Q$^2$: Quantization-Aware Gradient Balancing and Attention Alignment for Low-Bit Quantization

Researchers propose Q², a new framework that addresses gradient imbalance issues in quantization-aware training for complex visual tasks like object detection and image segmentation. The method achieves significant performance improvements (+2.5% mAP for object detection, +3.7% mDICE for segmentation) while introducing no inference-time overhead.