Reproduce “Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions” on OpenResearch

About this paper

Junzhang Liu, Zhecan Wang, Hammad Ayyubi, Haoxuan You, Chris Thomas, Rui Sun, Shih-Fu Chang, Kai-Wei Chang

Researchers introduce a framework addressing multimodal situations with insufficient event-specific context, incorporating a probabilistic context selection module for leveraging available context and a Context-AwaRe Abstention (CARA) detector to prevent baseless predictions. The CARA detector improves VLM accuracy and exhibits strong zero-shot generalization across various vision-language benchmarks by identifying samples where crucial context is absent.

Key insights

• The selective integration of *textual* context proves more beneficial for VLM performance than visual or multimodal context, suggesting that carefully curated textual cues are crucial for resolving temporal ambiguities.
• A specialized abstention detector, pseudo-labeled by comparing the behavior of context-aware and vanilla VLMs, can effectively identify context insufficiency without requiring explicit context during inference, enabling robust generalization.
• The problem of insufficient event-specific context is a widespread systemic issue across multiple established VLU benchmarks, and a generalizable abstention mechanism can significantly enhance overall model reliability and benchmark integrity by reducing baseless predictions.

Summary by alphaXiv.