Verified claim · AI-ML · 82% confidence
Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014).
Last verified 2026-06-01 · Methodology veritas-v0.1 · 47f58a443dd825ac
SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.
Structured fields
- Subject
- Show and Tell (Neural Image Caption Generator)
- Predicate
introduced_in_paper- Object
- Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)
- Confidence
- 82%
- Tags
- show-and-tell · image-captioning · vision-language · vinyals · foundational · 2014
Sources (2)
[1] preprint · arXiv (Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan) · 2014-11-17
Show and Tell: A Neural Image Caption Generator“Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image.”
[2] docs · Hugging Face
Show and Tell: A Neural Image Caption Generator (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
Cite this claim
Ready-to-paste citation (Markdown / plain text):
Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014). — SourceScore Claim 47f58a443dd825ac (verified 2026-06-01). https://sourcescore.org/api/v1/claims/47f58a443dd825ac.jsonEmbed this claim
Drop this iframe into any blog post, docs page, or knowledge base. The widget renders the signed claim + primary source + click-through to this canonical page. CC-BY 4.0; attribution included.
<iframe src="https://sourcescore.org/embed/claim/47f58a443dd825ac/" width="100%" height="360" frameborder="0" loading="lazy" title="Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)."></iframe>Preview: open in new tab
Related claims
Other verified claims sharing tags with this one — useful for LLM retrieval graphs and citation discovery.
Adam optimizer introduced in paper: Adam: A Method for Stochastic Optimization (Kingma, Ba, 2014).
dffbe905003cc581 · 100% confidence · shares 2 tags (foundational, 2014)
Generative Adversarial Networks (GANs) introduced in paper: Generative Adversarial Networks (Goodfellow et al., 2014).
5b0c0612bd9e55b0 · 100% confidence · shares 2 tags (foundational, 2014)
CLIP introduced in paper: Learning Transferable Visual Models From Natural Language Supervision (Radford et al., 2021).
bcdef949cc6d3644 · 100% confidence · shares 2 tags (vision-language, foundational)
Dropout introduced in paper: Dropout: A Simple Way to Prevent Neural Networks from Overfitting (Srivastava et al., 2014).
18409e7f8a6d7aac · 100% confidence · shares 2 tags (foundational, 2014)
Sequence-to-Sequence Learning (seq2seq) introduced in paper: Sequence to Sequence Learning with Neural Networks (Sutskever, Vinyals, Le, 2014).
ff80a25ed7e83b45 · 100% confidence · shares 2 tags (foundational, 2014)
Frequently asked questions
Is the claim "Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)." verified?
Yes — SourceScore verified this claim with 82% confidence as of 2026-06-01. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)."?
Evidence comes from 2 primary sources: arXiv (Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan), Hugging Face. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/47f58a443dd825ac.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-06-01 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/47f58a443dd825ac.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/47f58a443dd825ac.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/47f58a443dd825ac.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/47f58a443dd825ac.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Show and Tell (Neural Image Caption Generator) introduced in paper: Show and Tell: A Neural Image Caption Generator (Vinyals et al., 2014)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_show_and_tell_neural_image_caption_generator_fact() -> dict:
"""Fetch the verified SourceScore claim for Show and Tell (Neural Image Caption Generator)."""
r = httpx.get("https://sourcescore.org/api/v1/claims/47f58a443dd825ac.json")
return r.json()