Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net (1511.06881v5)

Abstract: Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. body, head and arms, etc.) from natural images is a challenging and fundamental problem for computer vision. A big difficulty is the large variability of scale and location for objects and their corresponding parts. Even limited mistakes in estimating scale and location will degrade the parsing output and cause errors in boundary details. To tackle these difficulties, we propose a "Hierarchical Auto-Zoom Net" (HAZN) for object part parsing which adapts to the local scales of objects and parts. HAZN is a sequence of two "Auto-Zoom Net" (AZNs), each employing fully convolutional networks that perform two tasks: (1) predict the locations and scales of object instances (the first AZN) or their parts (the second AZN); (2) estimate the part scores for predicted object instance or part regions. Our model can adaptively "zoom" (resize) predicted image regions into their proper scales to refine the parsing. We conduct extensive experiments over the PASCAL part datasets on humans, horses, and cows. For humans, our approach significantly outperforms the state-of-the-arts by 5% mIOU and is especially better at segmenting small instances and small parts. We obtain similar improvements for parsing cows and horses over alternative methods. In summary, our strategy of first zooming into objects and then zooming into parts is very effective. It also enables us to process different regions of the image at different scales adaptively so that, for example, we do not need to waste computational resources scaling the entire image.

Human and Object Parsing with Hierarchical Auto-Zoom Net

This paper presents a novel approach to the problem of parsing articulated objects, such as humans and animals, from natural images into semantic parts. This task is fundamental to computer vision, contributing significantly to pose estimation, object segmentation, and fine-grained recognition. The authors introduce the Hierarchical Auto-Zoom Net (HAZN) as a method for adapting object part parsing to local scales and positions effectively, overcoming limitations of existing approaches that struggle with scale variability and object localization errors.

The proposed method is structured into two sequential Auto-Zoom Nets (AZNs) using fully convolutional networks: the object-scale AZN and the part-scale AZN. This hierarchical approach first focuses on predicting the locations and scales of object instances and then refines parsing of their constituent parts. By doing so, the model performs adaptive "zoom" operations to adjust image regions to optimal scales, thus improving the resolution and accuracy of semantic part segmentation. The model was extensively evaluated on the PASCAL part datasets across multiple object categories including humans, horses, and cows.

Experimental results demonstrate the effectiveness of the HAZN approach, particularly for small object instances and small parts that are typically challenging for conventional methods. The method surpasses previous state-of-the-art techniques by significantly enhancing the mean Intersection-over-Union (mIOU) metric—by 5% for human instances—especially concentrating on lower body parts which are often less visible. The scalability of targets—zooming into parts—is a notable advancement as it allows finer-scale analysis with efficient computational use, scant memory for large scale transformations otherwise.

From a technical perspective, the paper explores the probabilistic view of the Auto-Zoom process where ROI predictions based on bounding box regressions aid in zooming and refining part scores. With adaptive scale change, the parsing evolved at three granular levels in the image, object, and part. Implementation details reaffirm the robustness through distinct datasets, possessing variations in object size, occlusion, and pose.

The implications of this research bear significant impact on practical and theoretical areas of AI. Within practical applications, this model can improve systems for autonomous robotics and assistive technologies for the visually impaired, allowing for finer object scene analysis and interaction understanding. Theoretically, this approach opens new avenues for scale-aware neural network designs, encouraging further exploration in dynamic, adaptive model architectures.

Despite promising advancements, the authors acknowledge failure modes related to heavy occlusion and abnormal poses, indicating room for enhancements in dealing with such complexities. With prospects of extending HAZN for finer-scale parts and potential applications in pose estimation, the paper concludes by laying down future directions for research.

In summary, the Hierarchical Auto-Zoom Net provides a powerful framework for overcoming challenges in object part parsing, demonstrating its advantages in handling varying scales, enhancing parsing performance, and optimizing computational resources effectively. The impact of this work extends to several domains within computer vision, underscoring the importance of adaptive, scalable approaches in handling real-world variability.