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REVIEW 2 major objections 1 minor 60 references

FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval

T0 review · 2 major / 1 minor · reviewed 2026-07-03 · grok-4.3

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Pith's one-line read FlowCIR casts zero-shot composed image retrieval as conditional semantic transport learned by flow matching on fixed vision-language model embeddings.

desk verdict FlowCIR swaps textual inversion for a small conditional flow-matching transport on frozen VLM embeddings and adds an inference heuristic for negation, but the abstract supplies no equations, ablations, or error breakdowns to confirm the transport actually recovers the claimed semantics. read the letter →

arxiv 2607.02284 v1 pith:S5CORFGS submitted 2026-07-02 cs.CV

Zhenqi He , Ziqi Jiang , Yuanpei Liu , Yanghao Wang , Teng Wang , Long Chen This is my paper · ORCID
classification cs.CV
keywords zero-shotcomposedimageretrievalflowmatchingsemantictransportvision-languagemodelsnegationhandlingconditional
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that converting a reference image and text instruction into a target query can be done by training a transport field that moves the instruction embedding toward the correct target embedding, conditioned on the reference. This replaces the textual-inversion step used in earlier methods, which the authors view as lossy for fine details. Because the transport module trains only on pre-extracted embeddings and leaves the encoders untouched, the approach requires far less compute than inversion-based training. The work further introduces an inference-time correction that steers away from negated concepts when the instruction contains removal or negation language.

What carries the argument

Conditional flow matching transport field that maps the instruction representation toward a target-aligned query embedding conditioned on the reference image.

What would settle it

A controlled experiment showing that FlowCIR retrieval accuracy on standard benchmarks drops below that of a textual-inversion baseline when both methods receive identical training compute and the same pre-trained encoders.

Watch

Extended reading notes

Core claim

Zero-shot composed image retrieval is reformulated as learning a conditional flow-matching transport field that maps an instruction representation, given the reference image, directly to a target-aligned query embedding; the resulting lightweight module produces competitive retrieval accuracy on standard benchmarks while using roughly ten times fewer training resources than textual-inversion baselines and incorporates a Multi-Negative Steering procedure to offset vision-language model weaknesses on negation.

Load-bearing premise

A lightweight transport module trained solely on fixed pre-extracted vision-language model embeddings can capture the fine-grained semantics needed for accurate target retrieval without any encoder updates.

Editorial extensions

If this is right

  • The method reaches strong performance on existing CIR benchmarks without requiring domain-specific triplet annotations.
  • Training cost is reduced by a factor of roughly ten compared with prior textual-inversion pipelines.
  • Multi-Negative Steering at inference improves results on queries that contain negation or removal instructions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same transport formulation could be tested on other vision-language tasks that currently rely on token inversion or simple concatenation for composition.
  • Because the approach never updates the underlying encoders, it may allow reuse of the same transport module across different vision-language model backbones.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper proposes FlowCIR for zero-shot composed image retrieval (ZS-CIR), framing the task as conditional semantic transport via flow matching. A lightweight transport module is trained on fixed pre-extracted VLM embeddings to map an instruction representation (conditioned on a reference image) to a target-aligned query embedding, without updating encoders or using task-specific triplets. It claims this yields competitive performance on standard CIR benchmarks while requiring roughly 10× fewer training resources than textual-inversion baselines, and introduces an inference-only Multi-Negative Steering heuristic to address VLM limitations on negation/removal.

Significance. If the performance and efficiency claims hold, the work offers a paradigm shift from textual inversion to flow-based transport on frozen embeddings, potentially lowering the barrier for ZS-CIR research. The explicit identification of negation as a VLM failure mode and the proposed mitigation are constructive contributions.

major comments (2)
  1. [Abstract / Method] Abstract and method description: The central claim that a small conditional flow-matching network trained solely on fixed VLM embeddings can accurately recover fine-grained target semantics (including directional composition) is load-bearing, yet the paper itself identifies negation/removal as a major VLM failure mode and resorts to a separate inference-time heuristic; this indicates the learned transport field may not fully compensate for information lost in the embedding space without additional supervision or adaptation.
  2. [Abstract] Abstract: The efficiency claim of 'roughly 10× fewer training resources' is presented without concrete metrics (e.g., parameter count of the transport module, GPU-hours, epochs, or side-by-side comparison tables), which is required to substantiate the load-bearing advantage over textual-inversion approaches.
minor comments (1)
  1. [Abstract] The abstract would benefit from a one-sentence description of the specific conditional flow-matching objective or network architecture to clarify how the transport field is parameterized.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential paradigm shift offered by FlowCIR. We address each major comment below with point-by-point responses.

read point-by-point responses

  1. Referee: [Abstract / Method] Abstract and method description: The central claim that a small conditional flow-matching network trained solely on fixed VLM embeddings can accurately recover fine-grained target semantics (including directional composition) is load-bearing, yet the paper itself identifies negation/removal as a major VLM failure mode and resorts to a separate inference-time heuristic; this indicates the learned transport field may not fully compensate for information lost in the embedding space without additional supervision or adaptation.

    Authors: We agree that negation and removal constitute a notable VLM limitation, which is why the manuscript explicitly identifies this failure mode and introduces Multi-Negative Steering as a targeted inference-time mitigation. The conditional flow-matching transport is trained to learn directional semantic mappings on the fixed embeddings for general compositional instructions, and benchmark results indicate it recovers target semantics effectively in most cases. The steering heuristic specifically addresses residual negation handling issues that are not fully resolved in the VLM embedding space. We will revise the abstract and method sections to more clearly separate the scope of the learned transport from the additional steering strategy and to discuss this distinction as a limitation. revision: partial

  2. Referee: [Abstract] Abstract: The efficiency claim of 'roughly 10× fewer training resources' is presented without concrete metrics (e.g., parameter count of the transport module, GPU-hours, epochs, or side-by-side comparison tables), which is required to substantiate the load-bearing advantage over textual-inversion approaches.

    Authors: We acknowledge that the efficiency claim requires concrete supporting metrics to be fully substantiated. In the revised manuscript we will add explicit details on the transport module's parameter count, training epochs, approximate GPU-hours, and a side-by-side resource comparison against textual-inversion baselines. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation is self-contained

full rationale

The paper defines FlowCIR as a conditional flow-matching transport module trained on fixed pre-extracted VLM embeddings to map reference+instruction to target-aligned queries, with an added inference-time Multi-Negative Steering heuristic. All load-bearing steps (embedding extraction, flow training, and benchmark evaluation) operate on external VLM features and standard CIR datasets without any reduction of the reported performance metrics to quantities defined by the fitted parameters themselves or to self-citations. The efficiency claim (10× fewer resources) follows directly from the architectural choice of freezing encoders rather than from any definitional equivalence. No self-definitional, fitted-input-as-prediction, or uniqueness-imported patterns appear in the provided text.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only review supplies no concrete free parameters, axioms, or invented entities; the transport field and Multi-Negative Steering are presented at the level of high-level technique names without further decomposition.

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Cite this review

Pith. "Pith review of FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval." pith.science (2026). https://pith.science/paper/S5CORFGS

@misc{pith2026260702284,
  author       = {Pith},
  title        = {Pith review of: FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S5CORFGS}},
  note         = {Machine review of arXiv:2607.02284}
}
abstract

Zero-shot composed image retrieval (ZS-CIR) aims to retrieve a target image by editing a reference image with a natural-language instruction, without relying on domain-specific annotated triplets. Most existing ZS-CIR methods rely on textual inversion to translate the reference image into pseudo-text tokens and then compose them with the instruction via simple concatenation in the text space, which can be lossy and brittle for fine-grained semantics. In this work, we propose a new paradigm, namely FlowCIR, that casts ZS-CIR as conditional semantic transport between reference and target embeddings. Leveraging \emph{conditional flow matching}, our model learns a lightweight transport field that maps the instruction representation toward a target-aligned query embedding conditioned on the reference image. Since FlowCIR operates on pre-extracted VLM embeddings and trains only a small transport module without updating the image or text encoder, it offers a computationally efficient training protocol compared with prior textual-inversion-based approaches. The resulting framework is training-efficient, requiring roughly $10\times$ fewer training resources than prior textual-inversion-based approaches. We further identify negation and removal as a major failure mode of VLM-based composition. To address this, we propose an inference-only Multi-Negative Steering strategy that steers a negation-containing relative instruction away from its negated semantics, mitigating the limited negation handling of VLMs and improving robustness on negation-heavy queries. Extensive experiments on standard CIR benchmarks demonstrate that FlowCIR achieves strong and competitive performance compared with recent ZS-CIR methods.

Figures

Figures reproduced from arXiv: 2607.02284 by the authors.

Figure 1
Figure 1. (a) Illustration of Composed Image Retrieval. (b) Prior methods are composed by textual inversion and token-level fusion in text space. (c) FlowCIR composes via conditional flow matching to produce a target-oriented retrieval query. images that match the intended edit (see [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Performance on CIRCO (mAP5) and CIRR (Recall1) with training hours (in single GPU) as bubble size. Recent zero-shot CIR methods [4, 19, 44, 47] largely build on textual inversion over well-aligned vision–language mod￾els [30,41], effectively reducing cross-modal composition to a text-only manipulation problem. Concretely, they learn a projec￾tor to transfer the reference image Ir into a small set of pseudo-text toke… view at source ↗
Figure 3
Figure 3. Framework overview of FlowCIR. (a) In training, FlowCIR learns a condi￾tional flow-matching transport from relative-instruction embeddings to target-oriented text embeddings under the reference-image condition, together with top-K hard￾negative retrieval supervision. (b) In inference, an inference-only Multi-Negative Steer￾ing module adjusts negation-containing instructions before one-step transport for target image… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Qualitative retrieval results illustrating the effect of reference-conditioned trans￾port and Multi-Negative Steering. Predicted targets are marked with for correct re￾trievals and for incorrect ones. pose; when the reference is a fluffy white dog, the retrieved images…

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Works this paper leans on

60 extracted references · 60 canonical work pages

  1. [1]

    arXiv preprint arXiv:2405.02951 (2024)

    Agnolucci, L., Baldrati, A., Bertini, M., Del Bimbo, A.: isearle: Improving textual inversion for zero-shot composed image retrieval. arXiv preprint arXiv:2405.02951 (2024)

    work page arXiv 2024

  2. [2]

    In: ICLR (2023)

    Albergo, M.S., Vanden-Eijnden, E.: Building normalizing flows with stochastic interpolants. In: ICLR (2023)

    work page 2023

  3. [3]

    In: CVPR (2025) 16 He et al

    Alhamoud, K., Alshammari, S., Tian, Y., Li, G., Torr, P.H., Kim, Y., Ghassemi, M.: Vision-language models do not understand negation. In: CVPR (2025) 16 He et al

    work page 2025

  4. [4]

    In: ICCV (2023)

    Baldrati, A., Agnolucci, L., Bertini, M., Del Bimbo, A.: Zero-shot composed image retrieval with textual inversion. In: ICCV (2023)

    work page 2023

  5. [5]

    In: CVPR (2022)

    Baldrati, A., Bertini, M., Uricchio, T., Del Bimbo, A.: Effective conditioned and composed image retrieval combining clip-based features. In: CVPR (2022)

    work page 2022

  6. [6]

    IJCV (2025)

    Bogensperger, L., Narnhofer, D., Falk, A., Schindler, K., Pock, T.: Flowsdf: Flow matching for medical image segmentation using distance transforms. IJCV (2025)

    work page 2025

  7. [7]

    In: ICCV (2025)

    Byun, J., Jeong, S., Kim, W., Chun, S., Moon, T.: An efficient post-hoc framework for reducing task discrepancy of text encoders for composed image retrieval. In: ICCV (2025)

    work page 2025

  8. [8]

    arXiv preprint arXiv:2305.15241 (2023)

    Chen, H., Dong, Y., Wang, Z., Yang, X., Duan, C., Su, H., Zhu, J.: Robust classi- fication via a single diffusion model. arXiv preprint arXiv:2305.15241 (2023)

    work page arXiv 2023

Show all 60 references
  1. [9]

    In: NeurIPS (2023)

    Clark, K., Jaini, P.: Text-to-image diffusion models are zero shot classifiers. In: NeurIPS (2023)

    2023

  2. [10]

    this is my unicorn, fluffy

    Cohen, N., Gal, R., Meirom, E.A., Chechik, G., Atzmon, Y.: “this is my unicorn, fluffy”: Personalizing frozen vision-language representations. In: ECCV (2022)

    2022

  3. [11]

    In: ICLR (2022)

    Delmas, G., Sampaio de Rezende, R., Csurka, G., Larlus, D.: Artemis: Attention- based retrieval with text-explicit matching and implicit similarity. In: ICLR (2022)

    2022

  4. [12]

    In: ICLR (2024)

    Du, Y., Wang, M., Zhou, W., Hui, S., Li, H.: Image2sentence based asymmetrical zero-shot composed image retrieval. In: ICLR (2024)

    2024

  5. [13]

    In: CVPR (2025)

    Duan, S., Sun, Y., Peng, D., Liu, Z., Song, X., Hu, P.: Fuzzy multimodal learning for trusted cross-modal retrieval. In: CVPR (2025)

    2025

  6. [14]

    In: ICML (2024)

    Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al.: Scaling rectified flow transformers for high-resolution image synthesis. In: ICML (2024)

    2024

  7. [15]

    arXiv preprint arXiv:2403.12803 (2024)

    Fu, Y., Chen, C., Qiao, Y., Yu, Y.: Dreamda: Generative data augmentation with diffusion models. arXiv preprint arXiv:2403.12803 (2024)

    2024

  8. [16]

    In: NeurIPS (2025)

    Geng,Z.,Deng,M.,Bai,X.,Kolter,J.Z.,He,K.:Meanflowsforone-stepgenerative modeling. In: NeurIPS (2025)

    2025

  9. [17]

    In: CVPR (2022)

    Goenka, S., Zheng, Z., Jaiswal, A., Chada, R., Wu, Y., Hedau, V., Natarajan, P.: Fashionvlp: Vision language transformer for fashion retrieval with feedback. In: CVPR (2022)

    2022

  10. [18]

    TMLR (2024)

    Gu, G., Chun, S., Kim, W., Jun, H., Kang, Y., Yun, S.: Compodiff: Versatile composed image retrieval with latent diffusion. TMLR (2024)

    2024

  11. [19]

    In: CVPR (2024)

    Gu, G., Chun, S., Kim, W., Kang, Y., Yun, S.: Language-only training of zero-shot composed image retrieval. In: CVPR (2024)

    2024

  12. [20]

    In: AAAI (2025)

    Gui, M., Schusterbauer, J., Prestel, U., Ma, P., Kotovenko, D., Grebenkova, O., Baumann, S.A., Hu, V.T., Ommer, B.: Depthfm: Fast generative monocular depth estimation with flow matching. In: AAAI (2025)

    2025

  13. [21]

    In: ICCV (2025)

    He, J., Yu, Q., Liu, Q., Chen, L.C.: Flowtok: Flowing seamlessly across text and image tokens. In: ICCV (2025)

    2025

  14. [22]

    In: CVPR (2026)

    He, Z., Li, L., Chen, L.: Flowcomposer: Composable flows for compositional zero- shot learning. In: CVPR (2026)

    2026

  15. [23]

    NeurIPS (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. NeurIPS (2020)

    2020

  16. [24]

    In: ICLR (2025)

    Hui, M., Yang, S., Zhao, B., Shi, Y., Wang, H., Wang, P., Zhou, Y., Xie, C.: Hq- edit: A high-quality dataset for instruction-based image editing. In: ICLR (2025)

    2025

  17. [25]

    In: CVPR (2024)

    Islam, K., Zaheer, M.Z., Mahmood, A., Nandakumar, K.: Diffusemix: Label- preserving data augmentation with diffusion models. In: CVPR (2024)

    2024

  18. [26]

    In: ICLR (2026) FlowCIR 17

    Jiang, Z., Wang, Y., Chen, L.: Exploring cross-modal flows for few-shot learning. In: ICLR (2026) FlowCIR 17

    2026

  19. [27]

    In: ICLR (2024)

    Karthik, S., Roth, K., Mancini, M., Akata, Z.: Vision-by-language for training-free compositional image retrieval. In: ICLR (2024)

    2024

  20. [28]

    In: CVPR (2025)

    Koh, G., Oh, H.J., Noh, J., Jeong, W.K.: Synthetic data augmentation using pre- trained diffusion models for long-tailed food image classification. In: CVPR (2025)

    2025

  21. [29]

    In: ICCV (2023)

    Li, A.C., Prabhudesai, M., Duggal, S., Brown, E., Pathak, D.: Your diffusion model is secretly a zero-shot classifier. In: ICCV (2023)

    2023

  22. [30]

    In: ICML (2022)

    Li, J., et al.: Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation. In: ICML (2022)

    2022

  23. [31]

    In: ICML (2026)

    Li, L., Jiang, Z., Ye, G., He, Z., Li, J., Xiao, J., Cheng, K.T., Chen, L.: Path- decoupled hyperbolic flow matching for few-shot adaptation. In: ICML (2026)

    2026

  24. [32]

    In: NeurIPS (2024)

    Li, T., Tian, Y., Li, H., Deng, M., He, K.: Autoregressive image generation without vector quantization. In: NeurIPS (2024)

    2024

  25. [33]

    In: ICML (2024)

    Li, W., Fan, H., Wong, Y., Yang, Y., Kankanhalli, M.S.: Improving context under- standing in multimodal large language models via multimodal composition learn- ing. In: ICML (2024)

    2024

  26. [34]

    In: ICLR (2023)

    Lipman, Y., Chen, R.T.Q., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling. In: ICLR (2023)

    2023

  27. [35]

    In: CVPR (2025)

    Liu, Q., Yin, X., Yuille, A., Brown, A., Singh, M.: Flowing from words to pixels: A noise-free framework for cross-modality evolution. In: CVPR (2025)

    2025

  28. [36]

    In: ICCV (2025)

    Liu, X., Pu, N., Zheng, H., Li, W., Sebe, N., Zhong, Z.: Generate, refine, and encode: Leveraging synthesized novel samples for on-the-fly fine-grained category discovery. In: ICCV (2025)

    2025

  29. [37]

    In: ICLR (2023)

    Liu, X., Gong, C., Liu, Q.: Flow straight and fast: Learning to generate and transfer data with rectified flow. In: ICLR (2023)

    2023

  30. [38]

    In: CVPR (2021)

    Liu, Z., Rodriguez-Opazo, C., Teney, D., Gould, S.: Image retrieval on real-life images with pre-trained vision-and-language models. In: CVPR (2021)

    2021

  31. [39]

    In: ACCV (2024)

    Ning, W., Chang, D., Tong, Y., He, Z., Liang, K., Ma, Z.: Hierarchical prompting for diffusion classifiers. In: ACCV (2024)

    2024

  32. [40]

    arXiv preprint arXiv:2412.12594 (2024)

    Qi, Z., Liu, B., Zhang, S., Li, B., Xu, Z., Xiong, H., Xie, Z.: A simple and efficient baseline for zero-shot generative classification. arXiv preprint arXiv:2412.12594 (2024)

    2024

  33. [41]

    In: ICML (2021)

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML (2021)

    2021

  34. [42]

    arXiv preprint arXiv:2511.12331 (2025)

    Ranjbar, S.K., Alhamoud, K., Ghassemi, M.: Spacevlm: Sub-space modeling of negation in vision-language models. arXiv preprint arXiv:2511.12331 (2025)

    2025

  35. [43]

    In: CVPR (2022)

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)

    2022

  36. [44]

    In: CVPR (2023)

    Saito, K., Sohn, K., Zhang, X., Li, C.L., Lee, C.Y., Saenko, K., Pfister, T.: Pic2word: Mapping pictures to words for zero-shot composed image retrieval. In: CVPR (2023)

    2023

  37. [45]

    In: ICCV (2025)

    Sun, Z., Jing, D., Lu, Z.: Cotmr: Chain-of-thought multi-scale reasoning for training-free zero-shot composed image retrieval. In: ICCV (2025)

    2025

  38. [46]

    In: AAAI (2026)

    Tang, H., Wang, J., Zhao, M., Meng, G., Luo, R., Chen, L., Xia, S.T.: Heteroge- neous uncertainty-guided composed image retrieval with fine-grained probabilistic learning. In: AAAI (2026)

    2026

  39. [47]

    In: AAAI (2024)

    Tang, Y., Yu, J., Gai, K., Zhuang, J., Xiong, G., Hu, Y., Wu, Q.: Context-i2w: Mapping images to context-dependent words for accurate zero-shot composed im- age retrieval. In: AAAI (2024)

    2024

  40. [48]

    In: CVPR (2019) 18 He et al

    Vo, N., Jiang, L., Sun, C., Murphy, K., Li, L.J., Fei-Fei, L., Hays, J.: Composing text and image for image retrieval-an empirical odyssey. In: CVPR (2019) 18 He et al

    2019

  41. [49]

    In: CVPR (2025)

    Wang, L., Ao, W., Boddeti, V.N., Lim, S.N.: Generative zero-shot composed image retrieval. In: CVPR (2025)

    2025

  42. [50]

    Wang, Y., Chen, H., Liu, J., He, Z., Liu, R., Wang, Z., Chen, L.: Lisa: Likelihood score alignment for visual-condition controllable generation (2026)

    2026

  43. [51]

    In: CVPR (2025)

    Wang, Y., Chen, L.: Inversion circle interpolation: Diffusion-based image augmen- tation for data-scarce classification. In: CVPR (2025)

    2025

  44. [52]

    In: NeurIPS (2025)

    Wang, Y., Chen, L.: Noise matters: Optimizing matching noise for diffusion clas- sifiers. In: NeurIPS (2025)

    2025

  45. [53]

    arXiv preprint arXiv:2510.06139 (2025)

    Wang, Z., Jiang, D., Li, L., Dang, S., Li, C., Yang, H., Dai, G., Wang, M., Wang, J.: Deforming videos to masks: Flow matching for referring video segmentation. arXiv preprint arXiv:2510.06139 (2025)

    2025

  46. [54]

    In: CVPR (2021)

    Wu,H.,Gao,Y.,Guo,X.,Al-Halah,Z.,Rennie,S.,Grauman,K.,Feris,R.:Fashion iq: A new dataset towards retrieving images by natural language feedback. In: CVPR (2021)

    2021

  47. [55]

    In: Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval (2024)

    Yang, Z., Xue, D., Qian, S., Dong, W., Xu, C.: Ldre: Llm-based divergent reasoning and ensemble for zero-shot composed image retrieval. In: Proceedings of the 47th International ACM SIGIR conference on research and development in information retrieval (2024)

    2024

  48. [56]

    In: CVPR (2024)

    Yue, Z., Zhou, P., Hong, R., Zhang, H., Sun, Q.: Few-shot learner parameterization by diffusion time-steps. In: CVPR (2024)

    2024

  49. [57]

    In: 2025 International Conference on Content-Based Multimedia Indexing (CBMI)

    Zhan, G., Liu, Y., Han, K., Xie, W., Zisserman, A.: Elip: Enhanced visual- language foundation models for image retrieval. In: 2025 International Conference on Content-Based Multimedia Indexing (CBMI). pp. 1–8. IEEE (2025)

    2025

  50. [58]

    In: ICML (2024)

    Zhang, K., Luan, Y., Hu, H., Lee, K., Qiao, S., Chen, W., Su, Y., Chang, M.W.: MagicLens: Self-supervised image retrieval with open-ended instructions. In: ICML (2024)

    2024

  51. [59]

    Zhang, P., Zeng, G., Wang, T., Lu, W.: Tinyllama: An open-source small language model (2024)

    2024

  52. [60]

    In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2025)

    Zhao, J., Li, J., Lian, D., Sun, L., Lv, P.: Dualcir: Enhancing training-free com- posed image retrieval via dual-directional descriptions. In: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2025)

    2025

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