mlsanityData to 7 Oct 2026

How well does CLIP understand texture?

arXiv 2203.11449 ↗First posted 22 Mar 2022cs.CVcited by 7

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Abstract (arXiv)

We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification datasets; (2) represent compositional properties of texture such as red dots or yellow stripes on the Describable Texture in Detail(DTDD) dataset; and (3) aid fine-grained categorization of birds in photographs described by color and texture of their body parts.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 22 Mar 2022.

Builds on

15 of 23 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  5. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  6. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  8. 2013Describing Textures in the Wildcited by 2,110
  9. 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 903
  10. 2021Image Segmentation Using Text and Image Promptscited by 509
  11. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
  12. 2019UNITER: UNiversal Image-TExt Representation Learningcited by 225
3 more
  1. 2018Global Second-order Pooling Convolutional Networkscited by 66
  2. 2018Second-order Democratic Aggregationcited by 12
  3. 2020Describing Textures using Natural Languagecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2024Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Modelscited by 55
  2. 2023Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representationscited by 28
  3. 2024TexSliders: Diffusion-Based Texture Editing in CLIP Spacecited by 23
  4. 2026Taming Vision Priors for Data Efficient mmWave Channel Modelingcited by 2
  5. 2025Re-purposing SAM into Efficient Visual Projectors for MLLM-Based Referring Image Segmentationcited by 1
  6. 2025Accelerating Physical Property Reasoning for Augmented Visual Cognitioncited by 1
  7. 2025ADAM: Autonomous Discovery and Annotation Model using LLMs for Context-Aware Annotationscited by 0