Title: HashPose: Memory-Efficient Human Pose Estimation via Progressive Hash Codes
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Classification, Pose Estimation, Memory-Efficient, Hash Codes, Progressive Refinement

Abstract: Real-time human pose estimation on edge devices demands memory-efficient, high-precision methods. The dominant heatmap approaches, however, scale quadratically with input size, waste computation on background regions, and require slow post-processing. We propose HashPose, a framework replacing heatmaps with progressive hash codes: each keypoint is a binary sequence where successive bits refine localization, cutting memory complexity from $\Theta(HW)$ to $\Theta(log(HW))$. This direct bit prediction avoids dense heatmap-style background computations and removes the need for argmax or non-maximum suppression to decode coordinates. Furthermore, HashPose utilizes image classification backbones without upsampling layers to achieve high accuracy while significantly boosting its speed. We validate HashPose's performance envelope across a wide range of model sizes, ranging from an efficient 3.5M-parameter HashPose-XT model with 0.82 milliseconds frame latency and 85.9\% $AP^{50}$ on the COCO, to a 196.8M Large model that achieves a state-of-the-art 91.9\% $AP^{50}$ with 5.57 milliseconds frame latency using only the COCO training set. Simultaneously, HashPose has 510x lower output memory than heatmap configurations (0.48 MB vs 244.8 MB) for a typical $256 \times 192$ input, enabling high-throughput pose analysis that maintains high practical precision for on-device applications. Furthermore, its discrete representation is inherently suited for integer-only quantization, offering a clear path to further hardware acceleration on edge devices. Code is provided as supplementary material.

TL;DR: HashPose replaces bulky heatmaps with tiny, progressive hash codes for efficient pose estimation. It achieves state-of-the-art accuracy with 256x less head memory and high FPS, ideal for mobile/edge AI.