visual entailment

**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.