., 2019), SUN397 (Xiao et al., 2010), StanfordCars (Krause et al., 2013), ImageNet (Deng et al., 2009), UCF101 (Soomro et al., 2012), Flowers 102 (Nilsback & Zisserman, 2008), Food101 (Bossard et al., 2014), OxfordPets (Parkhi et al., 2012), Caltech101 (Fei-Fei et al., 2004), sorted from difficult to easy." To better illustrate the effect of dataset difficulty on method, we divided these datasets into two groups: the first five as the difficult set, and the remaining six as the easy set. For each dataset, we followed the standard protocol to split the training, validation, and test sets. For K'shot classification, we randomly sample images from each class as the training set. Unless otherwise specified, the training epoch is set to 200. The architecture of the velocity network is the same as MAR (Li et al., 2024). By default, there are 6 ResNet blocks, and all hidden dimensions are equal to CLIP's feature dimension.
5.1 COMPARISONS WITH -TATE-OF-THE-MA
Settings. We compared our FMA framework with 8 state-of ofte-offthhe-rt methods, including CoOp (Zhou et al., 2022b), CoCoOp (Zhou et al., 2022a), CLIP ELIP-Adapter (Gao et al., 2024), Tip-Adapter (Zhang et al., 2022), PL.OT+ (Chen et al., 2022), KgCoOp (Yao et al., 2023), ProGrad (Zhu et al., 2023) and CLIP-LoRA (Zanella & Ben Ayed, 2024). We implemented our FMA framework on top of CLIP-LORA. Specifically, we first used a pre-trained CLIP-LORA model to extract image and text features. After that, a veloc- ity network was trained on these features ac- cording to FMA framework. Notably, the dataset for training CLIP-LoRA and the ve-
Table 1: Evaluation of FMA's generalization. "D" and "E" mean average performance on difficult and easy datasets. Higher values between baseline and FMA are bolded.
Adaptation
Generalization
Harmonic
Methed
F
D
D
CLIP
48.9
78.8
49.0
48.9
79.6
+PMA
68.9
87.6
49.4
81.0
57.5
84.2
CoOp
71.4
87.1
44.5
75.8
54,8
81.1
+FMA
87.9
44.5
55.6
81.3
CoCoOp
64.1
85.0
49.5
82.1
55.9
83.5
+EMA
68.5
87.3
50.6
58.2
84.7
CLIP-Adapter
62.4
86.6
47.0
79.8
53.6
83.1
+FMA
67.9
87.4
47.7
80.0
56.0
83,5
LoRA
76.1
88.2
45.7
80.5
57.1
84.2
+PMA
77,8
88.8
46.7
80.6
58.4
84,5
locity field are kept the same, which means no extra knowledge is introduced.
The AdamW optimizer was used in FMA training with a learning rate of 0.0002, and we used ViT-B/16 backbone as the default setting. For FMA inference, we set the default stepsize h = 0.1. To find the optimal number of inference steps M, we first attempted various values in the validation dataset, then chose the with the highest performance the final number of inference steps.
Results. We reported the classification accuracy of all methods on 11 datasets in Table 2. For each dataset, we reported the results of 1-shot, 4-shot, and 16-shot classification. The results show that FMA achieves the best performance on most datasets. Notably, compared with simple datasets, FMA shows more significant effectiveness on difficult datasets. This validates our previous conclusion: Multi-step rectification is necessary when the cross- modsl distribution is complicated to align.
5.2 GENERALIZATION ABILITY
Settings. While many PEFT approaches like CoOp can improve the performance of few-shot classifi- cation, they usually result in the degradation of the generalization ability. Therefore, we explored whether FMA will cause this problem by evaluating the cross-dataset transferability. Specifically, we first chose Imagenet to train the velocity field based on five different baselines. Then tested the velocity on difficult and easy datasets, respectively. To conduct more comprehensive evaluation, we
According the zero-shot performane of CLIP ViT-B/16, Table 11 in CLIP (Radford et al., 2021).
8

---

Published as a conference paper at ICLR 2026
Table 2: Comparison with other state-of- approaches. Based on CLIP-LoRA, we add FMA to further improve the performance. The highest value of each dataset is bolded.
Difficult
Easy
Shots Method
Aircraft
SAT
DTD
SUN
Avg
UCF
Net
Flowers
Pets
Caltech
Avg
(202))
24.8
47.8
43.8
62.5
65.5
48.9
66.7
66.7
67.4
89.1
92.9
78.0
(30220) (20226)
20,8
56,4
50.1
67.0
67.5
52.4
71.2
65.7
78.3
84.3
90.2
92.5
80.4
CoCoOp (2022a)
28.1
55.4
52.6
68.7
67.6
54.5
70.4
69.4
73.4
84.9
91.9
94.1
80.7
TIP-Adapter (2022)
28.8
67.8
51.6
67.2
67.1
56.5
73.4
69.4
83.8
85,8
90.6
94,0
82.8
CLIP-Adapter (2024)
25.2
19.3
44.2
65.4
65.7
50.0
66.9
67.9
71.3
86.1
89.0
92.0
78.9
PLOT++ (2022)
28.6
65.4
54.6
66.8
68.8
56.8
74.3
66.5
80.5
86.2
91.9
94.3
82.3
KgCoOp (2023)
26.8
61.9
52.7
68.4
66.7
55.3
72.8
68.9
74.7
86.4
92.1
94.2
81.5
ProGrad (2023)
28.9
57.0
52.8
67.0
68.2
54.8
73.3
67.0
80.9
84,9
91.4
93.5
81.8
CLIP-LoRA (2024)
28.0
71.9
54.1
70.3
69.4
58.7
75.4
70.3
81.4
85.1
91.9
93.8
83.0
+FMA (Ours)
28.3
7300
55.1
70.6
69.8
59.4
75.9
70.2
84.9
85.2
92.1
94.5
83.8
(20226)
30.9
69.7
59.5
69.7
74.4
60.8
77.6
68.8
92.2
84.5
92.5
94,5
85.0
CoCoOp (2022a)
30.6
61.7
55.7
70.4
69.5
57.6
75.3
70.6
81.5
86.3
92.7
94.8
83.5
TIP TIP-Adapter (2022)
35.7
76.8
59.8
70.8
74,1
63.4
78.1
70.7
92.1
86.5
91.9
94.8
85.7
CLIP:Adapter (2024)
27.9
51.2
46.1
68.0
67.5
52.1
70.6
68.6
73.1
86.5
90.8
94.0
80.6
P1,OT=+ (2022)
35,3
83.2
62.4
71.7
76.3
65.8
79.8
70.4
92.9
86.5
92.7
95.1
86.2
KgCoOp (2023)
32.2
71.8
58.7
71.5
69.5
60.7
77.6
69.9
87.0
86.9
92.6
95.0
84.8
ProGrad (2024)
34,1
69.6
59.7
71.7
75.0
62.0
77.9
70.2
91.1
85,4
92.1
94.4
85.2
CLIP-LoRA (2024)
38.8
83.5
64.0
72.8
77.4
67.3
81.1
71.4
92.9
82.6
90.6
95.0
85.6
+FMA (Ours)
40.3
85.0
67.0
73.7
78.9
69.00
82.4
72.0
95.0
83.2
90.8
95.8
86.5-14
CoOp (20226)
43.3
86,0
70.0
74.9
83,1
71.5
83.1
71.4
97.2
84.4
91.1
95.5
87.1
CoCoOp (2022a)
33.8
75.5
65.8
72.8
72.4
64.1
76.0
71.1
87.1
87.4
93.2
95.2
85.0
TIP-Adapter (2022)
44.6
85,9
70.8
76.0
82.3
71.9
83.9
73.4
96.2
86.8
92.6
95.7
88.1
CLIP-Adapter (2024)
34.2
71.4
59.4
74.2
74.0
62.6
80.2
69.8
92.9
87.1
92.3
94.9
86.2
PLOT+ (2022)
46.7
92.0
71.4
76.0
84.6
74.1
85.3
72.6
97.6
87.1
93.6
96.0
88.7
KgCoOp (2023)
36.5
76.2
68.7
73.3
74.8
65.9
81.7
70.4
93.4
87.2
93.2
95.2
86.9
ProGrad (2024)
43.0
83.6
68.8
75.1
82.9
70.7
82.7
72.1
96.6
92.8
95.9
87.7
CLIP-LORA (2024)
$4.7
90.7
73.0
76.
86.2
73.4
97.9
91.6
96.1
88.2
++MA (Ours)
57.8
91.0
75.4
77.8
87.1
99.1
91.6
96.5
88.8.06
Table 3: Model agnostic results. We implemented FMA on several PEFT approaches using 16-shot settings.
Difticutt
Easy
Method
Aircraft
SAT
DID
SUN
Cars
Avg
UCF
Net
Flowers
Food
Caltech
Avg
CLIP (2021)
24.8
47.8
43.8
62.5
65.5
48.9
66.7
66.7
67.4
85.3
89.1
92.9
78.8
+FMA
38.1
84.3
69.5
73.5
78.6
68.9
82.0
70.4
97.5
87.0
92.8
95.9
87.6 +4.
CoOp (2022b)
43.2
86.0
70.0
74.9
83.1
71.4
83.1
71.4
97.2
84.4
91.1
95.5
87.1
+PMA
47.6
88.1
73.1
75.9
85.4
74.0+24
84.4
72.5
98.2
85.0
91.4
95.7
87.9
CoCoOp (2022n)
33.8
75.5
65.8
72.8
72.4
64.1
76.0
71.1
87.1
93.2
95.2
85.0
+FMA
36.9
86.9
71.9
73.4
73.5
68.5
80.3
71.9
94.5
87.8
93.4
95.6
87.3++23
CLIP-Adapter (2024)
33.8
70.4
59.3
74.3
74,2
62.4
80.1
71.6
93.6
87.1
92.4
94.9
86.6
+FMA
35.8
85.6
69.2
74.4
74.7
67.9
81,5
71.3
95.6
87.2
96.0
87.4:08
CLIP-LORA (2022)
54.7
90.7
73.0
76.0
86.0
76.1
86.2
73.4
97.9
84.2
91.6
96.1
88.2
+FMA
57.8
91.0
75.4
77.2
87.7
77.8
87.1
73.5
99.1
85.1
91.6
96.5
88.8.aa
not only reported the generalization (cross-dataser transfer) results, but also provided the adaptation (few-shot learning) performance and its harmonic mean.
Results. The results are shown in Table 1. While FMA improves the few-shot learning performance (first two columns), it doesn't 't result in further degradation of the generalization ability (middle two columns). Meanwhile, the improvement of the harmonic mean indicates FMA achieves a better trade-off between the adaptation and generalization ability. See more details in Appendix D.
5.3 ABLATION STUDY
Architecture Agnostic. In this section We evaluated whether our FMA is effective on other PEFT approaches. Specifically, we implemented FMA on pre-trained CLIP and four different PEFT methods: CoOp. CoCoOp, CdIpeA, and CLIP-LoRA. For each PEFT approach, tuned the pre-trained ViT-B/16 CLIP accordingly. After that, we extracted image and text features using the pre-trained fine-tuned CLIP. Finally, we trained a velocity network these features.
first fine-
Results. As shown in Table 3, FMA achieves consistent performance improvements on all approaches, which demonstrates the architecture-agnosiic ability of our framework. This means FMA can be easily adapted to different PEFT methods without significant performance degradation. Meanwhile, across all approaches, improvements difficult datasets consistently surpass those on easy datasets.

---

Published as a conference paper at ICLR 2026
80
80
-FMA
Accuracy
70
61.7
21.0
Accuracy
65
65.3
60
65
55
55
Vanilla Solver
60
w/o augmentation
ESS
augmentation
50
50
RN-50
RN-101 VIT-8/32 VIT-8/16
1
2
3
4
5
6
9
10
1
16
Backbone
Inference Steps
Shots (c)
(a)
(b)
Figure 6: (a) Performance of different CLIP backbones difficult datasets. (b) Ablation different inference
strategies. (c) Ablation on noise augmentation strategy. average performance on datasets reported.
This further supports our conclusion that for difficult datasets with complex cross-modal distribution, multi-step rectification is more effective.
Different CLIP Backbones. This experiment was to evaluate whether FMA is effective on different CLIP backbones. We implemented FMA top of CoOp and chose four different CLIP backbones: ResNet50, ResNet101, ViT-B/32, and ViT-B/16. Specifically, we first used trained CoOp model to extract image and text features. After that, we trained a velocity network on these features according to our FMA framework. We reported the average performance on difficult datasets.
Results. The accuracy result is shown in Figure 6(a). Across all backbones, FMA achieves better performance than CoOp, demonstrating the effectiveness of our method. This also shows that FMA be easily adapted to different backbones and further boost their performance.
Different Inference Strategies. To evaluate the influence of inference strategies on FMA, we conducted experiments with different numbers of inference steps using two kinds of strategies: Vanilla flow matching solver and our early stopping solver. Specifically, we used the default stepsize h 0.1 and enumerated inference steps M (0, 10]. We conducted the experiments with setting of 16 shots on the DTD dataset.
Results. As shown in Figure 6(b), as the number of inference steps increases, the performance of ESS will decrease after a certain step number (8 in this case). This is because training a perfect velocity field is extremely difficult, so more inference steps mean more accumulated errors. This suggests that choosing proper number of inference steps is crucial for achieving good performance. Additionally, compared with the vanilla solver that not sensitive inference steps, ESS achieves better results when an appropriate inference step is chosen.
Noise Augmentation. To show the influence of noise augmentation in FMA, we trained two velocity networks on top of CLIP-LoRA, one with noise augmentation and another without any augmentation technique. We kept other parts FMA the same, such as the feature extraction and inference. In the inference, we use ESS to transform image features for classification.
Results. Performance on difficult datasets shown in Figure 6(c). We can see that noise augmentation improve the performance of FMA compared with augmentation. And this improvement is consistent across different sizes the training dataset, which proves the effectiveness of this strategy in the velocity field training process.
CONCLUSION
In this paper, we study how to achieve better coos-moodality alignment based on pre-trained VLMs. Although many PEFT methods have been proposed, we find that these methods usually struggle on difficult datasets. In this paper, we conclude that these approaches are "one-step", which are not enough to decouple and align features in difficult datasets. To solve this, we propose our FMA framework to utilize the multi-step rectification ability of flow matching. In the training of FMA, we design two methods, coupling enforcement and noise augmentation, to learn velocity field that can maintain class correspondence. Additionally, we propose an carly-stopping solver for better inference performance. FMA is alug-and play module and we have conducted extensive experiments to demonstrate its effectiveness over a variety benchmarks. We hope our research can explore a new potential opportunity, Le., multi-step rectification in the few-shot learning area.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENT
This work was supported by the National Natural Science Foundation of China Young Scholar Fund Category B (62522216), Hong Kong SAR RGC General Research Fund (16219025). National Natural Science Foundation of China Young Scholar Fund Category C (62402408), and Hong Kong SAR RGC Early Career Scheme (26208924).
REFERENCES
Michael S Albergo and Eric Vanden-Eijnen Eijnden. Building normalizing flows with stochastic interpolants. arXiv preprint arXiv:2209. 15571, 2022.
Michael Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden, Stochastic interpolants: unifying framework for flows and diffusions. arXiv preprint arXivv23303.08777,, 2023.
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-min 1on--nnn discriminative compo- nents with random forests. In European conference computer vision, pp. 446-461 461. Springer,
2014.
Adrian Bulat and Georgios Tzimiropoulos. Lasp: Text-to-text optimization for language-aware soft prompting of vision & language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 23232-23241, 2023.
Guangyi Chen, Weiran Yao, Xiangehen Song, Xinyue Li, Yongming Rao, and Kun Zhang. Plot: Prompt learning with optimal transport for vision-language models. arXiv preprint 2rX:v:2210.01253, 2022.
Mircea Cimpoi, Subhransu Maji, lasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describ- ing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3606-3613, 2014.
Victor G Turrisi da Costa, Nicola Dall' Asen, Yiming Wang. Nicu Sebe, and Elisa Ricci. Diver- sified in-domain synthesis with efficient fine-tuning for few-shot classification. arXiv preprint arXiv:2312.03046 2023.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference computer vision and pattern recognition, pp. leee, 2009.
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning. 2024.
Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference computer vision and pattern recognition workshop, pp. 178-178 IEEE, 2004.
Peng Gao, Shijie Geng, Renrui Zhang, Teli Ma, Rongyao Fang, Yongfeng Zhang, Hongsheng Li, and Yu Qiao. Clip-adapter: Better vision- languuge models with feature adapters. International Journal of Computer Vision, 132(2):581-59) 2024.
Zhengyang Geng, Mingyang Deng, Xingjian Bai, Zico Kolter, and Kaiming He. Mean flows for one-step generative modeling. arXiv preprint 3xXv:22505.13447. 2025.
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley. Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances neural information processing systems, 27, 2014.
Ju He, Qihang Yu. Qihao Liu, and Liang- Chich Chen. Flowtok: Flowing seamlessly across text and image tokens. arXiv preprint arXiv:2503. 2025.

---

Published as a conference paper at ICLR 2026
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip Torr, Song Bai, and Xiaojuan Qi. Is synthetic data from generative models ready for image recognition? arXiv preprint arXiv:2210.07574 2022.
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7):2217-2226, 2019.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pp. 2790- PMLR, 2019.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. Lu Wang, Weizhu Chen, al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Aaron Hurst, Adam Lerer, Adam Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024.
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference machine learning, pp. 4904-4916. PMLR, 2021.
Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, and Zhouchen Lin. Pyramidal flow matching for efficient video generative modeling. arXiv preprint arXiv:2410.0595 2024.
Jae Myung Kim, Jessica Bader, Stephan Alaniz, Cordelia Schmid, and Zeynep Akata. Datadream: Few-shot guided dataset generation. In European Conference on Computer Vision, pp. 252-268. Springer, 2024.
Jeongsol Kim, Yeobin Hong, and Jong Chul Ye. Flowalign: Trajestory-regulariize,, inversion-free flow-based image editing, arXiv preprint arXiv:2505.23145, 2025.
Diederik P Kingma and Max Welling. arXiv:1312.6114, 2013.
Auto-encoding variational bayes. arXiv preprint
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine- fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pp. 554-561, 2013.
Vladimir Kulikov, Matan Kleiner, Inbar Sabberrman-Spiegglggee and Tomer Michaeli. Flowedit: Inversion-free text-based editing using pre-trained flow models. arXiv preprint arXiv:24/2.08629, 2024.
Dongjun Lee, Seokwon Song, Jihee Suh, Joonmyeong Choi, Sanghyeok Lee, and Hyunwoo J Kim. Read-only prompt optimization for vision-language few-shot learning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1401-1411, 2023.
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre- training for unified vision- laiguae understanding and generation. In International conference on machine learning, pp.
PMLR, 2022.
Tianhong Li. Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector guantization. Advances in Neural Information Processing Systems, 37: 06424-564455 2024.
Xiang Lisa Li and Perey Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2 101.00/90. 2021.
12

---

Published as a conference paper at ICLR 2026
Yaron Lipman, Ricky TQ Chen, Heli Ben- Hem-Hamu,, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2110.02777, 2022.
Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A Theodorou, Weili Nie, and Anima
Anandkumar. image-to-image schrödinger bridge. arXiv preprint arXiv:2302.05882, 2023a.
Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, Advances in neural information processing systems, 36:34892-34916, 2023b.
Qihao Liu, Xi Yin, Alan Yuille, Andrew Brown, and Mannat Singh. Flowing from words to pixels: A noise-free framework for cross-modality evolution. In Proceedings of the Computer Vision and Pultern Recognition Conference, pp. 2755-2765, 2025.
Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003 2022.
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-gree Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.330 2013.
Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian conference on computer vision, graphics & image processing, pp. 722-729. IEFE, 2008.
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. 2012 IEEE conference on computer vision and pattern recognition, pp. 3498-3505. IEEE, 2012.
Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Cho Ma, Ching- Yao Chuang, et al. Movie gen: A cast of media foundation models. arXiv preprint arXiv:2410.13720, 2024.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp. 8748-8763. PmLR, 2021.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Litu Rout, Yujia Chen, Nataniel Ruiz, Constantine Caramanis, Sanjay Shakkottai, and Wen-Sheng Chu. Semantic image inversion and editing using rectified stochastic differential equations. arXiv preprint arXiv:2410.1077 2024.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02500 2020a.
Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32, 2019.
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:201 13456, 2020b.
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint 1212.022, 2012.
Yaqing Wang, Quanming Yao. James T Kwok, and Lionel M Ni. Generalizing from few examples: A survey on few-shot learning. ACM computing surveys (csur), 53(3):1-34, 2020.
Jianxiong Xiao. James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE computer society conference on computer vision and patlern recognition, pp. 3485-3492. IEFE, 2010.
13

---

Published as a conference paper at ICLR 2026
Hantao Yao, Rui Zhang, and Changsheng Xu. Visual-language prompt tuning with knowledge-guine guided context optimization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6757-6767, 2023.
Zhongqi Yue, Hanwang Zhang, Qianru Sun, and Xian-Sheng Hua. Interventional few-shot learning. Advances in neural information processing systems, 33:2734-2746, 2020.
Maxime Zanella and Ismail Ben Ayed. Low-rank few-shot adaptation of vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp- 1593-1603, 2024.
Renrui Zhang, Wei Zhang, Rongyao Fang. Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li. Tip-adapter: Training-free adaptation of clip for few-shot classification. In European conference computer vision, pp. 493-510 510- Springer, 2022.
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16816-16825, 2022a.
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision- language models. International Journal of Computer Vision, 130(9):2337-2244 2022b.
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang. Prompt-aligned gradient for prompt tuning. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 15659-15669, 2023.
Kaizhen Zhu, Mokai Pan, Zhechuan Yu, Jingya Wang, Jingyi Yu, and Ye Shi. Diffusion bridge or flow matching? a unifying framework and comparative analysis. arXiv preprint 2rX55, 2025.
14

---

Published as a conference paper at ICLR 2026
A THEORETICAL GUARANTEE OF FMA
Proposition (Identity). Consider an intermediate feature 2s, where t E [0, 1]. and že E is its corresponding text feature. Then the marginal velocity field is identical to the condition velocity field of zz:
vx(xe) Ut (zelze))
Proof. As we have discussed in the Coupling Enhancement strategy, the conditional probability pz(z)(2) only assigns probability mass features with the same label. The implicts that, if xe and &e belong to the same category, then for all x' $ Ze. we have pr. (Fx) =²) = O. Then we have:
(x(2)) f [m(ma)z)) pa(aal=)pp(()) dz Pe (e(e)e) Pe (z1)
(e(xelz) (((x)1z)p1(2) dz
(2r(2e)
pe(ere)
2
Pa(
0. p1(2) dz
ey (
ve(xel2)
(o(xx)
(r(
Pr (ael2e)) (zc)
u. (ze)
(r()
pe (r (1) (Ze)
(. (ze)
pc (x ())p(z)d
po(Felze)p((2)
ur (x (ze)
Pc (e + Sayt, (
pe(xel=e)P1 (1)())
v. (x ke)
Pe (0 + +2 0 Pr(2)dz
Ut (Zx kz)
(ate 2e p1)
0z (Zie (z.)
Proposition (Classificationn) Given any text embedding 26 E Pt, if conditional velocity field (e(2)(2e) is defined generate Pe(zelze), namely:
dæ ve(xelzo), Dx ~ pe te (0.1]
To ~ Po
(8)
dt
Then the marginal velocity ve (xxe), defined by:
(x)))) = (2( (r (2) dz
(9)
Pe (xx)
can guarantee:
31 Pc
(10)
where In is estimated by: dx, = v2 (xt
Proof.
Consider the two transformation ODE using the marginal/conditional velocity:
dæ = vz(relz)),
Fo ~ Po.
= xx ~ ~((pkk))))))), te (0,
(11)
dt
dz
2o Pos
vx(2e)
+ D > ~ pe(ee), t (0,
(12)
dt
Given the same intial image feature øp, because 0, (z,) = vy (z, 2.). these two ODEs are essentially the same one. By the BeaardLLindelöt theorem (assuming we (3rx) and un (=relzz)) is Lipschitz continuous,
15

---

Published as a conference paper at ICLR 2026
which is standard in flow modeling), the solution an ODE with given initial condition is unique. Therefore, 21 obatained by (x())) also have:
21 p1 ze) .V + 0 zo. 0) 2c
By now, we have proved that the marginal velocity 2/ (Ir) can not only convert an image feature zo into a text feature 2 pı, but also guarantee that this text feature is exactly of the same class as Zo. that 2 Ze-
After that, we need to figure out how to learn (z using neural network of (ax). Intuitively, we can optimize by solving least square regression problem between vs (z4) and uy (x+):
CFM (0) = ta - (x+)))))
(13)
Because (x+) = (2)g)2). and pi (zt (2) (lt () zu. 0). we have 22 t (1 - then :
To-
dæ
d(t (1 t) Fo)
(x(2)) 0e (z122)
220
dt
Therefore, the training objective becomes:
CFM (0) E p.. - ((2-xo)ll
(14)
B
ALGORITHM
Below is the training and inference algorithm of our FMA, respectively.
Algorithm Training
Algorithm Inference
1: Input: paired image features and text features
Input: image features xo, trained uf (xx))
D (1)) =2) N
steps M, stepsize h.
2: repeat
3:
É t~ u([0, 1)), (ID, 2c) D
3: for n. 1 to M do
4:
It (1 1) + lic
Tt+h Ft + l (x:))
5:
N (1- (2(1)))
5:
I 1-1+
6:
Take gradient descent on (fte
end for
7:
until converges
return Th-as as new image feature
C MODEL AGNOSTIC EXPERIMENT
This section provides more detail of Table 3: standard deviation(Table 4), statistical signifi- cance(Tabll 5), and corresponding inference steps(Table 6).
Table 4: Details on standard deviations on Table The bottom right gray number next to each FMA's result is the corresponding standard deviation.
Diffienlt
Easy Food
Method
Cars
Avg
UCF
Flowers
Avg
78.6.1
97.5
87.0em2
92.8u94
95.9
CoOp
43.2
70.0
74.9
83.1
71.4
91.1
95.5
87.1
+PMA
47.6
88.1
73.1
75.9
85,4
74.0044
84.4ss3
72.5an2
98.2sse
85.0smi
91.4ua3
95.7am
87.9ss1
CoCaOp
338
75.5
65.8
72.8
72.4
64.1
76.0
71.1
87.1
87.4
93.2
95.2
85.0
JFMA
369106
86.911
71.9.0
73.4:08
73.5.44
68.5100
80.3
71.9..
94.5.43
87.8
93.4
95. 23.611n
87.3
Adapter
33.8
70.4
59.3
74.3
74.2
62.4
80.1
71.6
93.6
87.1
92.4
91.9
86.6
+FMA
35.8
85.6s11
69.2es1
74.4am1
74.7-a1
67.94m7
81.5as3
71.3
95.6us4
87.2402
92.9uap
96.0amo
87.4es3
CLIP-LARA
54.7
90.7
73.0
76.0
86.0
76.1
86.2
73.4
97.9
84.2
91.6
96.1
88.2
75.4gss
77.2,03
87.7.03
77.8103
87.1
73.5
99.1
85.1102
91.6ua3
96.5
16

---

Published as a conference paper at ICLR 2026
Table 5: FMA.
Details on p-value on Table 3. The baseline indicates which method we implement for
Difficult
Easy
Baseline
Aircraft
SAT
DTD
SUN
Cars
Avg
UCF
Net
Flowers
Food
Pets
Caltech
Avg.
CLIP
0.000
CoOp
0.008
0.006
0,015
0.142
0.021
0.004
0.032
0.185
0.009
0.254
0.347
0.289
0.035
CoCoOp
0.048
0.000
0.002
0.375
0.210
0.001
0.001
0.245
0.000
0.322
0.415
0.076
0.008
CLIP-Adapter
0.015
0.000
0.835
0.475
0.000
0.028
0.003
0.802
0.124
0.004
0.045
CLIP-LoRA
0.008
0.374
0.024
0.046
0.018
0.012
0.034
0.876
0.001
0.095
0.871
0.089
0.041
Table 6: Details on inference steps (M) for different baselines corresponding to the results in
Table 3. The average steps are rounded to one decimal place.
Baseline
FGVC
EuroSAT
DTD
SUN
Cars
UCF
Net
Flow
Food
Pets
Cal
Avg
6
3
5
3.7
CoOp
2
2
3
4
1
1
1
1.7
CoCoOp
2
6
6
4
9
2
3
2
3
4.3
CLIP-Adapter
7
5
3
2
5
1
2
3.1
CLIP-LoRA
5
3
3
6
2
1
2.8
D GENERALIZATION EXPERIMENT
This section provides the detail of Table 1. For each dataset, we report its generalization performance on five baselines and FMA in Table 7.
Table Cross-dataset transfer evaluation. We train models on ImageNet (Source) and evaluate their generalization ability on 10 other unseen datasets (Target).
Source
Target Datasets
Method
ImageNet
FGVC
EuroSAT
SUN
Cars
UCF
Flow
Food
Pets
CoOp
71.4
16.2
49.3
35.7
58.0
63.2
60.9
59.7
80.8
86.9
91.1
+FMA
72.5
16.1
49.8
36.5
57.6
62.4
61.6
58.7
80.2
86.5
91.5
Adapter
71.6
23.2
44.0
42.6
63.6
61.9
64.4
68.9
84.2
86.8
94.9
+FMA
71.3
23.0
45.1
45.4
63.9
61.1
64.2
68.7
83.3
88.2
95.9
CL.IP
66.7
24.8
48.3
44.1
62.6
65.5
67.5
70.7
85.9
89.0
93.3
+FMA
70.4
24.0
49.1
46.6
63.1
64.2
66.8
68.9
85.2
88.5
95.7
LoRA
73.4
22.5
32.5
44.2
66.5
63.2
66.0
70.0
84.5
89.1
92.9
+FMA
73.5
22.6
37.4
44.4
66.3
63.0
67.3
69.3
83.6
89.1
93.6
CoCoOp
71.1
23.7
45.7
46.0
66.5
65.8
69.4
70.2
86.0
90.3
94.8
+FMA
71.9
24.3
49.5
47.3
66.9
65.0
70.0
85.2
90.5
95.5
17


Reviews:
[{"review_id": "kPVQuMvdXg", "reviewer": "Reviewer_2Lin", "summary": "This paper proposes a method to improve alignment performance between modalities in cross-modal models.\nIt claims that existing methods fail to align well on challenging datasets because they attempt one-step alignment, \nand proposes a multi-step approach to align the embedding vectors of the two modalities.\nSpecifically, it performs flow matching to transform from images to the distribution of text embedding vectors.", "questions": "- What is the definition of one-step adjustment? Does it mean that the objective function is set once or that the optimization is only one step? Are Fig. 1(b)-(d) optimal embeddings in some sense?\n\n- In Fig. 2, isn't it a bit simplistic to conclude that PEFT is weak on challenging datasets based on LP?\nCouldn't one also conclude that LP is strong on more challenging datasets?\n\n- What happens if stronger methods are used as baselines in Table 2? Also, did you check the standard deviation of the results and their statistical significance?\n\n- How about the comparison of computational cost?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- This paper is well-written and well-structured.\n- Alignment of embedding vectors across multi modalities is an important research topic.\n- Flow-matching alignment seems novel. However, its necessity is questionable, and it might be just a combination of new techniques.\n- In experiments, the proposed method outperforms baselines on class classification tasks. However, as written in Weaknesses, it is unclear whether the evaluation is well-designed to confirm the claims.", "weaknesses": "- The motivation for multi-step adjustment is unclear. \nFirst, the definition of one-step adjustmentfor poor performance in existing methods is ambiguous.\nFor example, is the claim that PEFT's optimization objective function is inappropriate, or that optimization is insufficient due to difficult learning?\nFigure 2 discusses PEFT's characteristics compared to LP, but the validity of using LP as a baseline for this discussion is unclear. \nIt is unclear how this connects to the statement: “these methods try to adjust their general aligned multi-modal distribution towards the golden distribution by one rectification step.”\n\n- The experimental setups are insufficiently described, resulting in a lack of reproducibility. \nFor example, it states that velocity networks are learned, but I could not find a description of the specific structure of the velocity networks.\nThere is no definition of $\\sigma^2(\\cdot)$. There seems also no report of the number of steps M for the proposed method across each dataset.\nIn addition, there is no evaluation of statistical significance.\n\n- The baseline varies depending on the evaluation. \nWhile Table 1 compares against 8 baselines, Table 2 has one baseline and Table 3 has five.\nSpecifically, the baseline compared in Table 2 is one of the weaker baselines among those appearing in Table 1.\nAlthough there are practical limitations on the number of experiments, comparing against the strongest baseline yields more convincing results.\n\n- The proposed method seems computationally expensive. \nIt requires preparing velocity networks and performing multiple updates during inference (Algorithm 2).\nHow does the computational cost compare to the CLIP-Adapter with two linear layers? How does it compare to PEFT?\nSince the performance improvement over CLIP-LoRA is only 0–2%, the heavy inference cost makes the proposed method less useful.\n\n- Minor issues\n- The space is filled, making it difficult to read.\n The absence of a single line of space before and after figures and tables, such as the caption for Figure 4, violates the template.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "qD1wcMXWZF", "reviewer": "Reviewer_BSTW", "summary": "This paper addresses the challenge of achieving precise cross-modal alignment in vision-language models (VLMs) for few-shot learning. The authors argue that existing parameter-efficient fine-tuning (PEFT) methods—such as prompt tuning, adapter-based, and LoRA-based approaches—perform only a \"one-step\" adjustment of features, which is insufficient for complex datasets where modalities are highly entangled. To overcome this limitation, the authors propose Flow Matching Alignment (FMA), a model-agnostic framework that leverages flow matching theory to enable multi-step feature transformation. FMA incorporates three key designs: coupling enforcement to preserve class correspondence, noise augmentation to mitigate data scarcity, and an early-stopping solver for efficient and accurate inference. Extensive experiments on 11 benchmarks show that FMA consistently improves performance, especially on challenging datasets, and integrates seamlessly with various backbones and PEFT methods.", "questions": "1. Could the author provide more detailed information across different datasets in section 4.2 GENERALIZATION ABILITY, where only average performance was given?\n2. Was any exploration done into adaptive early-stopping criteria (e.g., based on feature discriminability) rather than a fixed stepsize?\n3. How does FMA perform in cross-modal retrieval or other downstream tasks beyond classification, given its alignment-focused design?\n4. Could the authors provide more intuition or theoretical insight into why coupling enforcement preserves class-level correspondence in high-dimensional feature spaces?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Novel application of flow matching to cross-modal alignment in few-shot learning, moving beyond generative tasks.\n2. Effective design choices (e.g., early-stopping solver, noise augmentation) that address practical challenges in training and inference.", "weaknesses": "1. No analysis of computational overhead or inference latency introduced by multi-step transformation.\n2. Ablation studies do not explore the sensitivity of performance to hyperparameters like inference steps.\n3. The early-stopping strategy uses a fixed step count rather than a sample-adaptive criterion, which may limit optimality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "owGXCbJ5Ok", "reviewer": "Reviewer_CrjH", "summary": "The paper reframes PEFT as a one-step adjustment problem that fails on difficult datasets and proposes Flow Matching Alignment (FMA), which learns a velocity field to iteratively transport image features toward their ground-truth text features. It introduces coupling enforcement to preserve class correspondence, noise augmentation to combat data sparsity and manifold collapse, and an early-stopping solver (ESS) that classifies from intermediate states to avoid late-stage drift. FMA is plug-and-play across CLIP and multiple PEFT backbones and shows consistent gains on 11 benchmarks, especially on difficult datasets, with ablations supporting each component.", "questions": "1. Can ESS be made adaptive without validation tuning (e.g., stop on sufficient logit margin, small velocity norm, or diminishing logit gains)?\n2. How is σ(xt) chosen in practice, and how sensitive are results to the noise magnitude/schedule? Any benefit from uncertainty- or density-adaptive noise?\n3. Did you try higher-order or adaptive ODE solvers (Heun/RK) to reduce truncation error and improve margins at the same step budget?\n4. Is fixed pairing too rigid for multi-modal classes? Would transporting toward multiple positive prototypes or a class subspace help?\n5. Have you considered adding a discriminative loss on intermediate states (e.g., contrastive/margin) to align transport with classification throughout the trajectory?\n6. Can you report detailed overhead (velocity net size, train/infer time, average ESS steps) and scaling with number of classes?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The diagnosis of one-step PEFT limitations is convincing; the method is simple, modular, and effective across backbones; experiments are comprehensive; and the early-stopping insight is well-supported by empirical phenomena.", "weaknesses": "Lack of formal guarantees for coupling assumptions, reliance on validation to set inference steps, missing comparisons with higher-order ODE solvers, coarse difficulty metric, and limited analysis of compute trade-offs and failure modes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}, {"review_id": "nfst7QIEoX", "reviewer": "Reviewer_girA", "summary": "This paper introduces a new framework called Flow Matching Alignment (FMA) to improve feature alignment between visual and textual modalities in few-shot learning. The authors observe that current parameter-efficient fine-tuning (PEFT) methods—such as prompt tuning, adapter tuning, and LoRA—perform only one-step feature adjustments, which are insufficient for complex datasets where image–text features are highly entangled. FMA leverages the multi-step rectification ability of flow matching by learning a cross-modal velocity field that iteratively transforms image features toward corresponding text embeddings, achieving finer alignment. To ensure stability and correctness, the method incorporates three key designs: coupling enforcement (to maintain class correspondence), noise augmentation (to mitigate data scarcity), and an early-stopping solver (to prevent over-transformation during inference). Experiments across 11 benchmarks and multiple backbones show that FMA consistently outperforms existing PEFT methods.", "questions": "1. As discussed in the weakness part, is there any theoretical guarantee of convergence and stability of FMA? Given that flow matching originates from generative modeling, what are the theoretical conditions under which FMA ensures convergence to the correct class-aligned distribution in supervised learning settings?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. FMA introduces flow matching to few-shot learning. By formulating traditional PEFT methods as one-step updates, FMA enables more precise iterative alignment between visual and textual features. As argued by athe uthors, FMA better handles entangled multimodal distributions, especially in challenging datasets.\n2. The framework is architecture-independent and can be integrated with various pre-trained vision-language models (e.g., CLIP, CoOp, LoRA) without altering their internal structures.", "weaknesses": "1. The multi-step flow matching process requires iterative training and inference, which increases computational cost compared to traditional one-step PEFT methods, potentially limiting scalability for large datasets or real-time applications.\n2. The method relies on carefully chosen parameters such as the number of inference steps, step size, and noise schedule. Suboptimal tuning can lead to degraded performance or instability during alignment. Especially when flow matching originates from generative modeling, and its adaptation to supervised classification tasks lacks rigorous theoretical grounding, in terms of convergence and optimal stopping criteria.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "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": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)