Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Computer Science > Computer Vision and Pattern Recognition

arXiv:2306.07969 (cs)
[Submitted on 13 Jun 2023]

Title:GeneCIS: A Benchmark for General Conditional Image Similarity

Authors:Sagar Vaze, Nicolas Carion, Ishan Misra
View a PDF of the paper titled GeneCIS: A Benchmark for General Conditional Image Similarity, by Sagar Vaze and 2 other authors
View PDF HTML (experimental)
Abstract:We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning methods, supervised or self-supervised, which learn a fixed embedding function and hence implicitly assume a single notion of similarity. For instance, models trained on ImageNet are biased towards object categories, while a user might prefer the model to focus on colors, textures or specific elements in the scene. In this paper, we propose the GeneCIS ('genesis') benchmark, which measures models' ability to adapt to a range of similarity conditions. Extending prior work, our benchmark is designed for zero-shot evaluation only, and hence considers an open-set of similarity conditions. We find that baselines from powerful CLIP models struggle on GeneCIS and that performance on the benchmark is only weakly correlated with ImageNet accuracy, suggesting that simply scaling existing methods is not fruitful. We further propose a simple, scalable solution based on automatically mining information from existing image-caption datasets. We find our method offers a substantial boost over the baselines on GeneCIS, and further improves zero-shot performance on related image retrieval benchmarks. In fact, though evaluated zero-shot, our model surpasses state-of-the-art supervised models on MIT-States. Project page at this https URL.
Comments: CVPR 2023 (Highlighted Paper). Project page at this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
Cite as: arXiv:2306.07969 [cs.CV]
  (or arXiv:2306.07969v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2306.07969
arXiv-issued DOI via DataCite

Submission history

From: Sagar Vaze [view email]
[v1] Tue, 13 Jun 2023 17:59:58 UTC (3,752 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled GeneCIS: A Benchmark for General Conditional Image Similarity, by Sagar Vaze and 2 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
license icon view license

Current browse context:

cs.CV
< prev   |   next >
new | recent | 2023-06
Change to browse by:
cs
cs.AI
cs.LG
cs.MM

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences