Pose Recognition with Cascade Transformers (2104.06976v1)

Abstract: In this paper, we present a regression-based pose recognition method using cascade Transformers. One way to categorize the existing approaches in this domain is to separate them into 1). heatmap-based and 2). regression-based. In general, heatmap-based methods achieve higher accuracy but are subject to various heuristic designs (not end-to-end mostly), whereas regression-based approaches attain relatively lower accuracy but they have less intermediate non-differentiable steps. Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches. We demonstrate the keypoint hypothesis (query) refinement process across different self-attention layers to reveal the recursive self-attention mechanism in Transformers. In the experiments, we report competitive results for pose recognition when compared with the competing regression-based methods.

• The paper presents a novel regression-based methodology that leverages cascade Transformers to streamline human pose detection.
• It employs a two-stage encoder-decoder system where the first Transformer detects persons and the second refines keypoint predictions, showing competitive performance on COCO and MPII.
• The study bridges traditional heatmap methods and end-to-end regression, reducing training complexity while enhancing prediction accuracy through progressive self- and cross-attention mechanisms.

Overview of "Pose Recognition with Cascade Transformers"

"Pose Recognition with Cascade Transformers" presents an advanced approach to tackle the 2D human pose recognition problem. This paper explores a regression-based method using cascade Transformers, contrasting it with the traditional heatmap-based techniques. While heatmap-based strategies offer precision, they involve complex, non-differentiable steps. The presented method harnesses the power of Transformers with an encoder-decoder structure tailored for regression-based person and keypoint detection.

Main Contributions

1. Regression-based Methodology: This paper introduces a novel approach for human pose recognition that leverages regression over heatmaps, incorporating cascade Transformers for keypoint detection. This strategy promises a more streamlined and general-purpose solution than its heatmap-based counterparts.
2. Cascade Transformers System: The paper develops a two-stage methodology using two cascade Transformers. The first Transformer detects persons, setting the stage for the second to perform keypoint detection, potentially in an end-to-end capacity. This method is termed Pose Regression TRansformers (PRTR).
3. Transformer Architecture: PRTR builds on the DETR framework, extending its application to pose recognition. This involves intricate self-attention and cross-attention layers that refine keypoint predictions progressively through the Transformer decoder layers.
4. Comparative Results: The paper reports competitive performance for PRTR against existing regression-based methods, showing its potential to meet or surpass state-of-the-art results in 2D human pose recognition.

Technical Approach

• Encoder-Decoder Structure: The transformer models leverage self-attention and cross-attention mechanisms across their layers. The regression tasks are accomplished through a refined decoding sequence, progressively increasing keypoint prediction confidence and decreasing spatial deviations to ground truth.
• Two-Stage and Sequential Arrangements: Two alternative arrangements were developed—a two-stage process where the Transformers learn sequentially and a sequential approach incorporating the spatial Transformer network for end-to-end learning.
• Visualization and Analysis of Keypoint Predictions: The paper provides extensive visualization of the keypoint query refinement process across self-attention layers, illustrating how the Transformer architecture enhances prediction precision as the decoding progresses.

Performance and Results

Empirical studies demonstrated the competitiveness of PRTR. The method was evaluated against various test beds, including COCO and MPII datasets, achieving notable results. The architecture's end-to-end capability optimizes the integration of person detection and keypoint recognition tasks, showing promise in refining prediction accuracy.

Implications and Future Directions

The methodology proposed in this research bridges the gap between complex heatmap-based methods and streamlined regression approaches by simplifying the training and prediction processes. This has significant implications for the field of computer vision, especially in applications requiring real-time responses such as surveillance and human-computer interaction.

Looking ahead, the integration of more robust backbones and finer-tuning of the two-stage versus sequential cascade process could further enhance the model's applicability across various domains. The exploration of advanced Transformer architectures may continue to influence both theoretical and practical advancements in AI-driven pose recognition.

In conclusion, "Pose Recognition with Cascade Transformers" serves as a substantial contribution to the field, reinforcing the potential of regression-based methods augmented with Transformer architectures to effectively address complex tasks in human pose estimation.