Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts
Authors: Yubin Wang
XINYANG JIANG
De Cheng
Xiangqian Zhao
Zilong Wang
Dongsheng Li
Cairong Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: prompt tuning
explainable AI
knowledge discovery
prototype learning
Primary area: interpretability and explainable AI

Abstract: Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which is essential for enhancing AI reliability and enabling AI-driven knowledge discovery. In this paper, rather than learning abstract prompt embeddings, we propose the first framework, named Interpretable Visual Prompt Tuning (IVPT), to explore interpretability for visual prompts by introducing cross-layer concept prototypes. Specifically, visual prompts are linked to human-understandable semantic concepts, represented as a set of category-agnostic prototypes, each corresponding to a specific region of the image. IVPT then aggregates features from these regions to generate interpretable prompts for multiple network layers, allowing the explanation of visual prompts at different network depths and semantic granularities. Comprehensive qualitative and quantitative evaluations on fine-grained classification benchmarks show its superior interpretability and performance over visual prompt tuning methods and existing interpretable methods. Our code is available at https://github.com/ThomasWangY/IVPT.

Published as a conference paper at ICLR 2026
EXPLORING INTERPRETABILITY FOR VISUAL PROMPT TUNING WITH CROSS-LAYER CONCEPTS
Yubin Wang's : :iinyang Jiang"t De Cheng', Xiangqian Zhao?, Zilong Wang",
Dongsheng Li", Cairong Zhao"!
School of Computer Science and Technology. Tongji University
"iicosooft Research Asia
3 School of Telecommunication and Engineering, Xidian University
ABSTRACT
Visual prompt tuning offers significant advantages for adapting pre-trained vi- sual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which is essential for enhancing AI reliability and enabling Al-driven knowledge discovery. paper, rather than learning abstract prompt embeddings, we propose the first framework, named Interpretable Visual Prompt Tuning (IVPT), to explore in- terpretability for visual prompts by introducing cross-layer concept prototypes. Specifically, visual prompts are linked to human-understanddble semantic con- cepts, represented
this
of category-agnosta prototypes, each corresponding a specific region of the image. IVPT then aggregates features from these regions generate interpretable prompts for multiple network layers, allowing the expla- nation of visual prompts at different network depths and semantic granularities. Comprehensive qualitative and quantitative evaluations on fine- tinegrained classifica- tion benchmarks show its superior interpretability and performance over visual prompt tuning methods and existing interpretable methods. Our code available at hhtpt::://// // tttbb/..tttt......m/ thub. coom/Toomaa Waans///nnnngnna
INTRODUCTION
Visual prompt tuning (Jia et al. 2022 has emerged promising approach for adapting pre-trained visual foundation models (He et al. 2022, Chen et al. 202T]] Dosovitskiy (020) to specific tasks, allowing for flexible task customization while avoiding the need for full-scale model fine-tuning. This approach has shown significant advantages in terms of efficiency and task adaptability, yet it still faces a major challenge in interpretability. Most prompt tuning techniques (Jia et al.) 2022 Dong et al. 2022, Gao et al. 2022) involve learning abstract embeddings that automatically capture high- level features, providing implicit guidance for the model but offering limited numan-understande information into its decision decision-making process. The lack of transparency in these prompts hampers the ability to assess the trustworthiness of AI systems and limits the scope for uncovering valuable insights through Al-driven analysis, especially in safety-critical domains such healthcare and autonomous driving. Although existing multi-modal approaches (Bie et af., 2024] Yao et al. 2023 Bulat & Tzimiropoulos (023) seek improve prompt interpretability using human-designed guidance, such as natural language, autonomously discovering clear and meaningful explanations for visual prompts during training remains a significant challenge.
Recent methods have been developed to enhance the interpretability of visual models, such as concept- based methods (Fel al. 2023) Ghorbani et al. 2019 Kim et al., 2018 Zhang et al.) 2021) that utilize high-level abstractions to represent learned features and attribution basedn methods YYssinski) et al., 2015) [Selvaraju et al., 2017) that identify regions in the input image that significantly influence the model's ' predictions. In this paper, we focus on an interpretable scheme that integrates both attribution and concept discovery. We aim to interpret abstract visual prompts by linking them to human-understandabbl concepts, each grounded in distinct image regions and assigned an importance
"Tii work was done during Yubin Wang's 's internship at Microsoft Research Asia Corresponding Authors. Email: xinyangjiang @ microsoft. com, ietaaaaarron@@tonji.ccooo

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Published as a conference paper at ICLR 2026
score based on its contribution to the model's prediction, all achieved in unsupervised manner, without relying on annotations or internal model access. For example, given an image of a bird, we learn an interpretable prompt token with the concept of a "bird wing". quantifies its impact predicting the class "bird", and localizes this concept to the wing region in the image.
Recently, several methods (Chen et al. 2019 Nauta al. 2021 [Wang al. 2021 Huang et al. 2023 have explored this attribution-concept hybrid interpretability scheme by learning part prototypes to represent specific concepts. However, these methods are primarily designed for conventional neural network architectures rather than visual prompt tuning, presenting three key challenges. First, prior methods focus on grounding concepts to image regions, while the connection between these concepts and abstract prompt embeddings remains largely unexplored. Second, existing methods extract concepts from the features of the final layer of deep models, neglecting the need to interpret visual prompts learned at different layers and to capture cross-layer interactions among concepts. Third, existing approaches typically learn a separate set of prototypes for each class, making it difficult to analyze the model's behavior across classes. We may be unable to capture and interpret shared concepts that may appear in multiple categories. We assume that prototypes are not inherently tied to specific pixels but gain semantic meaning through their similarity to localized patches in the image.
As a result, we introduce Interpretable Visual Prompt Tuning (IVPT), a novel framework that emphasizes the interpretability of visual prompt tuning. IVPT advances beyond abstract embedding learning by introducing cross-layer concept prototypes that connect learnable prompts with human- understandable visual concepts. Specifically, each interpretable prompt is generated by aggregating features from an image region corresponding to a concept prototype at a specific layer. These prototypes are distributed across multiple network layers, enabling IVPT to interpret visual prompts at different semantic depths. Unlike traditional methods, IVPT learns category-agnostr concept prototypes, enabling the model to capture shared, non-overlapping concepts across various categories, offering a more coherent explanation by focusing on the commonality of concepts. We also observe that coarse-grand garree-rrined prompts deep layers capture high-level concepts but lose fine-grained details, while overly specific prompts in shallow layers lack broader contextual understanding. To capture interactions between concepts across layers. IVPT bridges the gap with cross-layer prompt fusion to align fine- and coarse-grained tokens. By linking local-to-globa semantics across network layers with varying granularity, IVPT emulates human visual reasoning, thus enhancing interpretability.
The main contributions of this paper threefold: (1) We propose novel framework for interpretable visual prompt tuning that uses concept prototypes as a bridge to connect learnable prompts with human-bndeesttnddable visual concepts.
(2) We introduce cross-layer concept prototypes to explain prompts at multiple network layers while modeling their relationships in a fine-to-coarse alignment. (3) We demonstrate the effectiveness of our approach through extensive qualitative and quantitative evaluations on fine-grained classification benchmarks and pathological images, showing improved interpretability and accuracy compared to both conventional VPT methods and interpretable methods.
2
RELATED WORKS
2.1
INTERPRETABILITY OF VISUAL MODELS
Numerous methods have been developed to enhance the interpretability of visual models. Concept- based methods (Fel et al. 2023 Zhang et al. (021) use high-level abstractions to represent learned features, facilitating understanding of the internal representations of a model. However, these approaches typically operate single layer and lack region-level grounding, failing to provide mechanisms for linking concepts to prompt embeddings or enabling multi-layer interpretability. Atribution-based methods (Yosinski et al.) 2015) Selvaraju et al. (017) identify key regions in the input images that affect predictions, claritying the focus of the model during making decisions. While effective at identifying influential regions, these methods do not offer semantic concept alignment or interpretability of prompt tokens, especially across multiple layers. However, these approaches often lack transparency when applied to visual prompt tuning and generally fail to provide fine-grained, hierarchically organized explanations. To address this issue, we focus on part- cart-proootype methods (Chen et al.] 2019: Nauta et al., 2021] Wang et al., 2021; Huang et al. 2023), which offer localized representations associated with specific image regions. ProtoPNet (Chen et al. 2019), as the foundational model, compares input features with prototypes of object parts,
while its extensions (Rymarczyk et al. 2021 2022
Ma al. 2024) further enhance interpretabilityy
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ProtoTree (Nauta et al. 2021) integrates prototypes with decision trees, while TesNet (Wang et al. 2021) arranges prototypes for spatial regularization. Further advancements include evaluating prototype interpretability using a benchmark developed by Huang al. (Huang et al. 2023). However, these frameworks face limitations when applied to visual prompt tuning: they (1) lack concept-pronpt linkage, unable to connect concepts to prompt embeddings; (2) lack cross-layer interpretation, being restricted to final-layer prototypes; and (3) rely on class-specific concepts, limiting cross-category analysis. Consequently, they neither define trainable prompts nor organize concepts hierarchically across layers. In contrast, IVPT addresses this gap by employing category-agnoster prototypes at multiple layers to define interpretable prompts and model their fine-to-coarse relationships.
2.2 VISUAL PROMPT TUNING
Visual prompt tuning (Bowman et al.
2023 Hu et al. 2022 Loedeman et al. 2022 Wu et al. 2022
Tia et al. (022) has become a popular parameter- parfmeter-efficient approach to transfer the generalization capabilities of pre-trained vision models to various downstream tasks. Recent studies (Dong et al. 2022 Zhao et al.) 2024; Wang et al. 2024; Sohn et al., 2023 Zheng et al. 2025) focus on modifying inputs by embedding learnable parameters, aiming adjust the input distribution and enable frozen models to handle new tasks. For instance, VPT (Jia et al. (222)) introduces a limited set of Icarnable parameters input tokens to the Transformer. PViT (Herzig et al. (024) uses specialized parameters in a shared video Transformer backbone for both synthetic and real video tasks. E2VPT (Han et al. 2023) incorporates learnable key-value prompts in self-attention layers and visual prompts in input layers to improve fine-tuning, Gated Prompt Tuning (Yoo et al., 2023) learns a gate for each ViT block adjust its intervention into the prompt tokens. Despite their exceptional performance, these methods fundamentally treat prompts as unconstrained black-box vectors, providing no inherent interpretability or semantic grounding. They lack three critical properties: (i) spatial grounding to concrete image regions, (ii) human- -umaannndderttaddabble concept definition, and (iii) cross-layer semantic structure. This represents a significant gap in the VPT literature, as interpretability remains limited to final features/logits rather than the prompts themselves. Prompt-CAM (Chowdhury et al.) 2025 learns class-specific prompts for a pre-trained ViT and using the corresponding outputs for classification. However, these learned prompt tokens are typically unconstrained embedding vectors: they are not grounded in image regions, associated with reusable concepts, or linked across layers into a coherent hierarchy. In contrast, IVPT redefines prompts as embeddings that are explicitly aligned from shallow attributes to deeper parts across layers. Thereby, work establishes the first interpretable paradigm for VPT-a concept-attribttion hybrid-enabling direct semantic interpretation of prompts while maintaining parameter efficiency.
category- concept
3 IVPT: INTERPRETABLE VISUAL PROMPT TUNING
3.1 OVERALL PIPELINE
In this subsection, we explain how the proposed IVPT enhances the interpretability of visual prompts with the explainable properties of concept prototypes, as illustrated in Figure T
Constructing interpretable prompts. Given pre-trained Transformer model of N layers, we
learn a set of continuous embeddings to serve as prompts within the input space of a Transformer layer, following the methodology outlined in Visual Prompt Tuning (Jia et al. 2022). Specifically, for (i 1)-th Layer L4+1. we denote the collection of n input learnable prompts as P, {pf. E Rd k E N. < k I n]. where d denotes the feature dimension of the VIT backbone. During fine-tuning, only the prompt embeddings are updated, while the Transformer backbone remains frozen.
However, the learned prompts often abstract and difficult to interpret. To better explain their meaning, it is necessary to establish way of relating them to specific human-underssandable concepts. We use prototypes to convey set of specific meanings. These prototypes are generally focusing on particular image regions sharing similar semantics across images of different categories. As result, set of m concept prototypes Q {qx E Rd l E N, 1 k < m] is proposed to interpret group of prompts P in a specific Transformer layer as:
Pk F(qu: E), 1....,,
(1)
'The layer index i is omitted in subsequent equations, as they represent computations for a specific layer.
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Prompt Embeddings
Concept-level Attention Maps
Concept Prototypes
Patch Embeddings
Concept Prototypes
Classifier
Culculation
Softmax
Transformer Layer
Coarse-groined Region Guidence
tejection
Spatial Maps
Fuston
Layer
Concept Region Discovery
00-
Concept-level Attention Maps
Transformer Layer
Coansegrained Region Guidance
Intro-region Feature Apgrepation
Argmax
Argion
Manking
Grouping
Fusion
Layer
000-0
Concept-level Region Maps
Transformer Layer
Concept Region Discovery
Patch Embeddingg Prompt Embeddings
Intra-region Feature Aggregation
Prompt Prompt Embeddlnng
Figure IVPT introduces prompt embeddings. At each layer, Concept Region Discovery (CRD) module captures specific visual concepts as concept-level image regions, while Intra-region Feature Aggregation (IFA) module aggregates features grouped by these region maps to obtain prompt embeddings. Several sets of layer-wise concept prototypes are used to capture concepts across layers, while the fusion layer, guided by the coarse-grained region, fuses the prompts a fine-to-coarse manner as the final prompts to replace flat visual prompts at each layer.
concept prototypes to generate explainable visual
Here E E R(xxw)xxd indicates the patch embeddings at the corresponding layer, where and w represent the spatial dimensions (height and width), and d the feature dimension. In this context, the function F is learned in IVPT to map each prompt embedding P to a corresponding concept prototype qı- process that will further explained. To better understand the concept represented by a prototype E Q, IVPT grounds this prototype to a concept region R& within specific image by our proposed Concept Region Discovery (CRD) module, denoted as the function FCRD:
E), R FCRDD(K..F E).
k ==
This operation explains the semantics of prototypes the image level by highlighting the region with high attention on the image. Given a concept prototype qs. its corresponding prompt embedding Pù is derived with features within the concept region R2 through our designed Intra-region Feature Aggregation (IFA) module, denoted as the function FIFA:
(R E), = m.
(3)
Finally, we express the function FF as F= FORD FIFA, enabling us to present prompts with prototypes more interpretably. in Eq. consists of two steps, where CRD in Eq uses prototype 4s. semantic anchor to discover localized image regions R₁ and IFA in Eq, aggregates token embedding within RG to obtain Pk. m concept prototypes qa explain each visual prompt p& by grounding it in semantically meaningful image regions and representing it with the feature of these regions. More details will be provided in Section Conccet-prototype-bae
prompt learning.
Exploring cross-layer interpretation. To interpret prompts across network layers with varying granularity, we propose cross-layer concept prototypes. At each layer, prompts are represented by a layer-specific set of prototypes, as shown in Figure [ We adopt more prototypes shallower layers to capture fine- fine-grained and diverse visual features. As the network depth increases, the semantic representations become more abstract, and the number prototypes progressively decreases, reflecting the increased conceptual coherence of features in deeper layers. Section Cross- prompt fusion will explain how IVPT fuses fine- nee-grained prompts into n prompts integrated for fine-tuning. Different from (Jia et al. 2022), consider the prompts Py after the last Transformer layer as the final concept representations. which are passed into a classification head to output individual concept-conditioned category scores 8g. These scores reflect the likelihood of the input
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image belonging to given class, conditioned on the interpretable prompt Pk, highlighting the significance of each concept for classification. The overall score obtained by averaging individual concept-conditioned category scores, allowing the model to aggregate multiple complementary semantic perspectives for more robust predictions. We use cross-entropy to calculate the classification loss Lats by comparing the score with the one-hot groundtruth label y:
Lats CELoss
8k, where Są Heady(P
(4)
This formulation follows the common practice in prototype based interpretable models of aggregating multiple concept-conditioned category scores.
3.2 CONCEPT-PROTOT BASED PROMPT LEARNING
In this subsection, we dive into the pipeline of learning explainable prompts with concept prototypes.
Concept region discovery. Concept Region Discovery (CRD) module associates concept proto- types with specific image regions, denoted as function FORD in Eq. First, the patch embeddings E produced by the ViT are reshaped into feature map E E R Then, we compute concept-level attention maps A E [0, 1|mxxxx, with each element Ok.ij representing the attention score of a learn- able concept prototype embedding q Q on the patch embedding eij E E. Similarly to previous research (Aniraj et al. [2024] Huang & Li 2020 Hung et al.) 2019), we obtain the attention map using negative squared Euclidean distance function measure similarity, followed by Softmax function across the m channels and injection of spatial bias:
(- |e: qx1l
exp
bk,ij
E1= exp
- lle,, qul1²)
where bk.i denotes the corresponding element in Iearnable spatial bias maps B Rmxxxx We employ a combination of part-shaping loss functions from (Aniraj et al. 2024) to guide the discovery of non-overlapping regions associated with prototypes, denoted Lps- This set of loss functions ensures the discovery of distinct, transfornation-iivaaiann parts with unique assignments, foreground presence, universal background, minimum connectivity, and minimize prototype polysemanticity, whose details are provided in the Appendix. Finally, concept region maps R E [0, 1|m=xx= are generated based on the attention map A, where each image patch is assigned the attention value of the concept that has the highest attention score in A. Thus, each element T,,ú E R represents the probability that given image patch belongs to the corresponding concept and is defined as:
On,i {0
if arg max; Gk.āj otherwise.
Tn.ij
(6)
Intra-region feature aggregation. We introduce Intra-region Feature Aggregation (IFA) module corresponding to function FIFA in Eq. 3 which obtains an interpretable prompt corresponding to specific concept by aggregating the patch embeddings E within the corresponding concept regions. Specifically, we first calculate aegion-conditional feature maps Z Bm=xkxxx where the feature at each location is re-weighted based on the specific region and the probability element indicated in R by Z = R @ E, where @ denotes an unsqueeze operation on the last dimension of R. followed by noment-wise multiplication with E. Finally, we aggregate features within each region to derive the prompt Pa corresponding to the k-th concept prototype:
Ens
P& Ees FR,is
1: == 1,2,....
where Zk.ig E IRd represents feature vector in Z.
3.3 CROSS-LAYER PROMPT FUSION
IVPT employs cross-layer concept prototypes to represent prompts from different Transformer layers, capturing varying levels of semantic granularity. Shallow prompts associated with
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Concept-lecel Region Mrlge Fineegrained Prompt Embeddings
Badkground
Group
Group
Group 3
Group
Groupe Layer
[+
Transformer Layer
gralned Region Maps
averagdd
Layer
Coarsegrained Region Maps
Prompted Vision Tronsformer
Figure 2: Illustration of cross-layer prompt fusion.
more prototypes reflecting fine, low-level semantics, while deep prompts are associated with fewer prototypes with coarse, high-level concepts. To explore relationships among prompts with different semantic granularities, IVPT introduces cross-layer prompt fusion, where prompt embeddings at shallow layers are combined to form deep prompts containing high-level concepts, as shown in Figure 2 Let P denotes the set of prompts generated in the previous section. We partition the prompts into groups based on shared high-level semantics by assigning each Pı a group label y/: = (Pu). where 0 S U/ < Here, V/, = O denotes the background class, and represents the total number of deep-layer prompts with distinct high-level semantics. The grouping layer fg is implemented as linear layer followed by Gunbel-Softmax (Jang et al. 2016) operation to find the group with the largest value. Within each group i, prompts are aggregated by computing their mean vector P,, which associated with high-level concept.
To enable the group layer to effectively establish the intended correspondence between prompts in the shallow layers and those in the deep layers, introduce concept region consistency loss Lcon- This loss ensures that the combined concept regions associated with a group of fine-grained prompts align closely with coarse-grained region from the final layer corresponding concept. Formally, the concept region consistency loss is defined as the KL divergence between a set of grouped fine-graines region maps and their corresponding parse-grained region maps:
high-level
Leon Σ KL Ri), where R
Σ R KES.
(8)
Here R denote the combined fine-grained region map of the i-th group, and R is the i-th coarse- grained region map. which is derived from the last Transformer layer and used for classification. This aligmment encourages the fine-gra iie-ggrined prompts to merge cohesively, resulting in better alignment with the coarse-grained prompts with high-level semantics. The averaged prompts p, are then passed through layer normalization and an MLP:
p (LP(LayeNNorm(p.)
i= 1 I....,
The set of fused prompts f {p!* e Rd MiNNNNN, n] are finally used tune the network. The total training loss is defined as:
C ActoLde + Apeepps + deon Coons
(10)
where Acter Aps and Acon balancing ratios for these losses. Specifically, the part-shaping loss and the concept region consistency loss are averaged across layers. These losses enable our model to balance classification performance with interpretability, thus promoting explainable recognition.

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Table 1: Quantitative comparisons of interpretability using consistency scores (Con.) and stability scores (Sta.), as well as the accuracy (Acc.) on the CUB-200-2011 (Wah et al., 2011) dataset are provided. The results of IVPT are compared against related approaches, including conventional part-prototype networks and various visual prompt tuning methods. Best in bold.
Methods
D
DeiT-B
Dino
Dino
V-LL
Con.
Acc.
Con
Avc.
Cun.
Acc.
Con.
Con.
Conventional Prototpoe Networks
ProtoPNet Chen 2019
14.7
52.7
80.2
16.2
60.5
81.0
23.9
5288
82.7
27.6
57.0
85.8
26.7
59.9
86.1
ProtoPool Devmmitzzkk
26.9
58.3
81.2
29.6
82.5
38.2
TesNet Wang er 2021
32.3
66.7
82.3
38.5
67.5
84.3
40.8
64,1
84.4
51.2
66.7
86.8
55.3
68.9
88.3
Huang dral. Fuang et 2023
54.7
70.2
85.3
57.2
70.9
85.0
65.7
67.5
86.9
68.6
71.4
89.9
67.4
74.3
90.3
Visual Prompt Tumnnng Methods
VPT-Shallow (Ti al M023
5.6
35.5
82.4
8.9
33.6
83.5
9.0
39.2
86.2
12.7
40.3
88.5
11.3
44.5
88.7
VeT-Deen Dia Uireet Eure
7.7
37.1
82.7
9.2
37.8
84.0
11.5
40.6
86.5
14.6
19.5
89.1
14.0
47.6
89.5
EVT et 2010
13.7
45.2
83.8
26.7
41.9
84.3
22.3
43.2
27.5
27.3
55.0
89
Gated Prompr Timing Yoo et 2023
8.6
32.7
84.1
16.8
28.9
84.5
28.5
41.2
86.8
34.6
60.1
89.5
35.7
61.5
89.7
VPT Shallow (w/ Protd.)
51.0
65.0
83.4
50.9
62.4
84.9
52.0
67.1
87.1
60.5
68.5
89.9
54.5
71.7
90.1
VPT-Deeg Deep (w/ Proto.)
5488
66.7
84.1
99.7
70.3
85.6
61.8
65.3
87.3
70.2
72.5
90.3
61.9
75.0
90.7
E VPT (w/ Proto.)
60.8
65.2
85.2
54.7
69.5
86.1
62.4
67.3
87.8
66.6
70.9
90.1
64.6
76.1
90.7
Gated Prompt Tuning (wl Proto.)
56.7
68.2
85.2
61.3
65.4
86.2
63.4
68.2
87.8
70.3
71.8
90.4
67.2
75.6
90.5
IVPT
63.1
73.4
86.2
64.5
72.3
86.7
63.5
70.2
88.1
75.3
90.8
72.6
77.4
91.1
4
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
We follow visual prompt tuning (Ji al. 2022) paradigm for classification, supervised by image-
level category labels. We employ three Dino V2 Oquab et al.] (202)) variants (ViT-S, ViT-B, ViT-L) with register tokens (Darcet al. 2023 and two DefT (Touvron et al. 2021) variants (ViT-S, ViT-B) as backbones, with images resized to 518 x 518. We establish the cross-layer prototypes across the last four layers, with the number of concept prototypes m (including background) set to 17, 14. 11, and 8 per layer, and the number of fused prompts n (as well as coarse- -rrnine prompts) set to 4. Balancing ratios Acts Aps and Xeon are all set to For interpretability, we evaluate scores with 10 concepts parts (the second-to- lasst layer), following the number of the evaluation method in (Huang et al. 2023). The concept prototypes at each layer are all initialized randomly with a standard variation of 0.05. More details are provided in the Appendix.
Datasets. We evaluate both the classification accuracy and interpretability on the CUB-200-201
dataset (Wah et al. 201T), which is the only benchmark with part-level annotations required for fine-grained interpretability evaluation. It contains images of 200 bird species, including 5,994 training images and 5,794 testing images. Each image includes kcypoint annotations for 15 different bird body parts. We also perform visualizations on the Gleason-201 2019 9 (Nir et al. 2018), the Stanford Cars (Krause et al.) 2013) and the FGVCAircraft dataset (Maji et al. 2013). Gleason-201 is collection annotated prostate cancer histopathology images about automated Gleason grading of cancer aggressiveness. Stanford Cars is a dataset consisting of 16,185 images of 196 different car models from 10 car manufacturers.
contains 10,200 aircraft images, with 100 images for each of 102 different aireraft variants. Additionally, we conduct quantitative experiments other fine-gras mnn-grained datasets: PartImageNet, which provides pert-level annotations for 158 classes from ImageNet, and PASCAL-Paar, containing detailed part annotations for objects in the PASCAL VOC.
two
Evaluation metrics and baselines. We evaluate the interpretability of the prompts using the consistency and stability scores defined by (Huang et al., (023). We compare our approach with two categories of methods. First, we compare it with current state-of-the-
part prototype methods, such as ProtoPNet (Chen et al. 2019). ProtoPool (Rymarczyk et al. 2022), TesNet (Wang al. (021). and Huang et al. (Huang et al. 2023). which are evaluated based on vategory-specine prototypes without sharing between categories. We also validate the effecttieneess with several visual prompt tuning baselines (Jia et al. 2022; Han et al. 2023 Yoo et al.) (202)), along with variants that incorporate our proposed poncept-rroootype-bae based prompt learning after the last layer for classification.
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Original
Part 2
Part
Part 2
Part
Fine
Coarse
Figure 3: Qualitative results of region maps via the structure of cross-layer prompt fusion.
Gardurar Tumen
Gandular Vesicle
Thnue
(a)
Gleason-2019
(b) Stanford Cars
(c) FGVCAircraft
Figure 4: Qualitative results on patch-leve images in the Gleason- 2019 dataset about prostate cancer, well as Stanford Cars and colors representing distinct concepts or features relevant to the prediction.
about general fine-grate fine-grained classification, with different
4.2 QUANTITATIVE COMPARISONS
We first evaluate the interpretability and accuracy of IVPT with quantitative metrics. Table I demonstrates that IVPT outperforms conventional part prototype networks in consistency scores (measuring coherent concept alignment across instances), achieving gains of +8.4% +7.3%, +6.7%, and +5.2% for DeiT-S, DeiT-B B. D2-B and V2-LL, respectively. However, lags behind Huang et al. (Huang et al. 2023) by 2.2% for D2-o likely due to smaller models' limited capacity maintain both interpretability and accuracy. Additionally, IVPT achieves higher stability scores (reflecting robustness to imput variations) than prior works, with improvements of 3.2%, 1.4%, 2.7%, 4.5%, and 3.1% for DeiT-S, DeiT-B, D2-S Dino and V2-L highlighting the framework's resilience to distribution shifts. IVPT also maintains prediction accuracy, even slightly surpassing part prototype networks in recognition performance. Notably, the distinct trends in consistency and stability metrics suggest excellent semantic alignment fidelity and prediction invariance under perturbations.
4.3 QUALITATIVE INTERPRETABILITY ANALYSIS
Qualitative results of the cross-layer structure. To validate the effectiveness of the cross-layer structure in capturing fine-to-coarse semantics and demonstrate how it addresses the limitations of non-cross-layer concept learning, we provide qualitative visualizations of region maps from three sample images across different layers. As shown in Figure 3] ine-grained semantics at lower layers progressively transition to coarse-grained representations at higher layers, enabling inter-layer interaction and systematic explanation pathway between detailed and abstract semantics. By leveraging this cross-layer relationship, IVPT efficiently extracts intra-region features to construct interpretable prompts, thereby improving semantic alignment across granularities in complex visual scenes. These results explicitly illustrate how IVPT's cross-layer design enhances its capacity to model and explain multi-granularity concepts.
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Concept D
Concept 1
Concept 2
Concept 3
Table 2: Performance comparison on PartImageNet and PASCAL-Part datasets with DinoV2-1
PartImageNet Sta.
PASCAL-Part
Methods
Con.
Acc.
Con.
Sta.
Acc.
ProtoPool
47.5%
56.9%
61.9%
52.3%
45.9%
75.1%
Huang et
58.6%
62.3%
66.9%
67.2%
68.6%
78.9%
VeT-Deep
54.2%
63.7%
73.9%
61.5%
62.9%
85.95 95.9%
IVPT
63.2%
71.5%
74.2%
72.6%
77.4%
86.4%
Category:
Category: House Sparrow
Category: Forster's 5 Tern
Cortus When
Negirtana
0.45
Table 3: Ablation component combinations.
0.20
9.27
0.22
2.25
0.26
Methods
Con.
Sta.
Acc.
Baseline
62.7
64.3
88.4 4
Sputial bias maps
63.5
66.7
88.7
Figure 5: Illustration of coarse- -eriinedg region maps with prototypes across images highlight- ing the importance score of each concepts.
+
Intra-region feature aggregation
65.4
68.3
89.8
+ Cross-layer prototype
70.4
70,9
90.5
Fine- Tine-to-coarse prompt fusion
75.3
75.9
90.8
Qualitative results on other recognition tasks. To assess the applicability of IVPT in pathology. we perform explainability experiments on the Gleason-2019 dataset for prostate cancer classification. Whole-slide images are divided into 256x256-pixel patches, each classified with Gleason scores of 3, 4, or 5. As shown in Figure 4a), IVPT effectively highlights key grading features, such as green regions (glandular lumen) and purple regions (diseased glandular vesicles), consistent with pathological standards. Blue areas denote common tissue types. This visualization underscores IVPT's potential to enhance diagnostic workflows by clarifying the model's reasoning. Further analysis of concept significance across grades is provided the Appendix.
Moreover, identify key contributing concepts in two fine- graingd recognition datasets: Stanford Cars (Krause et al. (013) and FGVCAircraft (Maji et al. (0013). As shown in bb) IVPT focuses on fine-grained details essential for distinguishing similar classes. For Stanford Cars, identifies critical concepts such as the emblem or handle (blue). rearview mirror (orange), wheel (green), and headlight (red). Similarly, for FGVCAircraft, it detects key components including the tail (blue), fuselage (orange), turbine (green), and wing (red). The concepts associated with individual prototypes tend cluster around semantically related features. When necessary, fine- grained distinctions can be uncovered by subdividing these clusters, enabling interpretable refinement without evidence uncontrolled polysemantic mixing.
Importance scores of explainable concept-level regions. We analyze the importance scores linked to concept-level regions associated with prototypes for classification in Figure 5 The analysis reveals how these four concept prototypes affect classification across three bird species. Each image is associated with region map, where patches are assigned to one of the regions (concepts), each contributing differently to the classification. Concept-level importance scores are computed via Equation and displayed as bar graphs, showing each concept's 's varying influence. For instance, Concept has a high score for Cactus Wren (0.43), while Concept 2 is more influential for Forster's Tern (0.37). The region corresponding to the highest-scoring concept overlaps precisely with the discriminative concept of the image, which aids the interpretation of critical areas for classification.
4
4.4 RESULTS ON PARTIMAGENET AND PASCAL-PART
To validate the generalization of our IVPT beyond CUB, we conduct additional quantitative experi- ments two fine- fine-grained part-annotation datasets: PartImageNet and PASCAL-Part. Following the evaluation protocol in this paper, a single representative point was extracted for each background semantic part (c.g., the centroid of certain part in PartImageNet). As shown in Table 2 IVPT achieves superior interpretability and robustness on key metrics across datasets, along with con- sistent edge in classification accuracy, confirming that our prototype learning framework effectively captures monosemantic concepts in diverse scenarios.
Beyond the quantitative metrics, we present qualitative analysis to visually demonstrate the concept discovery capability of IVPT. Figure showcases example visualizations from the PartImageNet and PASCAL-PPar datasets. Its ability to discover and leverage semantically consistent concepts across

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Figure 6: Qualitative analysis on PartImageNet (top) and PASCAL-Part (bottom).
object categories with vastly different morphologies. For instance, visualized in Figure 6 (bottom), the model trained on PASCAL-Part identifies "head" coherent concept for both rigid objects like airplanes and articulated animals like horses and dogs. This demonstrates that IVPT grounds its reasoning in high-level semantics rather than low-level textures or shapes specific to category. The spatial region maps show that the model preciscly localizes these parts (c.g., the nose of plane, the head of horse) and associates them with the same underlying concept. This cross-category concept sharing suggests that IVPT builds a rich, part-based representation of the world. During inference, the presence of such universal concepts (e.g.. "head", "leg", "body") provide a robust, interpretable evidence trail, validating the generalization of IVPT's interpretability.
4.5 ABLATION STUDIES
We first investigate the impact of various components on model performance, as illustrated in Table The baseline applies prompt learning for explainability exclusively in the last layer, using feature aggregation strategy with global-level attention instead of conditioning on specific regions. Each additional component consistently improves performance across all metries. Specifically, intra-region feature aggregation achieves notable improvement in classification accuracy, with an increase of over 1%, as learning features within specific regions enhances their discriminative power. Furthermore, the cross-layer prototypes as well as the fine-to-coarse prompt fusion results in substantial improvement of both the consistency score and stability score, validating the effectiveness of cross-layer explainability. We also provide ablations the number of prompted layers and concept prototypes. Please refer to the Appendix for more details.
4.6
HUMAN STUDIES
We conduct comprehensive human evaluation study with 20 participants validate IVPT's inter- pretability, demonstrating strong aligmment between learned prototypes and human- undersndddttt concepts. The study achieves 97.5% accuracy in concept annotation and high ratings across three key dimensions: detail preservation (4.7/5), semantic abstraction (4.8/5), and transition naturalness (4.8/5), indicating that IVPT's hierarchical concept learning effectively mirrors human cognitive processes, Complete methodological details and illustrative examples are provided in the appendix.
5 CONCLUSION
In this paper, we propose Interpretable Visual Prompt Tuning (IVPT) framework, which enhances the interpretability of visual prompt tuning by associating prompts with human-understandable visual concepts through concept prototypes. With novel cross-layer structure, IVPT aligns concepts across multiple layers for learning explainable prompts. Extensive evaluations demonstrate that IVPT enhances both interpretability and accuracy compared to other methods, highlighting IVPT's potential to enable transparent and effective AI analysis, especially in critical applications. Our limitations include reliance in-domain concept prototypes, which limit flexibility in diverse domains.
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ACKNOWLEDGEMENT
This work was supported by National Natural Science Fund of China (No. U25A20527, 62473286) This work was also supported by Shanghai Municipal Science and Technology Major Project (No. 202SSHZZZX025510)) This work was supported in part by the National Natural Science Foundation of China under Grants 62576262, in part by the Key Research and Development Program of Shaanxi Province under grant 2024SF-YBXM-61
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A ADDITIONAL TRAINING DETAILS
We train all our models using the Adam optimizer, with class token, position embedding, register token (in DinoV2) and distillation token (in DeiT) unfrozen. We use starting learning rate of 2 x 10 for the fine-tuned tokens VIT backbone, spatial bias maps and learnable concept prototypes, and 1 X 10-2 for the other tunable layers of the model, including the fusion layers for prompt generation and the linear head for classification, with a batch size of 16. Training lasts for a total of 25 epochs with duration of 2 hours 4 NVIDIA RTX A6000 GPUs, and employ step learning rate schedule, reducing the learning rate by factor of 0.5 every 4 epochs. The concept prototypes at cach layer are all initialized randomly with standard variation of 0.05.
B DETAILS OF PART-SHAPING Loss
While the classification loss Lete ensures the discriminative capability of the discovered parts, it does not explicitly guide the attention maps to focus on semantically salient regions the object. To mitigate this limitation, we follow the methodology outlined in (Amiraj et al. 2024), which proposes a set of additional objective functions that incorporate structural priors into the learning process, summed the part-shaping loss Lps. Below, provide detailed description of each loss function.
B. ORTHOGONALITY LOss
Orthogonality loss L1 encourages decorrelation among the learned part embedding vectors by minimizing their pairwise cosine similarity, Formally, for the modulated embedding vectors v the loss is computed as:
L1
(11)
k=
m
m
B.2 EQUIVARIANCE LOSS
Equivariance loss Ley ensures consistent part detection under geometric transformations. This is achieved by: (1) applying random affine transformation T to the input image, (2) processing both original and transformed images through the network, (3) twversetransformmig the attention maps from the transformed image, and (4) computing the cosine distance between the original and transformed attention maps. This formulation encourages the learned parts to be equivariant to rigid transformations like translation, rotation, and scaling. This loss is mathematically defined using the attention map function (x)), which generates the 1t attention map for input image x. The loss computes the normalized correlation between the original attention maps and those from transformed images after inverse transformation:
Ax*()) T- (T(x)))
Log =
(12)
K
I (x) | (T()))I
&
B.3 PRESENCE LOSS
The presence loss consists two components designed to enforce different spatial priors:
Foreground presence loss f,, ensures each foreground part appears in least some images within mini-batch. For batch (xi...., x operates on pooled attention maps A (xx) to prevent single-pixel solutions:
C, =
Σ max a (xi) b.i.j
(13)
K
Background presence loss Lp, enforces consistent background detection across all images, with spatial bias toward image boundaries through soft mask M:
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P.
Σ
log
max
maydi (xb)
K
(14)
The mask weights maj increase radially from the image center:
mij = 2 (i-1 H 2 1
j j-1 1 2
+2
(15)
W - 2
B.4
ENTROPY LOss
Entropy loss Cent encourages unambiguous part assignments by minimizing the entropy of attention distributions:
K+1
Cent
k agg
log (a
(16)
K
B.5 TOTAL VARIATION LOsS
Total variation loss Lt promotes spatial coherence in attention maps without imposing strict shape constraints, favoring connected components through standard total variation regularization. This loss employed to encourage spatial smoothness the learned part attention maps. This regularization term computes the average magnitude of spatial gradients across all part maps:
K+1 Σ
L.v
V
(17)
HW
where Valy represents spatial gradient (computed via finite differences) at position (i,j) in tt attention map A This formulation promotes the formation of coherent regions in the attention maps while remaining agnostic to specific part shapes. The normalization by image dimensions ensures scale-invariant regularization across different input resolutions.
C ANALYSIS ON CRD/IFA COMPUTATIONAL COST DURING TRAINING/INFERENCE
IVPT introduces a moderate computational overhead compared to vanilla Visual Prompt Tuning (VPT), primarily due to the incorporation of its Concept Region Discovery (CRD) and Intra-region Feature Aggregation (IFA) modules. When implemented in PyTorch and executed RTX 3090 GPU, the attention mechanism and additional loss computations within CRD result in approximate 4.8% increase in training time and 5.2% rise in per-image inference latency. This overhead is largely attributable to the cross-layer prototype similarity calculations, whose complexity scales with the feature dimensions.
NVIDIA
Notably, the introduced modules are designed to be lightweight, contributing only a minimal number of tunable parameters the overall model. The breakdown of parameters is follows:
CRD Tunable Parameters:
Concept Prototypes: 50 in total distributed across layers (i.c., 17 + 14 11 8)
Each prototype is 768-dimensions vector
Total: 50 x 768 38, 400 parameters
Spatial Bias Maps: concept-specific maps at a resolution 37 x 37
+
Total: 50 x 37 x 37 = 68, 450 parameters
Total CRD Parameters: 106.850
IFA: No tunable parameters (utilizes fixed feature averaging)
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For context, the full Vision Transformer- Base (ViT-B) backbone contains approximately 86 million trainable parameters (based on the standard Dino'22 ViT-B configuration). The additional parameters introduced by CRD and IFA account for only about 12% of the total parameters required for full ViT fine-tuning. Thus, IVPT achieves significant gains in interpretability while maintaining minimal computational overhead.
D EXTRA ABLATIONS
Ablation on the hierarchical structure of prototypes. We investigate how the hierarchical pro- totype structure strategy affects the performance, as shown in Table 4 We use D2no as the backbone (the same below). When using only one layer, the model exhibits poor interpretability and accuracy. Increasing the number of layers improves performance across all metrics, with four layers achieving optimal results, suggesting enhanced inter-layer relationships that improve interpretability. However, applying explainable prompt learning to more layers introduces semantic confusion and reduces performance, due to fewer explainable concepts in feature maps from shallower layers. Notably, reducing the number of prototypes layer by layer enhances interpretability by capturing concepts at varying granularities, facilitating dynamic prompt modeling.
Table 4: Ablation on the number prompted layers and the number of prototypes at each layer. Bolded items in each list indicate layers used for interpretability evaluation, each with 10 prototypes. After the final layer, we consistently use 4 prototypes to generate coarse graieeegredd part features for the final classification.
Layers
Prototype Number
Con.
Sta.
Acc.
[10]
62.5
65.2
89.5
2
[10, 7]
70.7
71.8
89.8
2
[10, 10]
67.2
72.6
89.6
3
[13, 10, 7]
73.7
73.0
90.3
3
[10. 10, 10]
74.4
70.7
90.5
4
[16, 13, 10, 7]
75.3
75.9
90.8
4
[10, 10, 10, 10]
75.6
71.2
90.5
5
[19, 16, 13, 10, 7]
66.6
68.1
90.1
5
[10, 10, 10, 10, 10]
64.3
67.4
89.7
Analysis cross-layer concept alignment. To rigorously quantify cross-layer concept alignment, we propose a new experiment measuring the Intersection over Union (IoU) between high-level concept regions and aggregated low-level concept regions. Extensive experiments the CUB-200-201 validation set reveal exceptional alignment, as shown in Table
Table Intersection over Union (IoU) measurements between different layer combinations
Layers
IoU
Layer (finest granularity) + Layer 4 (coarsest)
98.7% + 0.8
Layer Layer 4
98.9% 0.6
Layer - Layer 4
99.2% + 0.4
Overall mean
99.0% 0.6
These results demonstrate that cross-layer fusion mechanism (Eq. 8-9) achieves near-perfect spatial consistency (IoU >98% universally), validating that fine-gras ine-grained concepts are cohesively grouped under unified high-level semantics, and that the cross-layer prompt fusion with concept region consistency loss work well.
E HUMAN EVALUATION METHODOLOGY AND RESULTS
We conduct a structured three-stage evaluation with 20 participants to assess IVPT's interpretability. In the concept annotation phase, participants freely describe semantic meanings for 100 prototype-
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Importance Different Concepts by
Grade 3
0.7
Gilennilarr
Glanculla Veskle
0.0
Teeii
0.5
Grade
0.4
0.2
Grade 5
0.1
0.0
Grade
Grade 4
Grade
Calegory
Figure 7: Illustration of importance scores of different concepts by grade the Gleason- 2019 dataset.
activated regions from CUB-200-2011 images, achieving 97.5% overall accuracy in matching human descriptions to prototype intentions.
For hierarchical validation, participants evaluate cross-layer transitions using 5-point Likert scales (1 strongly disagree, = strongly agree), showing strong consensus across three critical aspects:
Detail Preservation (mean 4.7): Participants confirm IVPT's ability to retain fine-grained visual details during abstraction from shallow to deep layers. Example: When identifying "Cactus Wren", shallow-layer prototypes capture individual spine-like breast feathers, while deep-layer prototypes preserve these textural details within broader anatomical concepts.
Semantic Abstraction (mean 4.8): Users agree higher layers effectively capture meaningful macro-concepts without distorting original semantics. Example: For a "Black-footed Albatross", shallow prototypes detect detailed "hooked beak tip" features that naturally abstract to coherent "head" concepts in deeper layers.
Transition Naturalness (mean 4.8): The hierarchical progression demonstrates intuitive logical coherence aligned with human reasoning patterns. Example: Observing "Red-winged Blackbird". users note seamless progression from wing edge curvature (Layer 1) epaulet shape recognition (Layer 2), mirroring expert birding observation patterns.
The study demonstrates IVPT's effectiveness in bridging the gap between machine representations and human- -nttrrritterreetable visual concepts through its cross-layer prototype architecture.
F FURTHER ANALYSIS ON THE GLEASON-200 2019 DATASET
Figure iillustrates the importance scores of different concepts (Glandular Lumen, Glandular Vesicle, Tissue) across grades in the Gleason-2019 dataset. Glandular Vesicle shows a steady increase in importance from Grade 3 to Grade 5, reaching around 0.6, which aligns with the fact that variations in glandular vesicles are primary indicator of prostate cancer. As the malignancy level increases, its importance grows significantly. In contrast, Glandular Lumen remains consistently low, which is reasonable given its lack of direct relevance to cancer. Meanwhile, Tissue shows a high importance at Grade 3 but decreases significantly by Grade 5, likely due to the differentiation of tissue in highly malignant samples. This pattern suggests that different concepts contribute variably to feature extraction malignancy progresses.
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Original Images
VPT-Deep
VPT-Deep
(with Prototypes)
Figure Qualitative results of explainable region maps generated by various approaches. We highlight the areas that significantly distinguish IVPT from other methods with red circles.
G MORE VISUALIZATION
G.1 QUALITATIVE RESULTS OF EXPLAINABLE REGION MAPS.
We present qualitative results of explainable region maps generated by different approaches using D2-B as the backbone (the same below) in Figure g IVPT outperforms other methods by producing more comprehensive and interpretable maps. Unlike the other two methods with prototype learning, vanilla VPT-Deep struggles to identify explainable regions for the learnable prompts. In comparison to VPT-Deep with prototypes, the red-circled areas in the IVPT row highlight additional essential details, such finer anatomical features of birds, demonstrating its ability to focus on relevant features for clearer explanations. However, we also notice that in complex scenes with occlusions or dense backgrounds (e.g.. CUB images where birds overlap with branches), IVPT prototypes occasionally misalign due to visual ambiguity. For example, we often found that prototype designed to capture "bird legs" activated on textured branch mimicking leg scales, incorrectly assigning high importance to background clutter.
G.2 GENERALIZATION ANALYSIS OF IVPT.
To further assess the generalization of our method beyond fine-grained recognition, we evaluate IVPT on broader image classification benchmarks including CIFAR-100 and ImageNet. As illustrated in Figure 9 (left), IVPT consistently identifies semantically meaningful object parts even these more generic settings, demonstrating that its part-discovery capability not restricted to fine-grained domains.
We also explore whether the learned concept prototypes identify semantically meaningful parts when generalizing on unseen categories (class domain) from the same dataset. As illustrated in (right))righht, when applied novel categories not encountered during training, IVPT successfully localizes coherent object regions-Suun as bird heads or wings in fine-grained recognition-witlee any task-specific retraining. This indicates that the discovered prototypes capture transferable visual concepts that are consistent across category boundaries, rather than overfitting to specific training classes. The results confirm that IVPT exhibits robust categoy concept generalization, reinforcing the stability and semantic relevance of its learned prototypes.
To investigate the influence of domain shift on the concepts discovered by IVPT, we conduct comparative visualization study using the VLCS (Fang et al.) (2013) benchmark. We fine-tune IVPT separately on two Comains-Calteds and PASCAL-using a pre-trained ViT backbone, and visualize the top activated concepts for the "dog" and "person"" classes. As shown in Figure I0, these visual comparisons illustrate that IVPT dynamically adapts its concept priorities according to the domain, confirming that the method does not assume fixed concept set but rather learns domain-relevan interpretations. This behavior makes IVPT suitable for deployment environments where domain shifts are expected.
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CIFAR-100
CUB-200-2011 (Novel Categories)
ImageNet
Figure Qualitative results of IVPT on general image recognition datasets (left) and on novel categories from the fine-graines CUB-200-2011 dataset (right).
Caltech
PASCAL
Figure 10: Concept activation visualizations for IVPT models fine-tuned separately the Caltech and PASCAL domains of the VLCS benchmark. The comparison highlighted regions shows how the model prioritizes different semantic concepts when the domain shifts.
G.3 BAD CASE ANALYSIS.
Figure [ presents representative failure cases our IVPT model. The analysis of these cases reveals that the model can learn spurious correlations, such as a "bird" prototype activating surrounding branches or "shark" concept triggered by water texture. Crucially, this very interpretability not only makes these biases transparent and diagnosable but also directly enables practical model debugging and bias detection. By visually pinpointing and diagnosing such learned biases-where the model relies context rather than object features, IVPT allows researchers to move from observation to iitiionfacilitatinng targeted mitigation through data refinement or prototype pruning, thereby solidifying its role in building more robust and trustworthy models.
G.4 EXPLAINABLE REGION MAPS WITH DIFFERENT NUMBER CONCEPT PROTOTYPES.
This section presents additional visualizations to highlight the explainability of region maps with different numbers of concept prototypes. Specifically, Figure shows explainable region maps with varying concept prototypes. As the number of concept prototypes decreases, the region maps simplify, grouping larger areas into fewer explainable regions. These visualizations demonstrate how reducing the number of concept prototypes affects the granularity of the explainable regions, with higher values
Figure 11: Analysis of IVPT failure cases.
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providing finer, more detailed part regions across the bird images. This enables analysis of how concept-prototype based explanations adapt to different prototype settings, illustrating the trade-off between granularity and interpretability.
m=13
m=7
m=16
m=13
m=10
m=7
m=4
Figure 12: Visualization of explainable region maps with 16, 13, 10, 7 and 4 concept prototypes.
G.5 EXPLAINABLE REGION MAPS OF THE HIERARCHICAL STRUCTURE USING DIFFERENT BACKBONES.
This section provides the visualization explainable region maps generated from different backbones with the hierarchical structure, illustrated in Figure 13 The used backbones include DeÏT-S, DeïT- B, V2-S. D2noV2-B. and Dino Each row displays how each model processes the image of bird, capturing distinct regions with different color-coded parts, each corresponding to concept prototype. The visualization reveals variations part regions depending on the backbone architecture, with some models offering finer ones than others. These differences highlight the impact of the
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backbone choice the interpretability and granularity of the explainable regions, emphasizing how different architectures contribute uniquely to the generation of part-level explanations in the hierarchical structure. This comparative visualization demonstrates the flexibility and adaptability of the explainable region maps across varying backbones.
DeiT-S
DeiT-B
DinoV2-S
DinoV2-B
DinoV2-L
Figure 13: Visualization of explainable region maps of the hierarchical structure using different backbones, including DeiT-S, DeiT-B, V2-S, V2-B. and V2-L.
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Reviews:
[{"review_id": "c8lVS6WckX", "reviewer": "Reviewer_9y8U", "summary": "This paper explores the interpretability of vision–language models (VLMs) through a task-specific concept alignment framework. The authors aim to understand how visual and linguistic concepts align across different tasks, providing both qualitative and quantitative analyses to uncover internal reasoning processes of VLMs.", "questions": "Can you expand the related work section to more deeply analyze existing SOTA interpretability methods and highlight the specific gap your work addresses?\n\nHave you considered evaluating your approach in a concrete application setting (e.g., identifying bias, failure analysis, or improving model transparency for users)?\n\nHow generalizable is your approach across different VLM architectures? Are there differences in alignment quality depending on the model type?\n\nCould you include an example of how concept alignment results might be used in a downstream interpretability workflow or decision-support scenario to make the method’s impact more tangible?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The topic is highly relevant and well-motivated, addressing the growing need to make large-scale vision–language models more interpretable and transparent.\n\nThe paper provides comprehensive experimental analysis, including multiple datasets and evaluation settings, with clear presentation of alignment patterns.", "weaknesses": "While the analysis is useful, the methodological contribution is limited compared to prior interpretability frameworks. The paper primarily extends known alignment techniques rather than introducing a fundamentally new interpretability paradigm. The related work section lists many recent studies but lacks an in-depth synthesis or a critical comparison. A more detailed discussion of existing SOTA interpretability methods and their limitations would help clarify what specific gap this work fills. Because the related work discussion is broad but not deep, the method section does not clearly delineate the paper’s unique conceptual or technical contribution relative to existing approaches.\n\nWhile the experiments are well executed, the paper would benefit from showing a potential use case, for example, how this concept alignment framework could assist in model debugging, bias detection, or downstream task understanding.", "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": "DpwkJ8f024", "reviewer": "Reviewer_ANRv", "summary": "This paper proposes IVPT, an interpretable visual prompt tuning framework that aligns prompts with category-agnostic concept prototypes across network layers. It introduces concept-region discovery, intra-region feature aggregation, and cross-layer concept fusion to make prompts human-understandable while preserving accuracy. Experiments show IVPT achieves better interpretability and comparable performance to standard VPT.", "questions": "How stable are the learned concept prototypes when transferring to new domains or unseen categories?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It introduces a clear, concept-grounded approach that links visual prompts to human-understandable concepts across layers.\n2. It demonstrates improved interpretability metrics and visualization quality without sacrificing classification accuracy.\n3. The method is model-agnostic and works across different ViT backbones and domains, showing good generalization.", "weaknesses": "1. Experiments focus mainly on fine-grained classification. Broader tasks (e.g., detection, segmentation) are not explored.\n2. The method relies on well-learned concept prototypes, which may be sensitive to initialization or domain shift.\n3. The multi-layer prototype alignment and multiple loss terms increase implementation and computational complexity.", "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": "ehmxXZGRvK", "reviewer": "Reviewer_PdEH", "summary": "This work aims to propose a fine-tuning paradigm that learns interpretable visual prompt during fine-tuning via cross-layer concept prototypes. A concept region discovery module is proposed to learn prototypes with semantic meaning, and an intro-region feature aggregation module is proposed to group the features belonging to certain regions. Experimental results in CUB demonstrate improved performance compared to visual prompt tuning and show superior interpretability scores for the learned visual prompts compared to part-prototype networks.", "questions": "1.\tHow do you obtain the performances of ProtopNet and its following works based on ViT architectures in Table 1? How are they implemented? These original works are not designed for ViT and do not report relevant results.\n\n2.\tCan the learned visual prompts explain anything related to visual prompt **tuning**?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- This work investigates the interpretability of visual prompts during fine-tuning, which is a less explored area.\n- The method design in making the visual prompts more interpretable is reasonable.\n- Experimental results show improved performance and interpretability compared to 2 types of baselines.", "weaknesses": "*Motivation*\n\nWhy at all should the “visual prompt” be interpretable is not well justified. Since they are parameters learned to adapt the model from one domain to another domain, if they are possible to be interpretable, I would expect them to explain the domain shift instead of part-prototypes. Part-prototypes could explain a decision making process, but are not that meaningful in a fine-tuning process? Especially when the sum of the contribution of part-prototypes do not fully explain the decision making process in the proposed framework (see next concern).\n\n\n*Interpretability*\n\nThe final classification process in part-prototype networks can be fully interpreted by the contribution of each individual part-prototype. However, the prompts in this work only contribute to part of the classification logits, most tokens from frozen part of the network remain uninterpretable. Do the framework adopt a classification token in the final prediction or the average of all token representations? If using a classification token, the information are aggregated from both interpretable visual prompt tokens and rest tokens via self-attention, making the contribution of the visual prompt unclear. Such a mechanism also makes it less meaningful to make visual prompts interpretable.\n\n*Evaluation*\n\nHow are the areas that the evaluated visual prompts correspond to calculated and evaluated against the annotations in CUB?\n\n*Missing details/analysis*\n\nWhat’s the difference between the interpretability scores of prompts in different layers? How many prompts are used in each layer? What’s the influence of number of prompts on their interpretability?", "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": "hxXxLgNwWU", "reviewer": "Reviewer_RFsS", "summary": "This work proposes a novel, interpretable approach to visual prompt tuning. Here, interpretable tokens are appended to each ViT layer's computation, thereby extracting relevant information while keeping the image encoder frozen. The interpretability comes from the fact that the learnable tokens are not just embeddings, but they can be interpreted as localized prototypical parts, which give insights into which parts of the image are being focused on for each prompt token.", "questions": "* Can the authors provide predictive performance compared to baselines on other datasets to counter concern (i)?\n\n* Can the authors provide qualitative results for PartImageNet and PASCAL-Part to counter concern (ii)?\n\n* If I understand correctly, previously in VPT, the prompt tokens were learnable embeddings that were the same for different samples. Now, the prompt tokens are weighted average of the input image, thereby different per-image. How does that differ conceptually from the previous global prompt tokens? What are the benefits and potential downsides?\n\n* The concept regions are computed patch-wise. How did the authors obtain non-rectangular prompt visualizations, such as in Figure 3?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "This work manages to nicely integrate interpretability methodology into visual prompt tuning(VPT). Although related to prototype-based literature, it is non-trivial how to integrate it into VPT. The method is predominantly presented in a clear manner. Figures support the textual understanding.\nResults look promising in the aspects accuracy as well as interpretability, showcasing that the interpretability framework does not bottleneck the predictive performance on CUB. Also, the results cover various aspects of interpretability, ranging from quantitative metrics to visualizations and human user studies. I appreciate that clean code is provided.", "weaknesses": "* (i) My main concern is that each across datasets, the axes of evaluations are not the same. That is, Accuracy is only shown for CUB, and visualizations are missing for PartImageNet and PASCAL-Part. This gives the impression that for the left-out evaluations, the results were omitted due to being poor. This is concerning for the performance aspect, as CUB is a quite simple dataset and if this method is unable to generalize to other more complex datasets, it is problematic. Note that I would expect a performance degradation due to the interpretability adjustments, so the goal for IVPT should just be to not lose out too much. In the current state of the manuscript, I have to assume that IVPT is unable to reach a performance similar to baselines in all datasets apart from CUB.\n\n* (ii) An additional major concern is the faithful interpretability for more complex datasets. For CUB, Gleason-2019, Stanford Cars, FGVCAircraft datasets, the subparts are similar across samples, thereby making them interpretable. Additionally, the parts are also what is important for classification. However, I could envision that for more complex datasets (e.g. ImageNet and Pascal), the prototypes are becoming less interpretable, as they might be used to process information in ways that are not directly understandable when looking at the activation map. That is, without explicitly enforcing it, the visualization in image-space might not be anymore what is going on in the computations. \n\n* In line with the previous point, there might be considerable leakage, as the image regions are processed via weighted average and MLP such that the actual attention computations could deviate greatly from what is visualized. \n\n* I think the separation of $n$ learnable prompts and $m$ concept prototypes is sometimes unclear. E.g. line 128-129 and formula 3 use similar notation but different lengths, which was confusing at first.", "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)