From Cause to Effect: Generating Intermediate Event Chains in Videos
We introduce Causal Chain Generation (CCG), a novel task where models have to generate the sequence of intermediate events connecting a cause and an effect.
ProofPaper ↗
Key points
- Video-Language Models (VLMs) excel at visual recognition but struggle with causal reasoning.
- We accompany this task with two complementary metrics: SODA-Embedding, which measures the semantic matching of a generated chain against a reference, and Causal-QA, which probes the chain's causal coherence by asking whether it contains the specific causal facts needed to reconstruct the narrative.
- We provide two test sets: SF20K-CC, a manually annotated diagnostic on 20 short films, and CMD-CC, a movie clip benchmark derived from audio descriptions.
- Our analysis reveals that state-of-the-art VLMs struggle with narrative causality understanding, frequently hallucinating or omitting key events.
Sources (1)
- [1]From Cause to Effect: Generating Intermediate Event Chains in VideosarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 12:30 PM
We introduce Causal Chain Generation (CCG), a novel task where models have to generate the sequence of intermediate events connecting a cause and an effect.
Video-Language Models (VLMs) excel at visual recognition but struggle with causal reasoning.
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