Visual Entailment is a reasoning task derived from textual entailment (NLI) — where the model must determine the logical relationship between an image (premise) and a sentence (hypothesis): whether the text is Entailed (true), Contradicted (false), or Neutral (unrelated) given the image.

What Is Visual Entailment?

• Definition: Classification of (Image, Text) pairs into {Entailment, Neutral, Contradiction}.
• Dataset: SNLI-VE is the most common benchmark.
• Example:
• Image: A dog running on grass.
• Hypothesis A: "An animal is outside." -> Entailment.
• Hypothesis B: "A cat is sitting." -> Contradiction.
• Hypothesis C: "The dog is chasing a ball." -> Neutral (not visible in image).

Why It Matters

• Grounded Truth: Formalizes the notion of "truthfulness" in captioning.
• Hallucination Detection: Used to verify if a model's generated caption is supported by the image pixels.
• Strict Logic: Forces precise understanding of quantifiers (all, some, none) and actions.

Visual Entailment is the logic gate of multimodal AI — serving as the foundational verification step for checking consistency between vision and language.