Fashion Landmark Detection in the Wild (1608.03049v1)

Abstract: Visual fashion analysis has attracted many attentions in the recent years. Previous work represented clothing regions by either bounding boxes or human joints. This work presents fashion landmark detection or fashion alignment, which is to predict the positions of functional key points defined on the fashion items, such as the corners of neckline, hemline, and cuff. To encourage future studies, we introduce a fashion landmark dataset with over 120K images, where each image is labeled with eight landmarks. With this dataset, we study fashion alignment by cascading multiple convolutional neural networks in three stages. These stages gradually improve the accuracies of landmark predictions. Extensive experiments demonstrate the effectiveness of the proposed method, as well as its generalization ability to pose estimation. Fashion landmark is also compared to clothing bounding boxes and human joints in two applications, fashion attribute prediction and clothes retrieval, showing that fashion landmark is a more discriminative representation to understand fashion images.

• The paper proposes the Deep Fashion Alignment (DFA) framework and a large dataset for accurate fashion landmark detection in challenging real-world images.
• The DFA framework employs a multi-stage CNN cascade enhanced by pseudo-labeling and auto-routing for improved landmark prediction precision.
• Empirical results show the DFA framework outperforms prior methods and that fashion landmarks improve clothing attribute prediction and retrieval tasks.

Fashion Landmark Detection: An In-Depth Analysis

The paper titled "Fashion Landmark Detection in the Wild" introduces an innovative approach to visual fashion analysis, which extends previous efforts that primarily used bounding boxes or human joints. The paper presents a detailed methodology for fashion landmark detection and alignment, focusing on predicting functional key points on clothing items, such as necklines, hemlines, and cuffs. To facilitate further research in this domain, the authors have compiled a comprehensive fashion landmark dataset, consisting of over 120,000 images labeled with eight distinct landmarks.

The paper addresses the challenges associated with visual fashion analysis, highlighting the variations in clothing poses, scales, and appearances. These variations often complicate the detection process. As a solution, the authors propose a more discriminative representation in the form of fashion landmarks, which capture both the clothing boundaries and their functional regions. This approach provides a more nuanced understanding of fashion items than existing methods like bounding boxes and human joints.

A significant contribution of this work is the introduction of the Deep Fashion Alignment (DFA) framework, which employs a cascade of multiple convolutional neural networks (CNNs) over three stages. This cascade systematically enhances the precision of landmark predictions. The architecture is designed to harness the benefits of both network cascade and pseudo-labeling strategies. Pseudo-labels are used to encapsulate sample relationships at varying stages, aiding in reducing the diversities in fashion images. Furthermore, an auto-routing strategy partitions samples based on their complexity, allowing the model to efficiently handle diverse sample difficulties.

The paper provides extensive empirical validation of the proposed DFA method. It demonstrates superior performance over established methods like DeepPose and Image Dependent Pairwise Relations (IDPR) in fashion landmark detection. The novel use of pseudo-labels significantly boosts the model's ability to predict precise landmarks, particularly when soft labels are employed. The routing mechanism further enhances the robustness of the DFA framework, especially in cases with substantial pose and scale variations.

Additionally, the paper illustrates how fashion landmarks can improve the tasks of clothing attribute prediction and clothes retrieval. The use of fashion landmarks as a representation outperforms other localization methods (e.g., full images, bounding boxes, and human joints) in terms of the top-5 recall rates for attribute prediction and retrieval accuracy.

From a theoretical standpoint, this work advances the understanding of visual fashion analysis by demonstrating the superiority of fashion landmarks over traditional representations. Practically, the proposed DFA framework presents a scalable and efficient approach to landmark detection that is applicable to a wide range of fashion-related applications.

Looking toward the future, this research could pave the way for more advanced AI systems capable of understanding and interpreting complex visual fashion data. Possible future developments might include further refinement of the DFA framework, integration with other visual recognition tasks, and exploration of its applicability in other domains with similar structural analysis challenges.