Falcon Perception

Falcon Perception is a 0.6 billion-parameter early-fusion Transformer model designed for open-vocabulary grounding and segmentation, integrating image patches and text in a unified sequence with a hybrid attention mask. This approach aims to simplify perception systems by using a single backbone to handle both perception and language modeling, addressing the complexity issues associated with modular pipelines. Falcon Perception achieves a 68.0 Macro-F1 score on the SA-Co benchmark, outperforming SAM 3 in certain areas while identifying presence calibration as an improvement axis. The model's architecture emphasizes early fusion, hybrid attention, and efficient dense interfaces, allowing it to handle complex prompts and crowded scenes effectively. Additionally, Falcon OCR, a variant focused on document understanding, demonstrates strong performance on OCR benchmarks with a 0.3 billion-parameter design, offering high throughput and competitive accuracy. Both models illustrate the potential of early-fusion architectures to streamline tasks traditionally handled by more complex systems, suggesting a future direction focused on data, compute, and training signals rather than expanding pipeline complexity.