pith. sign in

arxiv: 2208.14649 · v7 · submitted 2022-08-31 · 💻 cs.CV

DetailCLIP: Injecting Image Details into CLIP's Feature Space

Zilun Zhang , Cuifeng Shen , Yuan Shen , Xinyu Zhou , Huixin Xiong , Tiancheng Zhao , Jianwei Yin This is my paper

Pith reviewed 2026-05-24 11:05 UTC · model grok-4.3

classification 💻 cs.CV
keywords CLIPhigh-resolution imagesfeature fusionimage retrievalremote sensingmulti-scale detailsvision-language modelssynthetic dataset
0
0 comments X

The pith

DetailCLIP generates one feature vector from high-resolution images that keeps multi-scale details while staying in CLIP's original semantic space.

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

The paper sets out to fix the loss of small details when high-resolution images are fed into CLIP models whose input size is fixed at 224 pixels. It does so by extracting CLIP features from many overlapping patches that together cover the whole image at every scale, then fusing those features into a single vector. The fusion is trained only with class-level text prompts and no pixel-level labels. A sympathetic reader would care because the resulting vector can be used directly for text-based retrieval of tiny objects, such as vehicles in satellite photos, without leaving the space where CLIP already works.

Core claim

DetailCLIP generates a single feature representation for a high-resolution image that retains image details from different scales while sharing the same semantic space as the original CLIP. This is achieved by a feature fusion model that relies on CLIP features extracted from a carefully designed image patch method, dubbed Complete Cover. This method ensures comprehensive coverage of objects across various scales and is weakly supervised by image-agnostic class prompted queries. The framework is shown to improve retrieval performance on both real-world remote-sensing data and a new controllable synthetic dataset called CLVER-DS.

What carries the argument

The Complete Cover patch method, which tiles the high-resolution image so that objects at every scale are fully covered by at least one patch, paired with a feature fusion model that merges the resulting CLIP vectors into one aligned representation.

If this is right

  • Image retrieval based on class prompts improves on both real remote-sensing images and the CLVER-DS synthetic set.
  • Small-scale targets such as vehicles and ships become retrievable without changing CLIP's semantic space.
  • The same fused vector can be used for any downstream task that already accepts standard CLIP features.
  • Controlled scale experiments on CLVER-DS allow direct measurement of how well details at each size are retained.

Where Pith is reading between the lines

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

  • The same patching-plus-fusion pattern could be tried on other vision-language models whose input size is also fixed.
  • If the weak supervision works, the method might extend to tasks that need fine detail but lack dense labels, such as medical or aerial image search.
  • One could test whether the fused vector still supports zero-shot classification on categories never seen during the fusion training.

Load-bearing premise

The fusion model can combine the patch features so that both fine details and CLIP semantics are preserved when the only training signal is class text prompts.

What would settle it

If retrieval accuracy for tiny objects on the CLVER-DS dataset shows no improvement when the fused high-resolution features are used instead of standard CLIP on downsampled images, the central claim does not hold.

Figures

Figures reproduced from arXiv: 2208.14649 by Cuifeng Shen, Huixin Xiong, Jianwei Yin, Tiancheng Zhao, Xinyu Zhou, Yuan Shen, Zilun Zhang.

Figure 1
Figure 1. Figure 1: Retrieval Results: CLIP Model vs. DetailCLIP Model. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of Patch Selection of Complete Cover. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The DetailCLIP framework with feature query proxy loss is illustrated in the figure. Here, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Illustration for different datasets. Existing datasets have different flaws for the retrieval by classname task. COCO [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Retrieval performance under different patch [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Retrieval result of different approach. CLEVR-DS-S. These results demonstrate that our patch-cc method generates superior patches compared to the patch-grid approach. Second, the "Patch-obj" method generates patches by cropping objects from the image using their bounding boxes. We use the "Patch-obj" method to generate bounding box patches and select the one most similar to the target as the retrieval resu… view at source ↗
Figure 7
Figure 7. Figure 7: After applying DetailCLIP, we achieve 100x #Patch [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distribution of retrieval results for positive & nega [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Patch numbers for different sidelengths when [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Our synthetic CLEVR-DS dataset illustrated above, [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Recall for retrieval and DetailCLIP models with different [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Retrieval Result Visualization: Ground truth images for each query are highlighted with blue frames, while other [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
read the original abstract

Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224), subtle details are lost in the feature representation if we input high-resolution images (e.g., 2240). Our proposed framework addresses this issue by generating a single feature representation for a high-resolution image that retains image details from different scales while sharing the same semantic space as the original CLIP. An application scenario is remote sensing text-image retrieval, where targets (e.g., vehicles and ships) often appear at tiny scales. To achieve this, we develop a feature fusion model that relies on CLIP features extracted from a carefully designed image patch method, dubbed Complete Cover. This method ensures comprehensive coverage of objects across various scales and is weakly supervised by image-agnostic class prompted queries. We evaluate our framework's performance using real-world and synthetic datasets, demonstrating significant improvements in image retrieval tasks based on class prompted queries. To further showcase our framework's capability in detail retrieval, we introduce a CLEVR-like synthetic dataset, named CLVER-DS. This fully annotated dataset offers a controllable object scale, allowing for a more thorough examination of our approach's effectiveness.Our code is publicly available at https://github.com/zilunzhang/DetailCLIP

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

3 major / 2 minor

Summary. The manuscript proposes DetailCLIP to produce a single CLIP-compatible feature vector from high-resolution images that retains fine details across scales. It extracts patch features via a Complete Cover method, fuses them with a learned model, and trains under weak supervision from image-agnostic class text prompts. The framework targets remote-sensing retrieval of small objects and is evaluated on real-world data plus a new controllable-scale synthetic dataset (CLVER-DS); code is released publicly.

Significance. If the central claim holds, the work would allow detail-preserving retrieval inside the original CLIP space without retraining the vision encoder, which is practically useful for high-resolution remote-sensing tasks. Public code and the introduction of CLVER-DS are concrete strengths that aid reproducibility.

major comments (3)
  1. [Method / training procedure] The training objective (described in the method section) uses only image-agnostic class prompts as supervision. Because the prompt is identical for every patch and every scale, the loss supplies no explicit signal that distinguishes fine-grained or scale-specific content; the fusion network can therefore satisfy the class-level retrieval metric while discarding the very details the paper claims to retain.
  2. [Experiments / CLVER-DS evaluation] Evaluation relies on class-prompted retrieval metrics. To support the claim that scale-specific details are preserved, the experiments must demonstrate gains on queries or annotations that require fine-grained information (e.g., object size, count, or spatial relations) rather than class identity alone; the current protocol does not isolate this property.
  3. [Abstract and §4] No quantitative numbers (recall@K, mAP, etc.) or baseline comparisons appear in the abstract, and the strength of the reported improvements cannot be assessed without the specific tables or figures that would allow effect-size evaluation.
minor comments (2)
  1. [Abstract] Abstract contains the typo 'CILP' (should be 'CLIP').
  2. [Abstract and dataset description] Dataset name alternates between 'CLEVR-like' and 'CLVER-DS'; consistent nomenclature would improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate where the manuscript will be revised.

read point-by-point responses

  1. Referee: [Method / training procedure] The training objective (described in the method section) uses only image-agnostic class prompts as supervision. Because the prompt is identical for every patch and every scale, the loss supplies no explicit signal that distinguishes fine-grained or scale-specific content; the fusion network can therefore satisfy the class-level retrieval metric while discarding the very details the paper claims to retain.

    Authors: We agree that the class-level, image-agnostic supervision provides no explicit per-patch or per-scale signal. The Complete Cover extraction and fusion architecture are intended to ensure that multi-scale patches contribute to the final feature, but the training objective itself does not enforce retention of fine details. We will add a clarifying paragraph in the method section acknowledging this limitation of weak supervision and include an ablation that isolates the contribution of multi-scale fusion versus single-scale inputs on small-object retrieval. revision: partial

  2. Referee: [Experiments / CLVER-DS evaluation] Evaluation relies on class-prompted retrieval metrics. To support the claim that scale-specific details are preserved, the experiments must demonstrate gains on queries or annotations that require fine-grained information (e.g., object size, count, or spatial relations) rather than class identity alone; the current protocol does not isolate this property.

    Authors: CLVER-DS provides full annotations for object scale, count, and spatial layout, which in principle support fine-grained queries. However, the reported experiments use only class-prompted retrieval. We will extend the evaluation section to include additional metrics and queries that directly test size, count, and spatial relations on CLVER-DS, thereby isolating the preservation of scale-specific details. revision: yes

  3. Referee: [Abstract and §4] No quantitative numbers (recall@K, mAP, etc.) or baseline comparisons appear in the abstract, and the strength of the reported improvements cannot be assessed without the specific tables or figures that would allow effect-size evaluation.

    Authors: We agree that the abstract should report key quantitative results. We will revise the abstract to include specific recall@K and mAP values together with the main baseline comparisons. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation is self-contained

full rationale

The paper describes a standard pipeline: extract CLIP features from Complete Cover patches of high-resolution images, train a fusion model under weak supervision from image-agnostic class prompts, and evaluate retrieval performance. No equations, fitted parameters renamed as predictions, or self-citation chains appear in the text. The central claim rests on empirical training and external CLIP features rather than any definitional reduction or imported uniqueness theorem. The approach is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract does not specify any free parameters, axioms, or new entities; full paper would be needed to identify them.

pith-pipeline@v0.9.0 · 5789 in / 923 out tokens · 28406 ms · 2026-05-24T11:05:53.926073+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · 9 internal anchors

  1. [1]

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al

    work page

  2. [2]

    Flamingo: a Visual Language Model for Few-Shot Learning

    Flamingo: a Visual Language Model for Few-Shot Learning. arXiv preprint arXiv:2204.14198 (2022)

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  3. [3]

    Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, and Stefano Soatto. 2022. X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks. arXiv preprint arXiv:2204.05626 (2022)

    work page arXiv 2022

  4. [4]

    ShapeNet: An Information-Rich 3D Model Repository

    Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianx- iong Xiao, Li Yi, and Fisher Yu. 2015. ShapeNet: An Information-Rich 3D Model Repository. Technical Report arXiv:1512.03012 [cs.GR]. Stanford University — Princeton University — Toyota Technological Institute...

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  5. [5]

    Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. [n. d.]. UNITER: UNiversal Image-TExt Representa- tion Learning. ([n. d.]). arXiv:1909.11740 http://arxiv.org/abs/1909.11740

    work page arXiv 1909

  6. [6]

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xi- aohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. CoRR abs/2010.11929 (2020). arXiv:2010.11929 https://arxiv.org/a...

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  7. [7]

    Gabriel Goh, Nick Cammarata †, Chelsea Voss †, Shan Carter, Michael Petrov, Ludwig Schubert, Alec Radford, and Chris Olah. 2021. Multimodal Neurons in Artificial Neural Networks. Distill (2021). https://doi.org/10.23915/distill.00030 https://distill.pub/2021/multimodal-neurons

    work page doi:10.23915/distill.00030 2021

  8. [8]

    Agrim Gupta, Piotr Dollar, and Ross Girshick. 2019. LVIS: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 5356–5364

    work page 2019

  9. [9]

    Le, Yunhsuan Sung, Zhen Li, and Tom Duerig

    Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, and Tom Duerig. [n. d.]. Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision. ([n. d.]). arXiv:2102.05918 http://arxiv.org/abs/2102.05918

    work page arXiv

  10. [10]

    Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2901–2910

    work page 2017

  11. [11]

    Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. [n. d.]. MDETR – Modulated Detection for End-to-End Multi- Modal Understanding. ([n. d.]). arXiv:2104.12763 http://arxiv.org/abs/2104.12763

    work page arXiv

  12. [12]

    Wonjae Kim, Bokyung Son, and Ildoo Kim. [n. d.]. ViLT: Vision-and- Language Transformer Without Convolution or Region Supervision. ([n. d.]). arXiv:2102.03334 http://arxiv.org/abs/2102.03334

    work page arXiv

  13. [13]

    Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al

    work page

  14. [14]

    International journal of computer vision 123, 1 (2017), 32–73

    Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision 123, 1 (2017), 32–73

    work page 2017

  15. [15]

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. BLIP-2: Boot- strapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. arXiv:2301.12597 [cs.CV]

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  16. [16]

    Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. [n. d.]. VisualBERT: A Simple and Performant Baseline for Vision and Language. ([n. d.]). arXiv:1908.03557 http://arxiv.org/abs/1908.03557

    work page internal anchor Pith review Pith/arXiv arXiv 1908

  17. [17]

    Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. 2022. Grounded Language-Image Pre-training. In CVPR

    work page 2022

  18. [18]

    Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. 2021. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208 (2021)

    work page arXiv 2021

  19. [19]

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014. Microsoft coco: Common objects in context. In European conference on computer vision . Springer, 740–755

    work page 2014

  20. [20]

    Wagner, and Saining Xie

    Norman Mu, Alexander Kirillov, David A. Wagner, and Saining Xie. 2021. SLIP: Self-supervision meets Language-Image Pre-training.CoRR abs/2112.12750 (2021). arXiv:2112.12750 https://arxiv.org/abs/2112.12750

    work page arXiv 2021

  21. [21]

    Michal Nazarczuk and Krystian Mikolajczyk. 2020. SHOP-VRB: A Visual Reason- ing Benchmark for Object Perception. International Conference on Robotics and Automation (ICRA) (2020)

    work page 2020

  22. [22]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. [n. d.]. Learning Transferable Visual Models From Natural Language Supervision. ([n. d.]). arXiv:2103.00020 http: //arxiv.org/abs/2103.00020

    work page internal anchor Pith review Pith/arXiv arXiv

  23. [23]

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Rad- ford, Mark Chen, and Ilya Sutskever. [n. d.]. Zero-Shot Text-to-Image Generation. ([n. d.]). arXiv:2102.12092 http://arxiv.org/abs/2102.12092

    work page internal anchor Pith review Pith/arXiv arXiv

  24. [24]

    ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei. 2014. ImageNet Large Scale Visual Recognition Challenge. CoRR abs/1409.0575 (2014). arXiv:1409.0575 http://arxiv.org/abs/1409. 0575

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  25. [25]

    Konstantin Schall, Kai Uwe Barthel, Nico Hezel, and Klaus Jung. 2021. GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval. CoRR abs/2111.13122 (2021). arXiv:2111.13122 https://arxiv.org/abs/2111.13122

    work page arXiv 2021

  26. [26]

    vijishmadhavan. 2022. Crop-CLIP. https://github.com/vijishmadhavan/Crop- CLIP#Simple-App

    work page 2022

  27. [27]

    Chenyun Wu, Zhe Lin, Scott Cohen, Trung Bui, and Subhransu Maji. [n. d.]. PhraseCut: Language-based Image Segmentation in the Wild. ([n. d.]). arXiv:2008.01187 http://arxiv.org/abs/2008.01187

    work page arXiv 2008

  28. [28]

    Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. 2022. GroupViT: Semantic Segmentation Emerges from Text Supervision. arXiv preprint arXiv:2202.11094 (2022)

    work page arXiv 2022

  29. [29]

    Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiao- dan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. 2021. FILIP: Fine-grained Interactive Language-Image Pre-Training. arXiv preprint arXiv:2111.07783 (2021)

    work page arXiv 2021

  30. [30]

    Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. [n. d.]. Flo- rence: A New Foundation Model for Computer Vision. ...

    work page internal anchor Pith review Pith/arXiv arXiv

  31. [31]

    Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liu- nian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. 2021. RegionCLIP: Region-based Language-Image Pretraining. CoRR abs/2112.09106 (2021). arXiv:2112.09106 https://arxiv.org/abs/2112.09106

    work page arXiv 2021

  32. [32]

    Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2021. Learning to prompt for vision-language models. arXiv preprint arXiv:2109.01134 (2021). DetailCLIP: Injecting Image Details into CLIP’s Feature Space 31st ACM International Conference on Multimedia, 2023, Ottawa, Canada A APPENDIX A.1 The Effectiveness of Complete Cover Let us explore the c...

    work page arXiv 2021