Visual Grounding With Region To Text Linking

via “visual grounding with region-to-text localization”

Microsoft's unified model for diverse vision tasks.

Unique: Grounds text phrases to image regions using the same seq2seq decoder that handles detection and captioning, treating grounding as a conditional generation task where text queries condition coordinate output

vs others: Simpler than ALBEF or BLIP-2 grounding (single model vs multi-stage) and more flexible than CLIP-based approaches, though with lower accuracy on fine-grained spatial reasoning compared to specialized grounding models

via “visual grounding with region-to-text linking”

* ⏫ 12/2023: [VideoPoet: A Large Language Model for Zero-Shot Video Generation (VideoPoet)](https://arxiv.org/abs/2312.14125)

Unique: Implements visual grounding as a text generation task within the unified sequence-to-sequence framework, enabling language-to-region mapping through the same interface as detection and captioning. Trained on grounding annotations from FLD-5B dataset.

vs others: Provides grounding without separate specialized models (e.g., ALBEF, BLIP) by leveraging unified architecture, reducing deployment complexity compared to ensemble approaches, though potentially at cost of grounding precision on specialized benchmarks.