Intermediate Domain Alignment and Morphology Analogy for Patent-Product Image Retrieval
Authors: Haifan Gong
Xuanye Zhang
Ruifei Zhang
Yun Su
Zhuo Li
Yuhao Du
Anningzhe Gao
Xiang Wan
Haofeng Li
Venue: NeurIPS 2025 poster ()
Keywords: open-set patent-product image retrieval
domain align
morphology analogy
Primary area: social_and_economic_aspects_of_machine_learning

Abstract: Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Product Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive Patent-Product Image Retrieval Dataset (PPIRD) including a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set of 3,799,695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter to select discriminative patent images based on visual features via analogical reasoning. Extensive experiments on PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR. (The PPIRD dataset is available at: \href{https://loslorien.github.io/idama-project/}{https://loslorien.github.io/idama-project/})

Intermediate Domain Alignment and Morphology Analogy for Patent-Product Image Retrieval
Haifan Gongl2t, Xuanye Zhangl2t, Ruifei Zhang Yun Su Zhuo Li
Yuhao Du Anningzhe Gao?, Xiang Wan 1.2- Haofeng Li4
TTe Chinese University of Hong Kong, Shenzhen,
Shenzhen Research Institution Big Data,
University of Waterloo, Schooo of Systems Science and Engineering, Sun Yat-sen University
Abstract
Recent advances in artificial intelligence have significantly impacted image retrieval tasks, yet Patent-Produch Image Retrieval (PPIR) has received limited attention. PPIR, which retrieves patent images based on product images to identify potential infringements, presents unique challenges: (1) both product and patent images often contain numerous categories of artificial objects, but models pre-trained on standard datasets exhibit limited discriminative power to recognize some of those unseen objects; and (2) the significant domain gap between binary patent line drawings and colorful RGB product images further complicates similarity comparisons for product-patent pairs. To address these challenges, we formulate it as an open-set image retrieval task and introduce a comprehensive product-patent 727.921 patents, and an unlabeled pre-training set of 3,799.695 images. We further propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy. IDAMA maps both image types to an intermediate sketch domain using edge detection to minimize the domain discrepancy, and employs a Morphology Analogy Filter select discriminative patent images based on visual features via analogical reasoning. Extensive experiments PPIRD demonstrate that IDAMA significantly outperforms baseline methods (+7.58 mAR) and offers valuable insights into domain mapping and representation learning for PPIR. (The PPIRD dataset is available at: https://lolloriengiibbb...c ioddaai
Image Retrieval Dataset (PPIRD) including a test set with 439 product- patent pairs, retrieval pool
Introduction
Patent systems play critical role in protecting intellectual property and fostering innovation. The rapid advancement artificial intelligence (AI) has revolutionized numerous domains, including patent retrieval D 2
both patent-patent retrieval Specifically, in the NLP area, significant progress has been made in and patent-product retrieval [3] 4, leveraging textual descriptions to identify similarities and potential infringements. In contrast, the CV community has primarily focused on patent-patent retrieval (5), leaving Patent-Produce Image Retrieval (PPIR) unexplored. Considering the increasing complexity of c-commerce platforms (a product may have incomplete, confusing name or descriptions, but its images are always clear) and the critical need to detect patent infringements efficiently, there is an urgent demand for research on PPIR.
3
! 5
PPIR faces two primary challenges: 1) product and patent images often contain numerous categories (more than 250,000) of artificial objects, such as mechanical parts or electronic devices, but most
CCoreeponning Email: wanxiang@srihd.ccm lihf95 @hf95@mmall www.aaalailllaa.....mm these authors contribute equally to
this work.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure I: Characteristics of Patent-Product Image Retrieval (PPIR) task: (Upper: Product Im- ages, Lower: Patent Images) 1) PPIR task has large-scale artificial categories, which are significantly different from those in general vision datasets: 2) Patent binary line-drawing, images and Product colorful RGB images have huge domain gap, casting negative effect for PPIR tasks.
of these categories are distinct from those in general pre-training datasets like ImageNet [6]. 2) There a significant domain gap between patent images (typically binary line drawings) and product images (typically colorful RGB images). This domain discrepancy further exacerbates the difficulty of accurate retrieval. (See Fig. I for details.)
Due to the unique characteristics of PPIR, existing general image retrieval methods 7 exhibit suboptimal performance for two reasons: 1) General image retrieval approaches often struggle to discriminate large number of unseen artificial-category objects, which makes challenging to grasp distinctive visual features of patents/products for accurate retrieval; 2) The domain gap causes distribution shift for specific categories, which further aggravate the retrieval performance, especially for some similar artificial objects.
We formulate PPIR as an open-set image retrieval task to simulate real-world scenarios. Moreover, to address the lack of suitable data for PPIR, we introduce the Patent-Produs Image Retrieval Dataset (PPIRD), large-scale dataset designed to facilitate research in this area. PPIRD comprises two components: (1) testing set containing 439 product-patent pairs alongside a retrieval pool of 727,921 patent images and (2) an unlabeled pre-training set with 3,799.69 product/patent images. We offer highly detailed product descriptions for each product and the corresponding potential patent infringement pair to verify potential patent-patent infringement. (The extremely detailed information required and the difficulties of ascertaining potential patent-patent infringement both limit the scale of the infringed product-patent pair.
Furthermore, propose extremely simple but effective Intermediate Domain Alignment and Mor- phology Analogy (IDAMA) strategy for PPIR. IDAMA contains an Intermediate Domain Mapping (IDM) strategy and a Morphology Analogy Filter (MAF) strategy: 1) IDM aligns binary line drawing patent images and colorful RGB product images by mapping them into an intermediate sketch domain using edge detector. We provide theoretical analysis prove that this alignment effectively mitigates the domain discrepancy, enabling more accurate retrieval. 2) MAF leverages a cognitive principle of morphology analogy. An unknown object be described by analogy by using high classification confidence (regardless of the label) to select a discriminative image for artificial patent images for retrieval similarity comparison. By selecting discriminative images for unseen artificial patents, MAF can grasp distinctive visual features of the patents for PPIR.
known
Experiments PPIRD demonstrate that IDAMA achieves significant improvements over base- line methods (+7.58 mAR), proving the effectiveness of IDM and MAF addressing the unique challenges of PPIR. Furthermore, systematically compare various domain alignment and represen- tation learning methods on PPIRD, providing insights into their strengths and limitations coping with unseen artificial objects in cross-domain scenarios. These findings offer valuable guidance for future research in PPIR and related areas.
In summary, this work makes three key contributions:

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1. We formulate PPIR as an open-set image retrieval task and introduce PPIRD, a large-scale dataset for Patent- Patent-Product Image Retrieval:
2. We propose IDAMA, a novel strategy that combines intermediate domain alignment and morphology analogy address challenges of PPIR; We further provide theoretical analysis to demonstrate the advances of intermediate domain alignment via compressive sensing.
3. We comprehensively evaluate domain alignment and representation learning methods, pro- viding insights for efficient cross poos--domain learning.
2 Related Works
2.1 Image-based Patent/Sketch Datasets and Benchmarks
The availability of large-scale, well-structured datasets is essential for advancing Al research in patent analysis. [8] presented PatentMatch, dataset specifically created for matching patent claims with prior art, facilitating studies in patent infringement detection and novelty assessment. For image- focused research, [5] developed DeepPatent, large-scale benchmarking corpus aimed at patent drawing recognition and retrieval. [9] extended this work with DeepPatent2, focusing on technical drawing understanding. [IO] introduced the Harvard USPTO Patent Dataset, comprehensive corpus of patent applications designed support diverse Al research tasks. However, the research on image-based patent retrieval mainly focuses on image retrieval between patents and solving patent infringement problems. Product infringement detection and corresponding eeenttproducc image retrieval (PPIR) are under-explored. This paper introduces a large-scale patent-product image retrieval dataset to satisfy the requirements.
On the other hand, Im4Sketch [I]] proposes a large-scale natural RGB/sketch image retrieval dataset together with a method address the RGB-sketch retrieval challenges. However, the images of Im4Sketch dataset are collected from ImageNet- 1K ( and adopt the closed-set image retrieval formulation, which exists two main differences from PPIR task: 1) PPIR contains large-scale artificial categories that do not exist in ImageNet-IK. 2) The huge amounts of artificial categories and the continuously appearing products make PPIR not suitable to be formulated as closed-set image retrieval task.
2.2 Patent Retrieval in NLP areas
The analysis and retrieval of patents have been pivotal research areas in the application of artificial intelligence (AI) across multiple domains, including classification and retrieval [2. Accurate patent classification is critical for efficiently organizing and accessing patent information. Several studies have explored Al-driven methods to automate patent classification, with notable contributions from 14 15 [6 17). These works investigate using machine learning models to handle the complex- ity of patent data. For example, [88 examined the potential of Al in solving the patent classification problem, analyzing various machine learning models and their effectivvness in handling the diverse and intricate nature patent data.
In patent retrieval, AI techniques have been widely used to enhance the accuracy and efficiency of retrieving relevant patents. Traditional text-based methods have seen significant advancements by applying deep learning models. [ proposed an advanced patent prior art search method using deep learning, which relies on semantic embeddings to capture the contextual meaning of patent documents, offering improvements over keyword-basee retrieval systems. Similarly, [19] integrated text embeddings with knowledge graph embeddings to further refine patent retrieval, highlighting the importance of external knowledge sources in improving search performance. More recently, for patent retrieval, demonstrating
20 explored the use of large language models (LLMs) 21 22] their potential to improve retrieval accuracy. To satisfy the real-world requirements of product infringement detection, 23 [4| 15 16
17 [0] propose and explore patent-produce retrieval. Patent- product retrieval is formulated an unsupervised retrieval task to satisfy the continuously emerging new products.
In summary, the formulation of patent retrieval in NLP areas has developed from classification or supervised retrieval to open-set unsupervised retrieval using a retrieval pool. Considering that open-set unsupervised retrieval from retrieval pool is closer to real-world scenarios, which can
3

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Alignment Morphology Analogy Strategy
Filtor
043
0.72
Potent Image
Intermedia Domain Mapping
Morphology Analogy
Shetch
Mlspoer
F: Filber
Clasatier
Product image
Mapping
Footure
Product
Similarity Comparison
Simelenity Soore
Extractor
Feeture
Figure 2: Pipeline of IDAMA: IDAMA consists of Intermediate Domain Mapping and Morphology Analogy Filter methods. The pipeline of IDAMA is follows: Using IDM to align product/patent images by mapping them into intermediate sketch domains; 2) Using MAF to filter discriminative mapped patent images; 3) Comparing product-patent similarity by obtaining product-paten similarity score from product-patent similarity matrix between filtered patent images and mapped product images for infringement detection.
address the issue of conducting infringement detection for continuously emerging products, the proposed PPIR tasks also adopt such formulations to satisfy real-world requirements.
2.3 Patent Retrieval with Images
Patent images often include technical drawings and diagrams, and image retrieval in this domain has emerged as an important area of study. [3] focused on improving the quality of convolutional neural network (CNN) [24 training datasets for patent image retrieval, emphasizing the importance of dataset quality in model performance. Building on this, 4 introduced methods using cross- entropy-based metric learning to enhance the accuracy of patent image retrieval systems. In another study, proposed transformer-ban effectiveness of transformer architectures in this domain. Some works achieve pure patent retrieval by transferring the binary line-draw patent images (25 26 27
deep metric learning for patent image retrieval, showcasing
into sketches to enhance performance.
However, the huge domain gap between patent binary line-drawing images and product colorful RGB images makes it extremely challenging for unsupervised open-set PPIR compared to patent retrieval tasks. Althought there are some works that aims to address the domain gap [28 29
30
31
32).
especially cross-domain image retrieval [33 34
However, these methods usually focus on close- set with options rather than the challenging open- open-world setting. Therefore, this paper proposes domain mapping methods to bridge domain gaps between patent and product through an intermediate domain to alleviate the challenges.
Methodology
The Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy for PPIR consists of Intermediate Domain Mapping (IDM) (Sec.
3.J) 3.J ) and Morphology Analogy Filter (MAF) (Sec. 3.2) We also prove brief for IDM's effectiveness (Sec. 3.3 The pipeline of IDAMA is demonstrated in Fig. 2 1) Using IDM to align product/patent images by mapping them into intermediate sketch domains; 2) Using MAF filter discriminative mapped patent images; 3) Comparing product-paten similarity by obtaining product-pates similarity score from product-pated similarity matrix between filtered patent images and mapped product images for infringement detection.
3.1 Intermediate Domain Mapping
The PPIR faces primary challenge: the domain gap 35 Specifically, product images are RGB images containing rich color, texture, and other information 38] while patent images are binary line drawings that abstractly represent products (39 40). Resolving the domain gaps between product and patent images thus becomes our primary task.
37
between product and patent images.

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To address the challenge, we propose the Intermediate Domain Mapping (IDM) (See Fig. 2 for details) to align binary drawings, patent images, and colorful RGB product images by mapping both of them to the intermediate sketch domain.
Specifically, denote sketch mapper as Msketch( (+) which map both product images I, and patent images Ip to the intermediate sketch domain. In the PPIR process, IDM for PPIR first maps raw product images I, and patent images In to the intermediate sketch domain and obtains mapped sketch images I and I.p. which can be expressed as:
r = Makesch (Tr); I = Makeach (1p)-
(2)
With the above-defined sketch-based intermediate domain mapping method, we can mitigate the domain gap between patents and products. Analysis in Fig. further confirms the effectiveness of our process.
3.2 Morphology Analogy Filter
PPIRD needs to handle a large number of unseen man-made objects. Because general pretraining datasets contain limited man-made categories, extracting features with backbones pre-trained on these datasets is less discriminative for efficient product-patent retrieval.
To overcome the challenge, we observe how human beings grasp the distinct visual feature of an unseen object: human beings analogize the morphological feature of the unseen object to some familiar objects [42]. By doing this, even those who do not see the object can recognize the object through the morphology analogy. Inspired by this cognitive principle, we propose Morphology Analogy Filter (MAF) for filtering patent images before mapping to the edge domain.
Specifically, given Filter (a classification network) and mapped patent images Ip. MAF first obtain the classification vector Cp by
Cp = F(Tp)
Then, MAF obtains discriminative patents Tpd image using the max classification scores regardless of the label itself (class-agnostic scores), which can expressed as:
Ip arguuerrearrgaaz(e > Tmaf)
where cp, is the classification score in Cp and Tmaf is the MAF threshold. all the image classification scores are less than Tmaf, preserve the image of max Cp, for the patent.
We only conduct MAF for patent images as follows: The motivation of PPIR discover whether a product infringes any patent. Each image contains valuable visual features. It'd be better to follow the 'Better have more than to miss out' philosophy for products. Because morphology similarity may indicate potential infringement for patent images, it'd be better to follow the rule 'Better to have less than to have something of poor quality' to select distance visual features patents to avoid negative influence. Therefore, we only conduct MAF for patent images.
Similarity Comparison: Then, a feature extractor E(+) are adopted extract feature f, and fp from I, and Lpd:
f (4+
(5)
f = E
(6)
After obtaining the intermediate sketch-domain feature f/ and f/, PPIRD calculates the cosine similarity score for them to conduct PPIR:
s = sim f()
where sim(.) denotes the cosine similarity function.
For PPIR tasks, each patent p and product may contain more than one image, which can be expressed as = (Tp, Ip2,...) and p = (1r1, I.2..... IDM uses a maximum of image-level similarity scores (formulating product-pon ponduct-patent similarity matrix) in the intermediate domain as product-patent similarity score S
Spr =marss 11122 .... 22..)
(8)
5

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IDM for PPIR compares each product with all the patents in the retrieval pools, denoted as P Pa....... obtains their similarity score set, denoted as S, {spl,r sp,, and then sort S, for patent-product retrieval.
3.3 Understanding Intermediate Domain Mapping via Compressive Sensing
Preliminaries. Let IER" denote vectorised image that can be decomposed
-=
where encodes the geometrical structure (edges, contours) while the term contains texture, colour and background clutter. The mapper M used in intermediate domain mapping (IDM) has linear front-end A E <m m < n. whose entries are i..d. NO, 1 1/m) random variables. Such matrix acts Johnson-Lindensttren
embedding and, with overwhelming probability, satisfies the
Restricted Isometry Property (RIP) required by compressed-sensiine theory 43
45 46 471.
Model assumptions. 1) is k-sparse known orthonormal basis v; 2) the embedding dimension obeys m 2 Ck (og(n/k) for universal constant C > 0; 3) the backbone network E is L-Lipschitz, i.e. [E(x)) E(y)lle I L|I yll2 for all x, y (see, e.g., [48 49)).
Definition 1 (Restricted Isometry Property (50) A matrix A satisfies RIP(2k, 6) if. for every 2k-sparse vector x E R",
(1 - 5) 11=1| S ||r1 S (1 (1+
(10)
Claim (Distance preservation). Consider patent-product pair (Lps (pr) whose structural compo- nents Sp, Spr satisfy || Spr 2 < E. If A fulfils RIP 8), then
AA Apr S 5)e.
(11)
Theorem (IDM feature-space contraction). Under Assumptions 1-3 and Definition 7 the feature distance of the mapped images satisfies
2 - Eprl12 1l2 S L((1+ + 8) + = (l1zlll + l1)p)
(12)
where 2, = =A(I)) and ăpr - E(Alp))
Remark 3. Because m < n, the nuisance term is suppressed by the factor m/n, whereas the structural term is almost isometrically preserved by RIP. Consequently, with high probability, l| Zprlle l2, which explains the empirical robusiness of Intermediate Domain Mapping.
Epell2 < ||pp
The proofs of Claim 2 and Theorem T are deferred to Appendix
4
Experiments
4.1 PPIRD: Patent-Product Image Retrieval Dataset
Retrieving patents for infringing products is complex and time-consuming heavily relying on patent experts to search massive patent databases to identify potential infringing patents [13.. This reliance makes it challenging obtain large-scale paired dataset of product-paten. images train models in a supervised manner. To address the challenge, we proposes PPIRD, consisting of PPIRD-testing and PPIRID-unlabeled and formulates it as retrieval task. To the best of knowledge, we provide the first benchmark for image-based patent-product retrieval at near million data level.
PPIRD-testing set: The PPIRD-testing set contains 439 product-patent pairs and retrieval pool of 727.921 patent images. Experts annotate and validate the product-patcos infringement pairs (Expert can refer the detailed descriptions of 439 products for more accurate validation). For the infringed patents, we use Google Patents to search 9,999 patents for each product (using their name keyword) and download their patent images formulate retrieval pool. We formulate PPIRD-testing as product-patent simulate real-world scenarios better.
retrieval rather than image-level retrieval like DeepPatent S
The formulation of PPIRD-testing is as follows: the product dataset D, and the patent dataset be Dp. For product z, from the product dataset Dr, aim to find its corresponding patent yj in

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Table I: Quatitative Results of IDAMA: 1) IDAMA can bring significant performance enhancement compared with baseline methods, and both IDM and MAF can contribute to the improvement. 2) Comparison between DeepPatent [3] and MAF ('DMM-DeepPaten'' and 'IDAMA') proves the intuitive idea of MAF can bring more performance enhancement even without extra pre-training. 3) Comparison between UCDIR [33 and UCDIR+IDM/IDAMA also demonstrates that method can consistently boosts the performance. 4) Contrastive Learning ("IBOT") may be more suitable for PPIR intermediate sketch domain.
Method
Backbone
Pre-train
mAR
R@ 100
R @ 500
R@ 1000
R @ R@20000
Product-Patent
ResNet-18
Supervised
13.50
1.59
11.62
14.12
26.65
IDM
ResNet-18
Supervised
21.70
3.42
14.12
26.20
43.05
IDM+DeepPatent
R8
Supervised
21.98
3.19
13.90
25.74
45.10
IDAMA
ResNet-18
Supervised
22.84
4.10
15.03
26.42
45.79
Product-Patent
ResNet-50
Supervised
18.05
2.73
13.90
19.82
35.76
UCDIR
ResNet-50
UCDIR
18.64
2.87
14.03
20.17
35.81
UCDIR+IDM
ResNet-500
UCDIR
24.94
4.96
18.13
28.42
46.91
UCDIR+IDAMA
ResNet-50
UCDIR
25.36
5.31
18.47
28.97
47.28
IDM
ResNet-50
Supervised
25.06
18.45
28.93
47.61
IDM+DeepPatent
ResNet-50
Supervised
25.12
5.47
18.68
28.25
48.06
IDAMA
ResNet-50
Supervised
25.63 (47.55
5.47
19.13
29.84
48.06
IDAMA
Swin-B
Supervised
26.20
6.38
19.36
29.84
49.20
IDAMA
VIT-B
Supervised 26.43
6.83
20.05
30.30
48.52
IDAMA
Swin-L
Supervised
28.02
7.52
22.32
33.94
48.29
IDM
VIT-L
Supervised
26.99
6.83
22.55
31.44
47.15
IDAMA
VIT-L
Supervised
28.30
7.97
23.23
33.48
48.52
IDAMA
VIT-L
MAE 52
23.35
5.24
17.31
28.02
42.82
IDAMA
VIT-L
IBOT
531
31.61
9.34
28.02
36.21
$2.85
the patent dataset Dp. We represent the labels as the correspondence between infringing products and patent images, forming set R {I Viy) 1, I....,I 439). This work collected 439 product-patent image pairs to evaluate product-patent retrieval.
Given the unsupervised formulation of PPIR tasks, performance by calculating the cosine similarity between patent and product image features. In PPIR tasks, product patent may contain similarity of all the product-image and patent-image pairs of the product or patent to denote the retrieval similarity in this pair. Moreover, PPIR contains 1-to-N situations, indicating that one product may infringe several patents, and we regard retrieving at least one patent as a successful retrieval.
Evaluation metrics:
the retrieval
than one image, and choose
the average
We sort the patents in the retrieval pool according their similarity scores and metries to systemically evaluate PPIR performance [51]: the mean Average Recall (mAR) and top-k recall at specific thresholds (A 100, 500, 1000, 2000) (R @ K) for each patent- pereuttproduct pair. mAR calculates the average recall rate of different top-k thresholds. AR considers the PPIR performance different thresholds and reflect the overall PPIR performance among other difficulties. Considering the I-to-N matching situations, we use recall metric for evaluation. The @KK can demonstrate the model discrimination capability in specific scenarios.
4.2 Pre-training in Intermediate Domain
The capability of feature extractor (.)(.) can scriously influence PPIR performance. Therefore, pre-training in the intermediate sketch-image domain may contribute to enhancing the representation and discrimination capability of the model, obtaining Esketeh and improving PPIR performance. In this section, We compare the performance between supervised and unsupervised methods for feature learning in the intermediate domain. In this section, all the images, including images both PPIRD-unlabeled D Dunlabeled and ImageNet D DimageNet. mapped to the intermediate domain using Maach (+) to extract sketch images for pre-training:
Tpre Maketch (Ipre); VIpre E Dunnlabeedd DimageNet;
(13)
where I're and Ipre denote the images of intermediate and domains, respectively.
Unsupervised Pre-training: Unsupervised pre-training has played significant role in computer vision recently [54] 53 55| 56 57 58 52 enabling efficient visual representation encoders without
7

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Table 2: Quatitative Results of different domain mapping methods: Compared with other mapping methods, IDM ('Product(Edge) (atge)tdeet)))gee is the more suitable mapping method for PPIRD and bring more performance enhancement.
Method
mAR
R 100
@@500
@@ 1000
R@ 2000
Product-Patent
18.05
2.73
13.90
19.82
35.76
Product-Patent (Colorized by [27]
20.44
3.19
15.49
23.01
40.09
Product (Binary Line)-Patent
22.67
3.64
17.77
25.51
43,74
Product (Edge)-Patent
23.80
4.33
18.45
27.33
45.10
Product (edge)-Patent (Edge)
25.63
5,47
19.13
29.84
48.06
(a) Self- suff-superiiedd & unsuporvized pre training
(b) Supervised pre-training
M Statch Mappe
Unlatakel data
prettainns
Unsuperdted
pre-trained hoctbone
Superisse
Figure 3: Comparison between seff-/unsupervised pretraining and supervised pretraining strategies. Subplot (a): seff-lunsupervise contrastive learning method 'IBOT" pre-trained on PPIRI--unlabeled Matched product-patent image pairs
Subplot (b): Supervised pretraining ImageNetlk-Fdee are depicted using the same color. The visualization demonstrates that the unsupervised pretraining method effectively brings matched pairs closer in the feature space, enhancing their alignment.
the need for labeled training data. We utilize unsupervised pre-training methods of both Masked Image Modeling (MAF (52)) and Contrastive Learning (IBOT (53)) to obtain feature encoders for the intermediate domain by minimizing the unsupervised loss function Cussup:
min
Lumsup (F((Tpr)))
(14)
where E() is the feature extractor and Cussup is the unsupervised loss function specific the pre- training method used. Images of PPIRD-unlabeled Duntabeled are used for unsupervised pre-training,
Supervised Pre-training: According to the conclusion of [59], supervised pre pre- vre-training may be more suitable for learning representative and distinguishable features in the intermediate sketch-image domain due the sparsity of this domain. Specifically, Masked Image Modeling Contrastive learning may be challenging to reconstruct those large blank areas or discriminate blank areas from others, hindering the model's ability learn representative and distinguishable feature representations. Considering this possibility, we conduct a simple yet effective supervised pre-training method by mapping the images ImageNet and using the mapped well-annotated data for backbone pre-training. Based on these mapped sketch images, we train a classifier to obtain feature extractor E(-) using the original labels of these images. The training objective minimize the cross-entropy loss:
L Σ ye log it, with yi softmax (atimax(((Tseechaa) (Isketch,;)),
(15)
where yi is the ground truth label, and Ihench is the sketch images. The advantage of this approach is that sketch feature extractor has explicit learning objective and does not rely negative samples in contrastive learning or masks in self-supervised learning. This endows feature extractor with excellent discriminative ability. More implemental details are provided in Sec. B
4.3
Main Results
We compare IDAMA with raw ResNet [60] (denoted as PProduct-Patent) and the current SOTA patent retrieval method DeepPatent [5]. For fair comparison, we add IDM before DeepPatent (5) since is designed for patent image retrieval. Tab. demonstrates the quantitative results on PPIRD. As is shown, IDAMA can bring significant performance enhancement compared with baseline methods, and both IDM and MAF can contribute to the improvement.

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(a) Directly extract original product and patent feature
(b) Colorized patent to align product
Generator
Simiarity
Sinfority
(c) Extract product sketch to align patent
(d) Joint mapping of product and patent to edge
Simfority
M: Sketch Mapper
Feature Extractor
Product Feature
Patent Feature
Figure 4: t-SNE results for different domain mapping methods: (a) Product-Pateent Directly extract original product and patent feature: (b) Product-Patted (Colorized): Colorized patent to align product; (c) Product (Binary Line)-Patent: Extract product edge to align patent; (d) IDM: Extract both product images and patent images to sketch images. In each subplot, closer interleaving of the two colored points indicates a greater reduction in the domain gap, indicating better patent/product image alignment. IDM can best achieve the goal.
Comparison with DeepPatent: The comparison with DeepPatent [5] proves that PPIR (product- patent level retrieval) is quite different from Patent Image Retrieval (image leval retrieval) tasks. PPIR more challenging because the product and the patent are not the same object. When comparing DeepPatent with MAF ('DMM+DeePPatent' and 'IDAMA' in Tab. [T. though the idea of MAF is more intuitive and even without requiring extra pre-training. brings more performance enhancement, indicating the importance of integrating human beings' philosophy for challenging tasks.
Comparison of Different Pre-training methods: When comparing with different types of pre- training methods, the contrastive learning methods ('IBOT" in Tab. [ may be the most suitable pre-training methods for PPIR tasks. We can notice that Masked Image Modeling methods ('MAE' in Tab. T perform worse than supervised learning, indicating these methods may not be suitable for such sketch domain lack of texture or color information. Fig. reaches similar conclusion.
4.4 Comparison with Other Domain Mapping Approach
We compare IDM with some naive domain mapping approaches for PPIR:
Mapping colorful RGB product images into binary line drawings through Edge Detection (denoted as 'Product(Binary Line)-Patent'): We employ an Edge Detection Mapper Medge (-) to extract edge from product images I,, converting them into line drawings Ire Medge (I,). The advantage of this method is its computational efficiency and ease of implementation. However, the it. Under varying lighting and texture conditions, the extracted edge maps from product images may exhibit discontinuities, affecting the performance of PPIR. Additionally, the feature extractor demonstrates in-robustness in extracting features from binary line- drawing images.
accuracy of the edge feature extraction algorithm is heavily dependent on
Converting patent images into RGB colorful images through diffusion (denoted as 'Product- Patent (Colorized))): Recently, generative models [6]
62 63 have gained increasing attention,
with many models capable of efficiently generating RGB images based on line drawings 25 26 27
We utilize a generative model Meslerize (-) 25 transform patent images into pseudo-produce images, Ipg = Meotorize (p)) The advantage of this method is that the generated images are in RGB format, allowing direct use feature encoders pre-trained on natural images for feature extraction. However, challenges include the high computational cost of generative models and the lack of specific domain knowledge, which leads to the color distribution and texture of generated images may differ substantially from natural images.

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We conduct extensive evaluations of the above three approaches using ResNet-50 [60], pre-trained on ImageNet- 1K [6] (or -e-geNee-IK-Edger for feature extraction. According to the t-SNE [64 results in Fig. 4 IDM (denoted as 'Product (Edge)- -Ptent (Edge)', mapping patent images and product images into the intermediate sketch domain) is the best method to align patent/product images and alleviate the negative effects of domain gaps. Tab. reaches a similar conclusion. We also conduct experiments by only mapping product images to directly sketch and compare their similarity with patent images. Experiment result 'Product
in Tab. 2 proves the domain matching method is suboptimal for the tasks, indicating a domain gap between sketch and binary line drawings.
5 Conclusion
In summary, we formulate Patent-Product Image Retrieval (PPIR) as an open-set image retrieval task and propose large-scale Patent-Product Image Retrieval Dataset (PPIRD) comprising (1) testing set with 439 product-patent pairs (annotated and validated by experts with detailed descriptions) and a retrieval pool of 727,921 patents, and (2) unlabeled pre-training with 3,799,695 product/patent images. We propose a novel Intermediate Domain Alignment and Morphology Analogy (IDAMA) strategy tailored for PPIR. IDAMA contains an Intermediate Domain Mapping method align binary line drawing patent images and colorful RGB product images by mapping them into an intermediate sketch domain using an edge detector to effectively mitigate the domain discrepancy and Morphology Analogy Filter to select discriminative patent images containing distinctive visual feature of patents for efficient similarity comparison (inspired the cognitive principle- an unknown object can be described by analogy to known object (patent image with high classification score regardless of label). Extensive experiments on PPIRD demonstrate that the intermediate domain more suitable for aligning patent/product images and improving performance.
Acknowledgements
This work is supported in part by the Shenzhen Science and Technology Program (ICY1202208
JCCI100208881030000000 8103001002), the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong. Shenzhen, the Longgang District Special Funds for Science and Technology Innovation (LGKCSDPT2023002), the Project (No. 20232ABC03A25), the Scientific Research Program of Wuxi Health Commission (Project NO. Z202309).
References
[1] Dylan Myungchul Kang. Charles Cheolgi Lee, Suan Lee, and Wookey Lee. Patent prior art search using deep learning language model. In Proceedings of the 24th Symposium on International Database Engineering & Applications, pages 1 1-5, 2020.
[2] Kotaro Higuchi and Keiji Yanai. Patent image retrieval using transformer-bant deep metric learning. World Patent Information, 74:102217, 2023.
[3] Alla Kravets, Nikita Lebedev, and Maxim Legenchenko. Patents images retrieval and convolutional neural network training dataset quality improvement. In IV International research conference" Information rechnologies in Science, Management, Social sphere and Atlantis Press, 2017.
2017), pages 287-293,
[4] Kotaro Higuchi. Yuma Honbu, and Keiji Yanai. Patent image retrieval using cross cross-entropy-oo based metric learning. In Proc. of the 29th International Workshop on Frontiers of Computer Vision, 2023.
[5] Michal Kucer, Diane Oyen, Juan Castorena, and Jian Wu. Deeppatent: Large scale patent drawing recognition and retrieval. In Proceedings of the IEEE/CVE Winter Conference on Applications of Computer Vision, pages 1309-2311 18, 2022.
[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hicrarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248 255. lcce, 2009.
[7] Shiv Ram Dubey, A decade survey of content based image retrieval using deep learning. IEEE Transactions Circuits and Systems for Video Technology. 32(5):2687-2700 2021.
10

---

[8] Julian Risch, Nicolas Alder, Christoph Hewel, and Ralf Krestel. Patentmatch: dataset for matching patent claims & prior art. arXiv preprint arXiv:2012.13919 2020.
[9] Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M Jones, Michal Kucer, and Diane Oyen. Deeppatent2: A large-scale benchmarking corpus for technical drawing understanding. Scientific Data, 10(1):772, 2023.
[10] Mirac Suzgun, Luke Mclas-Kyriazi, Suproteem Sarkar, Scott D Kominers, and Stuart Shicher. The harvard uspto patent dataset: A large-scale, well-structured, and multi-purpose corpus of patent applications. Advances in Neural Information Processing Systems, 36, 2024.
[11] Nikos Efthymiadis. Giorgos Tolias, and Ondfej Chum. Edge augmentation for large-scale sketch recognition without sketches. In 2022 26th International Conference on Pattern Recognition (ICPR), pages 3595-3605 TEFE, 2022.
[12] Ralf Krestel, Renukswamy Chikkamath. Christoph Hewel, and Julian Risch. A survey on deep learning for patent analysis. World Patent Information, 65:102035, 2021.
[13] Homaira Huda Shomee, Zhu Wang, Sathya N Ravi, and Sourav Medya. A comprehensive survey on ai-based methods for patents. arXiv preprint xrXiv:2404.06668. 2024.
[14] Yonghe Lu, Xin Xiong, Weiting Zhang, Jiaxin Liu, and Ruijie Zhao. Research on classification and similarity of patent citation based on deep learning. Scientometrics, 1338813-839, 2020.
[15] Selen Yúcesoy Kahraman, Türkay Dereli, and Alptekin Durmuçoğlu. Forty years of automated patent classification. International Journal of Information Technology & Decision Making, pages 1-32, 2023.
[16] Ran Li, Wangke Yu, Qianliang Huang, and Yuying Liu. Patent text classification based deep learning and vocabulary network. International Journal of Advanced Computer Science and Applications, 14(1). 2023.
[17] Hamid Bekamiri, Daniel Hain, and Roman Jurowetzki. Patentsberta: A deep nlp based hybrid model for patent distance and classification using augmented sbert. Technologicul Forecasting and Social Change, 206: 123536, 2024.
[18] Eleni Kamateri, Michail Salampasis, and Eduardo Perez-Molina. Will ai solve the patent classification problem? World Patent Information, 78: 102292,,4, 2024.
[19] L Siddharth, Guangtong Li, and Jianxi Luo. Enhancing patent retrieval using text and knowledge graph embeddings: a technical note. Journal of Engineering Design, 33(8-9):670-6)) 683, 2022.
[20] Hao-Cheng Lo, Jung-Mei Chu, Jieh Hsiang, and Chun-Chieh Cho. Large language model informed patent image retrieval. arXiv preprint arXiv:2404. 19360, 2024.
[21] Chenhe Dong, Yinghui Li, Haifan Gong, Miaoxin Chen, Junxin Li, Ying Shen, and Min Yang. A survey of natural language generation. ACM Computing Surveys, 55(8):1388. 2022.
[22] Josh Achiam, Steven Adler Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint 230.008744 2023.
[23] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, llya Sutskever, al. Language models are unsupervised multitask leareers. OpenAl blog, 18))9, 2019.
[24] Yann LeCun, Yoshua Bengio, et al. Convolutional networks for images, speech, and time series. The hundbook of bruin theory and neural networks, 3361 1995.
[25] Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Jun- Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403. 20t.:::22033/2336666 /2036, 2024.
[26] Subhadcep Koley, Ayan Kumar Bhunia, Anceshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song. You'll never walk alone: A sketch and text duet for fine-grained image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16509-16519, 2024.
[27] Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi- Zhe Song. Text-to-image diffusion models are great sketch matchmakers. In Proceedings of the 2024.
IEEE/CVF Conference Computer Vision and Puttern Recognition, pages
11

---

[28] Hyeonseob Nam, HyunJae Lee, Jongchan Park, Wonjun Yoon, and Donggeun Yoo. Reducing domain gap by reducing style bias. In Pruceedings of the IEEE/CVE conference on computer vision and pattern recognition, pages 8690-8699, 2021.
[29] Liuliu Xu, Haifan Gong. Yun Zhong, Fan Wang, Shouxin Wang, Lu Lu, Jinru Ding, Chen Zhao, Wenchao Tang. and Xu. Real-time monitoring of manual acupuncture stimulation parameters based on domain adaptive 3d hand pose estimation. Biomedical Signal Processing and Control, 83:104681, 2023.
[30] Senmao Wang, Haifan Gong, Runmeng Cui, Boyao Wan, Yicheng Liu, Zhonglin Hu, Haiqing Yang. Jingyang Zhou, Bo Pan, Lin Lin, et al. Costal cartilage segmentation with topology guided deformable mamba: Method and henchmark. arXiv preprint arXiv:2408. 07444, 2024.
[31] Haifan Gong, Yu Lu, Xiang Wan, and Haofeng Li. Domain generalized medical landmark detection via robust boundary- pre-training. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 3140-3148, 2025.
[32] Haifan Gong, Huixian Liu, Yitao Wang, Xiaoling Liu, Xiang Wan, Qiao Shi, and Haofeng Li. Fetal cerehellum landmark detection hased on 3d mri: Method and benehmark. IEFE Journal of Biomedical and Health Informatics, 2025.
[33] Conghui Hu, Can Zhang, and Gim Hee Lee. Unsupervised feature representation learning for domain- generalized cross-domain image retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11016-11025 2023.
[34] Bin Li, Ye Shi, Qian Yu, and Jingya Wang. Unsupervised cross-domain image retrieval via prototypical optimal transport, Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 2024.
[35] Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. Interna- tional conference on machine learning, pages 1189-189,,,,11111111111111191191 PMLR, 2015.
[36] Zihang Xu, Haifan Gong, Xiang Wan, and Haofeng Li. Asc: Appearance and structure consistency for unsupervised domain adaptation in fetal brain mri segmentation. In International Conference on Medical Image Computing and Computer-Assistr Intervention, pages 325-335. Springer, 2023.
[37] Pedro O Pinheiro. Unsupervised domain adaptation with similarity learning. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8004-8013 2018.
[38] Yunhao Ge, Yao Xiao, Zhi Xu, Xingrui Wang, and Laurent Itti. Contributions of shape, texture, and color in visual recognition. In European Conference on Computer Vision, pages 369-386. Springer, 2022.
[39] Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, and Yi-Zhe Song. Picture that sketch: Photorealistic image generation from abstract sketches In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 6850-6861, 2023.
[40] Andrey Voynov, Kfir Aberman, and Daniel Cohen-Or. Sketch-guided texl-to-image diffusion models. In ACM SIGGRAPHI 2023 Conference Proceedings, pages 1-11, 2023.
[41] Caixia Zhou, Yaping Huang, Mengyang Pu, Qingji Guan, Ruoxi Deng, and Haibin Ling, Muge: Multiple granularity edge detection. In Proceedings of the IEEE/CVF Conference Computer Vision and Patlern Recognition, pages 25952-25962. 2024.
[42] Irving Biederman. Recogniton-by--cmpoeentt A theory of human image understanding. Psychological Review, 94(2):115 147, 1987.
[43] David L. Donoho. Compressed sensing. IEEE Trans. Inf. Theory, 12(4):1289-1300 2006.
[44] Emmanuel J. Candès. Justin Romberg, and Terence Tao. Stable signal recovery from incomplete and
inaccurate measurements. Communications on Pure and Applied Mathematics, 1297)112071123 2006.
[45] Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin. A simple proof of the restricted isometry property for random matrices. In Constructive Approximation, volume 28, pages 333-263, 2008
[46] Roman Vershynin. High- Singe--Dmmssinal Probability: An Introduction with Applications in Data Science. Cambridge University Press, 2018
[47] Yaodong Yu. Sam Buchanan, Druv Pai, Tianzhe Chu, Ziyang Wu, Shengbang Tong, Hao Bai, Yuexiang Zhai, Benjamin D Haeffele, and Yi Ma. White-box transformers via sparse rate reduction: Compression is all there is? Journal of Machine Learning Research, 25(300):1-128, 2024.
12

---

[48] William W Hager. Lipschitz continuity for constrained processes. SIAM Journal on Control and Optimizu- tion, 17(3):321-338, 1979.
[49] Martin Anthony and Peter I., Bartlett. Neural Network Learning: Theoretical Foundations. Cambridge University Press, 2009.
[50] Emmanuel J. Candès, Justin Romberg, and Terence Tao. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2):489 509,050990000000 2006.
[51] Hervé Jégou, Matthijs Douze, and Cordelia Schmid. Aggregating local image descriptors into compact codes. European Conference on Computer Vision, pages 776-789. Springer, 2010.
[52] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022.
[53] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Yujun Hou, Wenliang Jiang. Tianjun Gong, Yu Qiao. Thomas Y Hui, Ziwei Liu, et al. ibot: Image bert pre-training with online tokenizer. In International Conference on Learning Representations, 2022.
[54] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and puttern recognition, pages 9729-977 1099-9735 373,.22 2020.
[55] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in super-idd vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660. 2021.
[56] Haifan Gong, Guanqi Chen, Sishuo Liu, Yizhou Yu, and Guanbin Li. Cross-modal self-attention with multi-task pre-training for medical visual question answering. In Proceedings of the 2021 international conference on multimedia retrieval, pages 1556-460,200 2021.
[57] Yuxin Fang, Wen Wang, Binhui Xie. Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang. Xinlong Wang. and Yue Cao. Eva: Exploring the limits masked visual representation learning at scale. In Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 19358-19369, 2023.
[58] Yuxin Fang, Quan Sun, Xinggang Wang. Tiejun Huang, Xinlong Wang. and Yue Cao. Eva-02: A visual representation for neon genesis. Image and Vision Computing, 149: 10517,,,,, 2024.
[59] Wenxuan Li, Alan Yuille, and Zongwei Zhou. How well do supervised models transfer to 3d image segmentation. In The Twelfth International Conference on Learning Representations, volume 1, 2024.
[60] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pottern recognition, pages 770-778, 2016.
[61] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NewrIPS, 33:6840- 6851, 2020.
[62] Ling Yang. Zhilong Zhang, Yang Song. Shenda Hong, Runsheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diftusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 36(4):1-39) 2023.
[63] Haifan Gong, Yitao Wang, Yihan Wang, Jiashun Xiao, Xiang Wan, and Haofeng Li. Diffuse- uda: Addressing unsupervised domain adaptation in medical image segmentation with appearance and structure aligned diffusion models. arXiv preprint 24Xiv:408.00998, 2024.
[64] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne, 2008.
[65] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021.
[66] Alexey Dosovitskiy. Lucas Beyer, Alexander Kolesnikov, Dirk Weissenbor, Xiaohua Zhai Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer. Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x 16 words: Transformers for image recognition at scale. ICLR, 2021.
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A Proofs for Section 3.3
A.1 Proof of Claim 2
Let x == Sp Spr. By assumption, æ is 2k-sparse and ||>|| S E. Because A satisfies R2k 6),
| S vI+ |||| S (1 + 8) E.
(16)
Since A is linear, Ax Asp A A which concludes the proof.
A.2 Proof of Theorem 1
Decompose each image as 8 + and observe
A Alp- (Asp Aspr) (Arl Alppr.
(17)
Applying the backbone F and using its L--Lipschitz continuity [48).
_ - 2pr 12 | - E (AAlpr) 122
< L AI ATpr ll
(18)
$ L(]Asp - Asplll + ||mpll + ||Anp)l12)
The first term is bounded by Claim For the nuisance terms we appeal to the concentration of the x² distribution of Gaussian projections (46] Thm. 7.5]: for any fixed vector n and all E (0, 1).
Pa m Il
2 th Imll3) eexp -ctmm.
(19)
where c > 0 is an absolute constant. Selecting t = 1 and using tb I b/ gives,
with probability at least - 2e cm//4
A S m |1112
(20)
Applying this bound to both Up and Apr and combining all inequalities establishes the asserted result.

---

B Implemental Details
The Tmaf is set 0.4. We directly use the Supervised Pre-trained ResNet-50 on ImageNet-IK as the Morphology Analogy Filter. For all the supervised pre-training models (including ResNet [60]. SwinTraasformer [65], and ViT [66]), the pre-training dataset is ImageNet- K-Edge (Edge is extracted using (25 the optimizer is AdamW, the learning rate is set to 0.002, the beta of the optimizer is set to (0.9, 0.95), and the weight decay is set to 0.001. We train all models for 100 epochs. For all the selfssunervieeddnnuurrriiee pretraining, the pre-training dataset is PPIR PPIR-unlabeled-E-an
(Edge is extracted using 251), the batch size is 256, the optimizer Adam W. the learning rate 0.002. We pre-train all the models for 100 epochs. The batch size is set from 256 to 1024, with more details available in the supplementary material.
C Appendix on Training Details
Table 3: Hyper-parameters for supervised training. successive model sizes listed in the Model row.
Values separated by "/" correspond to the
Hyper-paramette
ResNet
Swin Swin-T/S/ ImageNet K-eege 2048 1024 512
VIT
Model
ResNet-18 / 50 / 101
ViT-B-16 L-16
Dataset
I-egee
ImageNetIK-edge
Batch size
4096 / 2048 1024
512 256
Optimizer
AdamW
AdamW
AdamW
Learning rate
0.002
0.002
0.002
B (AdamW)
(0.9, 0.95)
(0.9, 0.95)
(0.9, 0.95)
Epochs
100
100
100
Weight decay
0.001
0.001
0.001
Training time (h)
5.3 / 18.7
7.9/ /
19.8 34.7
Table 4: Hyper- Seper-parameters for self-supervised learning.
Hyper-parameter
iBOT
MAE
Method
iBOT
MAE
Model
ViT-L-16
ViT-L-16
Dataset
PPIR-unlabeled
PPIR-unlabeled
Batch size
256
256
Optimizer
AdamW
Adam
Learning rate
0.002
0.002
3 (AdamW)
(0.9, 0.95)
(0.9, 0.95)
Epochs
100
100
Training time (h)
97.4
95.3

---

D Visualization Results
USA CaAaaaeeo
iutthaanal
flambnnie
Becetiiit oll
Figure 5: Upper: Diffusion generated colorful patent images
Lower: Sketches extracted from raw images
E Broader impact, Advantages, and Limitations
Broader Impact. Our work lowers the barrier for large-scale, scale, automated monitoring of potential design infringements, thereby helping both innovators and regulators to protect intellectual property more efficiently and earlier stages the product life-cycle. The publicly released PPIRD dataset also creates a new benchmark that can stimulate research on cross-domain retrieval, representation learning under distribution shifts, and open-set recognition. Nevertheless, any technology that simplifies infringement search can also be misused: malicious actors might automate "design-around" strategies or target small businesses with aggressive litigation. We therefore encourage downstream users combine PPIR systems with human expert review, publish detailed audit logs, and follow responsible-Al frameworks to mitigate unintended consequences.
Advantages. (I) Comprehensive benchmark. PPIRD is, to our knowledge, the largest and most realistic testbed for patent-product image retrieval, containing nearly one million patent drawings and millions of unlabeled images for pre- pre-training. (2) Efffective domain bridging, The proposed IDAMA pipeline explicitly maps both binary line drawings and RGB product photos into an intermediate sketch space, reducing domain discrepancy without requiring paired supervision. (3) Open-set readiness. By framing PPIR as an open-set problem and adding morphology-anallee method retains high recall novel object categories that are unseen during conventional pre-training. (4) Plug-and-play. IDAMA is architecture-se aggnosttree- it can be integrated into existing CNN or vision-transformer backbones with minimal code changes and no task-specific annotations.
filter, our
The morphology-analog: filter relies on hand-crafted similarity thresholds; tuning them across industries jurisdictions may require domain expertise. In addition, dataset-whit large-core covers only 439 manually verified product-patent pairs, limiting fine-grained evaluation of failure modes such functional equivalence or partial design overlap.
Limitations.
3


Reviews:
[{"review_id": "bjEFFoUU3q", "reviewer": "Reviewer_kHF7", "summary": "The paper addresses the underexplored task of Patent-Product Image Retrieval (PPIR), which involves retrieving patent images based on product images to identify potential infringements. The key challenge of PPIR is bridging the domain gap between binary patent drawings and RGB product images while recognizing diverse unseen objects. To tackle these issues, the authors introduce an open-set image retrieval task, a comprehensive PPIR Dataset (PPIRD) and a method Intermediate Domain Alignment and Morphology Analogy (IDAMA). The PPIRD contains a test set with 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set containing 3,799,695 images. The experiments conducted on PPIRD demonstrate that IDAMA significantly surpasses baseline methods by +7.58 mAR.", "questions": "1.\tHow did the authors collect such a large-scale dataset, and what are the sources of these data? Will the dataset be made publicly available in the future? Providing a detailed explanation of this process is crucial for ensuring the transparency and reproducibility of the research. Understanding the specific methods and challenges involved in data collection can assist other researchers in evaluating and replicating the experimental results.\n2.\tThe writing quality of this paper could be improved. Many sentences are overly complex and difficult to parse, which negatively affects the overall readability. For example, the following sentence is excessively long and lacks clarity: \"To address the challenge, we propose the Intermediate Domain Mapping (IDM) (See Fig. 2 for details) to align binary drawings patent images and colorful RGB product images by mapping both of them to the intermediate sketch domain.\" A more concise and clearly structured version would greatly enhance comprehension.\n3.\tThe proposed method lacks significant innovation. It primarily employs techniques that are commonly used in cross-domain tasks. Without introducing novel components, the contribution of this approach remains limited.\n4.\tI noticed that the comparative method, DeepPatent, uses mAP as its evaluation metric. Why did the authors choose to overlook this and rely solely on mAR for performance evaluation?", "limitations": "Although the paper highlights several technical challenges, such as the domain gap between product images and patent drawings, its experiments are limited to the authors' in-house PPIRD dataset. This restricts the evaluation across other data distributions, such as the DeepPatent dataset. Notably, although DeepPatent primarily consists of binary line drawings, it still offers valuable opportunities to assess the model's robustness to similar images of the same patent drawings. Expanding the experiments to more diverse datasets would provide a more comprehensive understanding of the proposed method's effectiveness and generalization capability.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1.\tThis article introduces a new benchmark for Patent-Product Image Retrieval, comprising a test set of 439 product-patent pairs, a retrieval pool of 727,921 patents, and an unlabeled pre-training set featuring 3,799,695 images. This benchmark is likely to be significant for guiding future research directions in the field.\n2.\tThe motivation behind MAF is very interesting.\n3.\tThe effectiveness of IDM is analyzed through the theory of compressive sensing.\n\nWeakness:\n1.\tThe PPIR dataset is clearly a significant contribution of this paper, providing an essential resource for advancing research in patent-product image retrieval. However, the description of how this dataset was collected and curated is limited to section 4.1, which primarily focuses on the test set and retrieval pool construction. It leaves questions regarding the comprehensive collection process of all data, especially the unlabeled pre-training set consisting of 3,799,695 images. More detailed information on the methodologies and challenges involved in gathering such a vast amount of data would provide valuable insights and enhance the reproducibility of their work. \n2.\tI question the necessity of using real product images to search for patent drawings, as detailed textual descriptions or metadata often suffice for identifying potential infringements. The value of comparing cross-domain images (i.e., real product images and patent drawings) is debatable, given the increased system complexity, resource demands, and potential accuracy issues. Without clear evidence of significant advantages over existing methods, the practicality of such a specialized system remains uncertain. \n3.\tThe proposed IDAMA method appears overly simplistic and lacks significant innovation. The approach is commonly employed in cross-domain tasks. It seems that the authors have primarily adapted this existing strategy to a new application scenario rather than introducing a fundamentally novel contribution.", "was_revised": true, "final_justification": "The authors addressed most of my concerns, therefore I raise my score.", "rebuttal": "Thank you very much for taking the time to review and your support. We try our best to address your questions as follows: \n\n**Q1: Unlabeled Data Collection**\n\nThe unlabeled data contain patent and product images, and the ratio is about 2:1. \n\n- **Patent Image Collection:** The IPC patent category system is a 5-level hierarchy. At the same time, the 4th-level categories group highly similar objects; items sharing the same 4th-level label may not necessarily belong to the same category in Image Retrieval tasks. To ensure that the collected patent data maintains some relevance to the test set while preserving the generalization capability, we first identified all 2nd-level labels in the test set. Randomly sample 50,000 patents (non-overlapping with the test set) for these 2nd-level labels and collect their images. We filtered some noisy images using contrastive clustering. \n\n- **Product Image Collection:** We randomly collect an additional 500 similar non-overlapping products for each product in the test set and collect their images. \n\n**Q2: Necessity of PPIR**\n\nWith the rapid development of e-commerce and live-stream shopping platforms, many sellers now deliberately omit detailed text descriptions or use ambiguous names to disguise infringing products, leading to ineffective traditional text-based detection methods. PPIR aims to overcome the challenge of such deliberate evasion of text information. \n\n**Q3: Necessity of IDAMA**\n\nPPIR is distinctively different from classical cross-modal image retrieval (CMIR) for two challenges: \n\n- **Data and category distribution of training sets and test sets:** CMIR is an **in-distribution** task (its test set only contains object categories already present in the training set for both modalities). PPIR is an **out-of-distribution** task (its test set contains unseen categories compared to the training set). \n\n- **Amount and Type of categories:** The test set of CMIR always contained quite **limited** categories, and most of them are common categories. PPIR contains **vast** amounts of artificial categories, so that PPIR can only be formulated as an **open-set** image retrieval task. Moreover, these artificial categories severely degrade the performance even for single-modal image retrieval (noted in DeepPatent), and their negative impact is even more pronounced in cross-modal image retrieval (PPIR). \nIDAMA aims to address these challenges of PPIR. \n\nExtra Experiments: We compare IDAMA with one more advanced CMIR method on ResNet-50 (Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval, denoted as UCDIR, referred by Reviewer FygZ), and the mAR results are as follows: \n- Baseline: `18.05`, \n- UCDIR on direct product-patent: `18.64`, \n- UCDIR+IDM: `24.94`, \n- UCDIR + IDAMA: `25.36`. \n\nThese results prove that even more advanced classical CMIR methods cannot improve significantly compared to the baseline method on PPIR. However, IDAMA can overcome the challenges and enhance the performance of the classical CMIR method on PPIR. \n\n**Q4: Novelty of IDAMA**\n\nThe core novelty of IDAMA is to address **out-of-distribution open-set image retrieval** of PPIR by introducing the cognitive principle of morphology analogy \"`unknown object can be described by analogy to a known object`\" (which you think is interesting), and systematically investigate, both empirically and theoretically, more suitable conditions to enhance performance using such a philosophical framework in such kinds of task. \n\nExtra experiment in Q3 proves that IDAMA achieves the goal and exhibits high extensibility, enabling seamless integration of other CMIR methods and consistently improving performance. \n\nReviewer sg9B has recognized the insight and value behind IDAMA and suggests extending it to be more similar to the CAD-to-product retrieval problem. \n\n**Q5: Evaluation Metrics**\n\nIn PPIR, one product may infringe multiple patents (1-to-N). We adhere to the industry-standard practice of determining patent infringement based on a single valid claim match and use mAR. If all the product-patent pairs are (1-to-1), mAR is the same as mAP. In product patent infringement detection, recall within the top‐$K$ retrieved patents (mAR@K) is critical since legal practitioners review only a fixed shortlist of candidates. Unlike the DeepPatent retrieval task—which optimizes mean Average Precision (mAP) to assess ranking quality across the entire corpus—our infringement use case demands that as many relevant prior‐art patents as possible appear in the top‐$K$ results. Precision beyond the $K$th rank has the disadvantage that missing a pertinent patent in the shortlist risks overlooked infringement, whereas extra false positives merely increase human review effort. Consequently, mAR@K directly aligns with operational workflows and risk mitigation goals, making it a more appropriate evaluation metric than mAP for our infringement detection scenario.\n\n\n**Q6: Writing and Datasets**\n\nWe will polish those sections to clarify and publish the datasets upon acceptance. We agree that it is essential to evaluate the DeepPatent dataset. However, the setting between the DeepPatent dataset and our dataset is different, with the DeepPatent focusing on the retrieval between patents. In contrast, our benchmark focuses on the retrieval between patents and products. Still, we agree that patent retrieval is essential, and we will further investigate this setting."}, {"review_id": "Umzfo0ezEW", "reviewer": "Reviewer_sg9B", "summary": "The authors address the under-explored task of Patent-Product Image Retrieval (PPIR), where the goal is to retrieve relevant patent images based on input product images—useful for identifying potential IP infringements. The task is uniquely challenging due to: (a)A vast number of artificial object categories not present in standard vision datasets. (b) A significant domain gap between binary patent images and colorful product images.\nTo tackle these, the authors propose: (a) A new large-scale PPIR dataset (PPIRD), featuring over 700K patent images and nearly 4M unlabeled examples. (b) A novel method called IDAMA (Intermediate Domain Alignment and Morphology Analogy) - IDM maps both product and patent images to a shared sketch-like intermediate domain via edge detection, reducing domain discrepancy and MAF filters patent images using a class-agnostic confidence threshold (based on human-like morphological analogy), improving retrieval precision. (c) Empirical validation shows that IDAMA improves mean Average Recall (mAR) by up to +7.58 over baselines, and achieves strong performance with ViT backbones and contrastive pretraining.", "questions": "Please check weaknesses section", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The work targets a novel and impactful task with practical relevance to IP law, e-commerce, and legal AI.\n2. The use of a sketch-based intermediate domain is both intuitive and theoretically justified (via compressive sensing theory). Morphology Analogy Filter is inspired by cognitive science and adds interpretability to the filtering process.\n3. The creation of the PPIRD dataset is a significant contribution to the vision-for-patents community. The scale, realism (open-set formulation), and annotation process (with expert validation) are well executed.\n4. The paper benchmarks across multiple backbones (ResNet, ViT, Swin), pretraining schemes (supervised, MAE, IBOT), and domain alignment alternatives (e.g., colorization, binary line rendering). Ablation studies isolate the impact of IDM and MAF components.\n\nWeaknesses\n1. While the paper focuses on the patent-product scenario, it would benefit from a discussion on whether IDAMA generalizes to other domain-gap retrieval problems (e.g., art-photo, CAD-to-product).\n2. MAF relies on a threshold-based classifier score, which, although interpretable, is quite heuristic. A more principled or learnable mechanism might outperform it in general settings.\n3. The paper would be stronger with examples of failure cases—when does IDAMA fail and why?\n4. More visualizations of retrieval results (successes and failures) would enrich understanding, especially given the paper’s visually grounded motivation.", "was_revised": true, "final_justification": "Authors addressed all of my concerns.", "rebuttal": "**Q1: Use of IDAMA for other cross-domain retrieval task**\n\nIDAMA demonstrates high potential to generalize to other domain-gap retrieval problems. IDM learns a shared latent space to bridge disparate domains, while MAF filters out structurally inconsistent candidates based on learned morphological analogies. Neither module relies on patent-specific priors, operating purely at the feature-space level. As a result, IDAMA can be seamlessly integrated into any existing retrieval framework. Specifically, the CAD-to-product retrieval task also confronts the challenges of many diverse artificial categories and potential out-of-distribution categories like PPIR. \n\n**Q2: MAF strategy design**\n\nWe have investigated several learnable mechanisms for MAF before adopting the threshold-based classifier score, including prototype learning, contrastive learning, etc. However, due to the massive category amounts, potential out-of-distribution categories, and lack of supervision, these strategies are challenging to design. It is challenging to preserve robustness for many data examples. Therefore, we finally chose the heuristic but effective threshold-based strategy for MAF. This will be our future work on the MAF strategy design.\n\n**Q3. Failure cases analysis**\n\nThank you very much for mentioning this point. We have collected some failure cases and added them to the revised paper. The results show that our method may fail when the product background is an outdoor environment with more noise, such as grass or water. Other failure cases occur when captured from specific angles where the object fails to display its discriminative features, leading to matching failure, especially for those potential out-of-distribution-category objects. \n\n**Q4. More visualization results**\n\nWe will add more visualization results (successes and failures) in the paper."}, {"review_id": "sGhdGtS00W", "reviewer": "Reviewer_FygZ", "summary": "This paper proposes a new dataset and retrieval method for product-to-patent image retrieval. The proposed retrieval method first maps product image and patent image into the intermediate sketch domain, then replaces the mapped patent sketches with the most similar patent sketches through an morphology analogy filter, and finally extracts features for both product sketches and patent sketches. Evaluation on the introduced dataset demonstrates the superiority compared to a related method DeepPatent.", "questions": "1. How product-patent image retrieval distincts from existing cross-domain retrieval tasks, what are the unique challenges in this specific task?\n2. Is it possible to compare to existing cross-domain image retrieval methods?\n3. Why retrieval in sketch domain works better than color image domain and line-drawing domain? Is it sensitive to the choice of feature extractor?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n1. This paper contributes a new large-scale dataset for product-patent retrieval, which would facilitate future studies on this task.\n2. This paper is well-written and easy to follow.\n3. Experiment results show apparent improvement over prior product-patent retrieval method.\n\n**Weaknesses**:\n1. Insufficient evaluation. The proposed method is only compared to DeepPatent. The task considered by this work belongs to cross-domain image retrieval, where there are many advanced methods that should be compared to verify the technical advantage of the proposed method, e.g., [1], [2].\n2. Novelty. Given that cross-domain image retrieval is widely studied, the novelty of product-patent retrieval needs justification.\n3. The design of intermediate domain. It is shown in Sec. 4.4 that mapping both product and patent image into edge map performs better than single domain adaptation, a typical way in cross-domain image retrieval. It is unclear why performing retrieval in an intermediate domain works better in original domains.\n\n[1] Li et al. Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport. AAAI 2024.\n\n[2] Hu et al. Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval. ICCV 2023.", "was_revised": true, "final_justification": "The authors' rebuttal clarifies the unique contribution and advantage of the proposed method in contrast to cross-domain methods. My concerns have been addressed. I suggest the authors add more discussion in the revised paper on the design of an effective intermediate domain, which would benefit more research areas beyond patent-product retrieval.", "rebuttal": "**Q1. Comparison with a more advanced cross-modal image retrieval (CMIR) method**\n\nWe test a more advanced cross-modal image retrieval method ([2] Unsupervised Feature Representation Learning for Domain-generalized Cross-domain Image Retrieval) on ResNet-50 (only [2] is code available, denoted as UCDIR) on PPIR. The mAR results are: \n- Baseline: `18.05`, \n- UCDIR on direct product-patent: `18.64`, \n- UCDIR+IDM: `24.94`, \n- UCDIR with IDAMA: `25.36`. \n\nThese results prove that even more advanced classical CMIR methods cannot improve significantly compared to the baseline method on PPIR. However, IDAMA can overcome the challenges and enhance the performance of classical CMIR on PPIR. PPIR is distinctively different from the classical CMIR task for two unique challenges. (See Q2 for details). We have added these works to the revised paper for better discussion. \n\n\n**Q2. Unique challenges of PPIR**\n\nThe two unique challenges of PPIR compared to classical CMIR can be summarized as follows: \n\n- **Data and category distribution of training sets and test sets:** CMIR is an **in-distribution** task (its test set only contains object categories already present in the training set for both modalities). PPIR is an **out-of-distribution** task (its test set contains unseen categories compared to the training set). Our product-patent retrieval task aims to find the corresponding patent of a given product from all existing patents.\n\n- **Amount and Type of categories:** The test set of CMIR always contained quite **limited** categories, and most of them are common categories. PPIR contains **vast** amounts of artificial categories, so that PPIR can only be formulated as an **open-set** image retrieval task. Moreover, these artificial categories severely degrade the performance even for single-modal image retrieval (noted in DeepPatent), and their negative impact is even more pronounced in cross-modal image retrieval (PPIR). \n\nMoreover, other challenges include **intrinsic data noisy**: The patent images intrinsically contain noise, whereas the patent image may contain context like text that describes the product's working flow and working mechanism.\n\n\n**Q3: Novelty of IDAMA**\n\nThe core novelty of IDAMA is to address an **out-of-distribution open-set image retrieval** of PPIR by introducing the cognitive principle of morphology analogy \"`unknown objects can be described by analogy to a known object`\" and systematically investigate, both empirically and theoretically, more suitable conditions to enhance performance using such a philosophical framework in such kinds of tasks. \n\nWe notice that Reviewer kHF7 thinks the MAF of IDAMA is interesting, and Reviewer sg9B thinks IDAMA is novel. \n\nExtra experiment in Q1 proves that IDAMA achieves the goal and exhibits high extensibility, enabling seamless integration of other CMIR methods and consistently improving performance. \nReviewer sg9B has recognized the insight and value behind IDAMA and suggests extending it to similar tasks like the CAD-to-product retrieval problem. \n\n**Q4. The design of the intermediate domain**\n\nThe better performance in the intermediate sketch domain is also related to the two unique challenges (Q2) of PPIR. Specifically, in tuition, the out-of-distribution categories make it challenging for the colorization method to recover details from binary line images of these categories, influencing their performance. On the other hand, binary line images omit too many details, making it challenging to discriminate many different categories, especially for out-of-distribution categories. We also analyze this intuition through the theory of compressive sensing. Experiments also prove this conclusion."}]

Author rebuttal:

Meta-review: The paper received all positive recommendations and ratings from the reviewers. They acknowledged the novelty of the proposed task setting and the contribution of the large-scale benchmark for patent-product image retrieval. Some raised issues are also well addressed and appreciated by the reviewers. Thus, AC decided to recommend acceptance as a poster for this submission.
Decision: Accept (poster)