Human Pose Regression with Residual Log-likelihood Estimation (2107.11291v3)

Abstract: Heatmap-based methods dominate in the field of human pose estimation by modelling the output distribution through likelihood heatmaps. In contrast, regression-based methods are more efficient but suffer from inferior performance. In this work, we explore maximum likelihood estimation (MLE) to develop an efficient and effective regression-based methods. From the perspective of MLE, adopting different regression losses is making different assumptions about the output density function. A density function closer to the true distribution leads to a better regression performance. In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution. Concretely, RLE learns the change of the distribution instead of the unreferenced underlying distribution to facilitate the training process. With the proposed reparameterization design, our method is compatible with off-the-shelf flow models. The proposed method is effective, efficient and flexible. We show its potential in various human pose estimation tasks with comprehensive experiments. Compared to the conventional regression paradigm, regression with RLE bring 12.4 mAP improvement on MSCOCO without any test-time overhead. Moreover, for the first time, especially on multi-person pose estimation, our regression method is superior to the heatmap-based methods. Our code is available at https://github.com/Jeff-sjtu/res-loglikelihood-regression

• The paper introduces a novel regression paradigm that leverages Residual Log-likelihood Estimation to outperform traditional heatmap-based approaches, achieving a 12.4 mAP improvement.
• The method integrates normalizing flows within regression models to significantly reduce computational cost (4.0 GFLOPs) while maintaining competitive accuracy.
• The approach redefines regression loss functions through a maximum likelihood framework, offering practical insights for resource-efficient and real-time pose estimation applications.

Human Pose Regression with Residual Log-likelihood Estimation

The paper "Human Pose Regression with Residual Log-likelihood Estimation" presents a novel approach to enhance regression-based methods for human pose estimation by applying the principles of maximum likelihood estimation (MLE), contrasting with the prevalent heatmap-based methods.

Methodological Insights

Heatmap-based approaches typically model joint locations using likelihood heatmaps, offering robust performance but at a high computational cost. These methods necessitate extensive resources, particularly when extended to 3D or 4D spaces. Meanwhile, regression-based methods, which map inputs directly to joint coordinates, are more efficient but traditionally suffer from performance deficits, especially in scenarios involving occlusions or ambiguous labels.

This work introduces a new regression paradigm by leveraging Residual Log-likelihood Estimation (RLE) in combination with normalizing flows to directly model output distributions. The authors challenge traditional regression loss functions (like $\ell_1$ or $\ell_2$) by suggesting they inherently make strong assumptions regarding the output distribution. Instead, RLE estimates residual changes in distribution rather than attempting to fit the underlying distribution directly. This novel approach is compatible with existing flow models, allowing it to adapt without altering network architectures significantly.

Results and Claims

The method shows substantial improvements, with experiments on the MSCOCO dataset demonstrating a 12.4 mAP increase in model performance over conventional regression methods. Notably, RLE enables regression-based models to surpass heatmap-based methods on multi-person pose estimation for the first time, achieving 71.3 mAP with significantly reduced computation (4.0 GFLOPs) compared to 71.0 mAP with 9.7 GFLOPs in corresponding heatmap-based methods.

Implications

Theoretical contributions include a broader perspective on regression loss functions, reimagining them through the lens of MLE and distribution modeling. Practically, the proposed method offers a more resource-efficient alternative, facilitating potential deployment on edge devices or scenarios where computational resources are limited.

Future Prospects

This research opens several avenues for future work, including further exploration of the versatility of normalizing flows in other regression tasks and extension to real-time applications in dynamic environments. The method's capacity to estimate output distributions adaptively suggests potential cross-application in fields such as medical imaging or autonomous systems where interpretability of predictions is crucial.

In summary, the proposed Residual Log-likelihood Estimation offers a promising shift in human pose estimation methodologies, challenging the dominance of heatmap-based solutions by enhancing the performance and applicability of regression-based methods. The findings are expected to inspire further exploration into adaptive estimation techniques across various AI-driven tasks.