Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition (2204.04654v2)

Abstract: Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation and attribute recognition. Contrary to the previous works that separately model each task as a multi-head prediction problem, our insight is to bridge these two tasks with one unified model via vision transformer modeling to benefit each task. In particular, we introduce the object query for segmentation and the attribute query for attribute prediction. Both queries and their corresponding features can be linked via mask prediction. Then we adopt a two-stream query learning framework to learn the decoupled query representations.We design a novel Multi-Layer Rendering module for attribute stream to explore more fine-grained features. The decoder design shares the same spirit as DETR. Thus we name the proposed method \textit{Fahsionformer}. Extensive experiments on three human fashion datasets illustrate the effectiveness of our approach. In particular, our method with the same backbone achieve \textbf{relative 10\% improvements} than previous works in case of \textit{a joint metric (AP$^{\text{mask}}_{\text{IoU+F}_1}$) for both segmentation and attribute recognition}. To the best of our knowledge, we are the first unified end-to-end vision transformer framework for human fashion analysis. We hope this simple yet effective method can serve as a new flexible baseline for fashion analysis. Code is available at https://github.com/xushilin1/FashionFormer.

• The paper introduces Fashionformer, a unified transformer framework that jointly addresses human fashion segmentation and attribute recognition with a two-stream query learning approach.
• It employs a Multi-Layer Rendering module that refines multi-scale feature aggregation, yielding a 10% AP boost on challenging datasets like Fashionpedia.
• The unified model outperforms separate-task methods, offering improved accuracy and reduced computational cost for practical fashion analysis applications.

An Analysis of FashionFormer: A Unified Baseline for Human Fashion Segmentation and Recognition

The increasing need for sophisticated fashion analysis methods in digital applications such as e-commerce and virtual design has prompted research into human fashion understanding. The paper "Fashionformer: A Simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition" proposes a novel approach to this complex task. This essay provides an expert analysis of the presented method, focusing on the core contributions, experimental outcomes, and potential implications for future research in computer vision, specifically in the domain of fashion analysis.

Core Contributions

The paper introduces Fashionformer, an innovative framework that addresses human fashion segmentation and attribute recognition jointly. Traditional methods, such as Attribute-Mask R-CNN, generally employ separate networks for each task, which can lead to suboptimal information sharing between related subtasks. Fashionformer departs from this convention by integrating both tasks using a unified framework based on vision transformers.

Key innovations in Fashionformer include:

1. Vision Transformer Framework: By utilizing object and attribute queries within a transformer architecture, Fashionformer efficiently models both instance segmentation and attribute recognition in a unified manner. This approach capitalizes on the strengths of transformers in handling long-range dependencies and enhances feature representation across tasks.
2. Two-Stream Query Learning: The framework includes a two-stream query learning model, distinguishing between object queries for segmentation and attribute queries for recognition. This separation allows for specialized processing paths, thereby improving task-specific outcomes without interference.
3. Multi-Layer Rendering (MLR) Module: To enhance fine-grained attribute recognition, the MLR module effectively aggregates multi-scale features to refine attribute query features. This results in increased granularity in the attribute recognition process.

Experimental Results

Detailed evaluations were conducted across several fashion datasets: Fashionpedia, ModaNet, and DeepFashion. The experimental results on these datasets underscore the efficacy of Fashionformer:

• On the challenging Fashionpedia dataset, using a ResNet-50 backbone, Fashionformer achieved a marked improvement, yielding a 10% increase in AP$^\text{mask}_{\text{IoU+F}_1}$ compared to previous methods.
• The framework demonstrated superior performance in joint tasks, suggesting that attribute recognition can indeed benefit segmentation performance and vice versa.
• Benchmarks on ModaNet and DeepFashion also show that Fashionformer surpasses existing methods in terms of both instance segmentation accuracy and efficiency, with reduced computation costs (GFlops) and parameter requirements, reinforcing its practicality in real-world applications.

Implications and Future Directions

The unified model proposed has significant implications for future research in AI-driven fashion analysis:

• Unified Models Benefits: The demonstrated advantages of task unification in Fashionformer suggest broader applicability across varied domains where multiple related vision tasks can be benefited from shared representations.
• Transformer Capabilities: The promising performance of vision transformers in this application encourages further exploration in other complex multi-task scenarios, beyond traditional CNN-based frameworks.
• Scalability: With the increasing availability of large-scale fashion datasets and the framework's effective handling of such datasets, Fashionformer presents a scalable baseline that could stimulate advancements in clothing recognition systems, which rely on vast and heterogeneous data.

In conclusion, this paper contributes a significant step forward in advancing the methodology for fashion analysis by effectively bridging the gap between instance segmentation and attribute recognition tasks. The introduction of Fashionformer illustrates the potential for vision transformer-based networks in complex multitasking environments, opening avenues for further research in AI applications within the fashion industry and beyond.