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About this paper
Bo Wan, Michael Tschannen, Yongqin Xian, Filip Pavetic, Ibrahim Alabdulmohsin, Xiao Wang, André Susano Pinto, Andreas Steiner and 2 more
Google DeepMind researchers introduce LocCa, a visual pretraining method that integrates location-aware tasks directly into a generative captioner architecture using natural language. The model achieves new state-of-the-art results on referring expression comprehension and improved performance on object detection, while maintaining strong capabilities across a wide range of holistic vision and vision-language tasks.
Key insights
- Natural language can serve as a flexible and powerful unified interface for diverse visual pretraining tasks, including precise spatial localization, by encoding coordinates as textual tokens.
- Integrating location-aware generative tasks (ARef, GCap) into a simple encoder-decoder framework effectively teaches fine-grained visual grounding and enhances object-centric understanding without complex, specialized components.
- A single, end-to-end generative model can successfully bridge the gap between holistic image understanding and precise object localization, achieving state-of-the-art results in both without compromise.
Summary by alphaXiv.
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