Human Pose Estimation with Iterative Error Feedback (1507.06550v3)

Abstract: Hierarchical feature extractors such as Convolutional Networks (ConvNets) have achieved impressive performance on a variety of classification tasks using purely feedforward processing. Feedforward architectures can learn rich representations of the input space but do not explicitly model dependencies in the output spaces, that are quite structured for tasks such as articulated human pose estimation or object segmentation. Here we propose a framework that expands the expressive power of hierarchical feature extractors to encompass both input and output spaces, by introducing top-down feedback. Instead of directly predicting the outputs in one go, we use a self-correcting model that progressively changes an initial solution by feeding back error predictions, in a process we call Iterative Error Feedback (IEF). IEF shows excellent performance on the task of articulated pose estimation in the challenging MPII and LSP benchmarks, matching the state-of-the-art without requiring ground truth scale annotation.

• The paper's key contribution is the Iterative Error Feedback (IEF) method that progressively refines initial 2D human pose estimates.
• It employs a novel convolutional architecture with hierarchical feature extractors and a Fixed Path Consolidation strategy to stabilize training and improve accuracy.
• Experimental results show significant improvements on MPII and LSP datasets, demonstrating state-of-the-art performance in keypoint detection.

Human Pose Estimation with Iterative Error Feedback

The paper "Human Pose Estimation with Iterative Error Feedback" by João Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik introduces a novel framework for addressing the complexities inherent in tasks that involve structured output spaces, such as 2D human pose estimation. The key contribution of this work is the Iterative Error Feedback (IEF) method, which augments the traditional feedforward architectures by incorporating feedback to progressively correct the output predictions.

Principal Contributions and Methodology

1. Hierarchical Feature Extractors and Feedback: The authors extend the capabilities of convolutional networks (ConvNets) beyond their traditional feedforward operation. This approach allows the model to capture dependencies not only within the input space but also within the structured output space. The proposed feedback mechanism continuously refines the initial predictions by iteratively adjusting them based on the error from previous steps.
2. Iterative Error Feedback (IEF): The IEF framework iterates over an initial guess of the keypoints, progressively modifying it. This process involves stacking the current input image with a rendered version of the estimated keypoints, passing this through a ConvNet which predicts the correction to be applied to the keypoints. Mathematically, the model updates its guesses using:

$$\epsilon_t = f(x_t), \quad y_{t+1} = y_t + \epsilon_t, \quad x_{t+1} = I \oplus g(y_{t+1}),$$

where $f$ is the ConvNet, $g$ is the rendering function, and $\epsilon_t$ is the correction applied to the current estimate $y_t$.

1. Learning Strategy: The learning algorithm incorporates a "Fixed Path Consolidation" (FPC) approach that progressively trains the model by adding correction steps iteratively. This curriculum learning strategy stabilizes the training and ensures that earlier corrections are well-optimized.

Experimental Results

The paper evaluates the performance of IEF on two challenging benchmarks for 2D human pose estimation: MPII Human Pose and Leeds Sports Pose (LSP) datasets. Key findings include:

• MPII Dataset: IEF achieves a PCKh-0.5 score of 81.0 without ground truth scale information, significantly outperforming previous methods (Tompson et al. scored 66.0). When using known scales, IEF matches the state-of-the-art with a PCKh-0.5 score of 81.3.
• LSP Dataset: The model achieves competitive results with a 73.6% total PCP score, equivalent to the performance of current state-of-the-art approaches.

Ablation Studies and Analysis

The authors conduct several ablation studies to verify the effectiveness of their approach:

• Iterative vs. Direct Prediction: Direct prediction of keypoints results in a PCKh-0.5 score of 74.8, whereas the iterative approach of IEF achieves 81.0, showing the significant benefits of iterative refinement.
• IEF vs. Iterative Direct Prediction: A direct iterative prediction without error feedback yields a PCKh-0.5 score of 73.4, highlighting the importance of correcting errors iteratively in small, bounded steps.
• Fixed Path Consolidation: The application of the FPC strategy yields higher scores and reduces model drift, as evidenced by improved performance metrics and the ability to perform more correction steps effectively.

Implications and Future Work

The introduction of feedback mechanisms into ConvNets opens pathways for handling more complex, structured output spaces in various vision tasks. The demonstrated benefits of IEF suggest that similar frameworks could be adapted for other problems, such as 3D pose estimation or object segmentation, where output spaces are highly correlated. Future work could explore more sophisticated feedback mechanisms, potentially using learnable deconvolution layers, to enhance the expressive power of these models.

In conclusion, the "Human Pose Estimation with Iterative Error Feedback" paper offers a robust framework that significantly pushes the boundaries of hierarchical feature extractors, making it a substantial contribution to the field of computer vision and structured output learning.