Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Computer Vision and Pattern Recognition

arXiv:2207.11100 (cs)
[Submitted on 22 Jul 2022 (v1), last revised 27 Jul 2022 (this version, v2)]

Title:Zero-Shot Video Captioning with Evolving Pseudo-Tokens

Authors:Yoad Tewel, Yoav Shalev, Roy Nadler, Idan Schwartz, Lior Wolf
View PDF
Abstract:We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model and the CLIP image-text matching model. The matching score is used to steer the language model toward generating a sentence that has a high average matching score to a subset of the video frames. Unlike zero-shot image captioning methods, our work considers the entire sentence at once. This is achieved by optimizing, during the generation process, part of the prompt from scratch, by modifying the representation of all other tokens in the prompt, and by repeating the process iteratively, gradually improving the specificity and comprehensiveness of the generated sentence. Our experiments show that the generated captions are coherent and display a broad range of real-world knowledge. Our code is available at: this https URL
Comments: preprint
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2207.11100 [cs.CV]
  (or arXiv:2207.11100v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.11100

Submission history

From: Yoad Tewel [view email]
[v1] Fri, 22 Jul 2022 14:19:31 UTC (12,668 KB)
[v2] Wed, 27 Jul 2022 21:52:21 UTC (12,679 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2022-07
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

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?)
Papers with Code (What is Papers with Code?)
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?)

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?)