Focus on Local Regions for Query-based Object Detection
This work addresses a key bottleneck in object detection for computer vision applications, offering an incremental improvement over existing query-based methods.
The paper tackles slow convergence and suboptimal performance in query-based object detectors by proposing FoLR, a transformer-like architecture that focuses on local regions, achieving state-of-the-art results with improved convergence speed and computational efficiency.
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