I believe DDN is exceptionally well-suited to the “generative models for discriminative tasks” paradigm for object detection.
Much like DiffusionDet, which applies diffusion models to detection, DDN can adopt the same philosophy.
I expect DDN to offer several advantages over diffusion-based approaches:
- Single forward pass to obtain results, no iterative denoising required.
- If multiple samples are needed (e.g., for uncertainty estimation), DDN can directly produce multiple outputs in one forward pass.
- Easy to impose constraints during generation due to DDN's Zero-Shot Conditional Generation capability.
- DDN supports more efficient end-to-end optimization, thus more suitable for integration with discriminative models and reinforcement learning.
I believe DDN is exceptionally well-suited to the “generative models for discriminative tasks” paradigm for object detection.
Much like DiffusionDet, which applies diffusion models to detection, DDN can adopt the same philosophy. I expect DDN to offer several advantages over diffusion-based approaches: - Single forward pass to obtain results, no iterative denoising required. - If multiple samples are needed (e.g., for uncertainty estimation), DDN can directly produce multiple outputs in one forward pass. - Easy to impose constraints during generation due to DDN's Zero-Shot Conditional Generation capability. - DDN supports more efficient end-to-end optimization, thus more suitable for integration with discriminative models and reinforcement learning.
Yep, the mental model I have from a cursory read of the paper is "generative decision tree".