  Query-based methods have garnered significant attention in object detection
since the advent of DETR, the pioneering query-based detector. However, these
methods face challenges like slow convergence and suboptimal performance.
Notably, self-attention in object detection often hampers convergence due to
its global focus. To address these issues, we propose FoLR, a transformer-like
architecture with only decoders. We improve the self-attention by isolating
connections between irrelevant objects that makes it focus on local regions but
not global regions. We also design the adaptive sampling method to extract
effective features based on queries' local regions from feature maps.
Additionally, we employ a look-back strategy for decoders to retain previous
information, followed by the Feature Mixer module to fuse features and queries.
Experimental results demonstrate FoLR's state-of-the-art performance in
query-based detectors, excelling in convergence speed and computational
efficiency.
  Index Terms: Local regions, Attention mechanism, Object detection

Computer Vision and Pattern Recognition