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arxiv: 2506.23471 · v2 · submitted 2025-06-30 · 💻 cs.IR · cs.CV

KiseKloset for Fashion Retrieval and Recommendation

Thanh-Tung Phan-Nguyen , Khoi-Nguyen Nguyen-Ngoc , Tam V. Nguyen , Minh-Triet Tran , Trung-Nghia Le This is my paper

Pith reviewed 2026-05-19 08:11 UTC · model grok-4.3

classification 💻 cs.IR cs.CV
keywords fashion retrievaloutfit recommendationvirtual try-ontransformer architecturecomplementary itemse-commerceimage searchrecommendation system
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The pith

KiseKloset pairs a new transformer for cross-category complementary fashion recommendations with a lightweight real-time virtual try-on module.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents KiseKloset as an end-to-end system for outfit retrieval and recommendation in fashion e-commerce. It supports two retrieval paths, similar-item matching and text-feedback guidance, while introducing a transformer that suggests items from different categories that work together as outfits. The system also adds a virtual try-on component built to run quickly, use limited memory, and generate realistic images of garments on a person. These pieces together aim to help online shoppers explore options more effectively and visualize purchases before buying.

Core claim

KiseKloset integrates similar-item and text-guided retrieval, a novel transformer architecture for recommending complementary garments across diverse categories, approximate algorithms for faster search, and a lightweight virtual try-on framework that operates in real time with low memory use while preserving output realism, as validated through deployment where 84 percent of users reported improved shopping experience.

What carries the argument

The novel transformer architecture that takes fashion item features and generates recommendations for complementary pieces drawn from multiple product categories to form coherent outfits.

If this is right

  • Approximate algorithms reduce search time over large fashion catalogs while preserving retrieval quality.
  • Text feedback allows users to refine searches beyond visual similarity alone.
  • Real-time virtual try-on lets shoppers preview how specific garments appear on their own body, supporting more confident purchase decisions.
  • Deployment feedback indicates that the combined retrieval, recommendation, and visualization tools raise overall user satisfaction in online fashion shopping.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The cross-category recommendation approach could extend to other visual product domains such as furniture or accessories where items must coordinate.
  • Widespread use of the virtual try-on module might lower return volumes by giving shoppers clearer expectations before purchase.
  • Mobile versions of the lightweight try-on framework could enable in-store augmented-reality previews without heavy hardware requirements.

Load-bearing premise

The new transformer and virtual try-on module deliver measurable improvements in recommendation quality and visualization realism over prior methods, as reflected in user satisfaction without detailed benchmark numbers.

What would settle it

A side-by-side A/B test that records purchase rates, return rates, or session completion times for shoppers using KiseKloset versus a baseline recommendation system without the transformer or virtual try-on components.

Figures

Figures reproduced from arXiv: 2506.23471 by Khoi-Nguyen Nguyen-Ngoc, Minh-Triet Tran, Tam V. Nguyen, Thanh-Tung Phan-Nguyen, Trung-Nghia Le.

Figure 1
Figure 1. Figure 1: Interface of the propose KiseKloset system, integrated outfit retrieval, recommendation, and virtual try-on capabilities. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our complementary item recommendation is more generalized than fill-in-the-blank outfit recommendation [ [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Our system supports various types of ORR. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Examples of text feedback-guided item retrieval. The first item is reference, the remain items are retrieval results. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: We augment retrieval results to enhance users’ experience. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Our proposed inter-category complementary item recommendation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Two settings of inter-category complementary item recommendation: Tone sur tone (top) and Mix and match (bottom). [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Overview architecture of the used DM-VTON [ [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Interaction flow of the proposed KiseKloset system. [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Query time of nearest neighbor methods. Subsequently, users must upload or pick the garment they want to try on from our provided list on the right side (as shown in Figure 9b). The left panel also displays the chosen model image in the previous step. Now users can press the Next button to view the try-on result. Once users have chosen both the input person and garment image, they are presented with the t… view at source ↗
Figure 11
Figure 11. Figure 11: Rating scores on ORR quality (1: very dissatisfied, 5: very satisfied). [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Issues of existing parser-free VTON methods (i.e., PF-AFN [11], FS-VTON [17], DM-VTON [30]). [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
read the original abstract

The global fashion e-commerce industry has become integral to people's daily lives, leveraging technological advancements to offer personalized shopping experiences, primarily through recommendation systems that enhance customer engagement through personalized suggestions. To improve customers' experience in online shopping, we propose a novel comprehensive KiseKloset system for outfit retrieval and recommendation. We explore two approaches for outfit retrieval: similar item retrieval and text feedback-guided item retrieval. Notably, we introduce a novel transformer architecture designed to recommend complementary items from diverse categories. Furthermore, we enhance the overall performance of the search pipeline by integrating approximate algorithms to optimize the search process. Additionally, addressing the crucial needs of online shoppers, we employ a lightweight yet efficient virtual try-on framework capable of real-time operation, memory efficiency, and maintaining realistic outputs compared to its predecessors. This virtual try-on module empowers users to visualize specific garments on themselves, enhancing the customers' experience and reducing costs associated with damaged items for retailers. We deployed our end-to-end system for online users to test and provide feedback, enabling us to measure their satisfaction levels. The results of our user study revealed that 84% of participants found our comprehensive system highly useful, significantly improving their online shopping experience.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the KiseKloset end-to-end system for fashion outfit retrieval and recommendation. It describes two retrieval approaches (similar-item and text-feedback-guided), a novel transformer architecture for recommending complementary items across diverse categories, integration of approximate search algorithms to optimize the pipeline, a lightweight real-time virtual try-on module claimed to be memory-efficient and more realistic than predecessors, and a deployed user study in which 84% of participants found the comprehensive system highly useful.

Significance. If the architectural contributions and user-study results can be substantiated with proper controls and comparisons, the work could demonstrate a practical integration of cross-category recommendation, efficient search, and visualization that improves online fashion shopping experiences and reduces return costs. At present the lack of methodological detail in the evaluation limits the ability to gauge its incremental contribution over existing retrieval and try-on systems.

major comments (1)
  1. [User Study] User Study section: the central claim that the deployed system delivers a practically superior experience rests on the statement that '84% of participants found our comprehensive system highly useful.' No participant count, recruitment method, questionnaire items, baseline interface, statistical test, or confidence interval is supplied, rendering the percentage uninterpretable as evidence of superiority.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'significantly improving their online shopping experience' is asserted without any quantitative comparison or baseline metric.
  2. [Methodology] The description of the transformer architecture for complementary-item recommendation would benefit from an explicit statement of its input/output format and loss function to allow comparison with prior cross-category models.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the concern regarding the user study below and commit to revising the paper to strengthen the evaluation section.

read point-by-point responses

  1. Referee: [User Study] User Study section: the central claim that the deployed system delivers a practically superior experience rests on the statement that '84% of participants found our comprehensive system highly useful.' No participant count, recruitment method, questionnaire items, baseline interface, statistical test, or confidence interval is supplied, rendering the percentage uninterpretable as evidence of superiority.

    Authors: We agree that the current description of the user study lacks the methodological details needed for proper interpretation and comparison. In the revised manuscript we will expand this section to report the exact number of participants, the recruitment approach via the deployed platform, the questionnaire items administered, any baseline interfaces used for comparison, and the results of statistical tests including confidence intervals. These additions will allow readers to better assess the practical impact of the system. revision: yes

Circularity Check

0 steps flagged

No circularity: system description without derivations or self-referential fits

full rationale

The paper introduces a KiseKloset system with a novel transformer for cross-category complementary recommendation, approximate search integration, and a lightweight virtual try-on module. Claims rest on architectural descriptions and a deployed user study reporting 84% satisfaction. No equations, parameters, first-principles derivations, or predictive models appear in the provided text. There are no self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations that reduce the central claims to inputs by construction. This is a standard applied systems paper whose evidence is empirical and descriptive rather than mathematically circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on domain assumptions about user study validity and the practical utility of the described architectures without introducing new mathematical entities or free parameters.

axioms (1)
  • domain assumption Deployed user feedback provides a reliable measure of overall system usefulness and satisfaction.
    The validation of the end-to-end system depends on this empirical claim from the user study.

pith-pipeline@v0.9.0 · 5758 in / 1258 out tokens · 43128 ms · 2026-05-19T08:11:29.712524+00:00 · methodology

discussion (0)

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Reference graph

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