Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
Zhejiang University（浙江大学）；Zhejiang Gongshang University（浙江工商大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；Alibaba Group（阿里巴巴集团）
Zhe Ma, Jianfeng Dong, Yao Zhang, Zhongzi Long, Yuan He, Hui Xue, Shouling Ji
This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute among fashion items, which has potential values in many fashion related applications such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings in an end-to-end manner, thus measure the fine-grained similarity in the corresponding space. With two attention modules, i.e., Attribute-aware Spatial Attention and Attribute-aware Channel Attention, ASEN is able to locate the related regions and capture the essential patterns under the guidance of the specified attribute, thus make the learned attribute-specific embeddings better reflect the fine-grained similarity. Extensive experiments on four fashion-related datasets show the effectiveness of ASEN for fine-grained fashion similarity learning and its potential for fashion reranking.
Intel（英特尔）；Peking University（北京大学）；Columbia University（哥伦比亚大学）；JD AI Research（京东人工智能研究院）
Fashion attribute classification is of great importance to many high-level tasks such as fashion item search, fashion trend analysis, fashion recommendation, etc. The task is challenging due to the extremely imbalanced data distribution, particularly the attributes with only a few positive samples. In this paper, we introduce a hard-aware pipeline to make full use of "hard" samples/attributes. We first propose Hard-Aware BackPropagation (HABP) to efficiently and adaptively focus on training "hard" data. Then for the identified hard labels, we propose to synthesize more complementary samples fo…
In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes). The proposed task requires both localizing an object and describing its properties. To illustrate the various aspects of this task, we focus on the domain of fashion and introduce Fashionpedia as a step toward mapping out the visual aspects of the fashion world. Fashionpedia consists of two parts: (1) an ontology built by fashion experts contai…
In this paper, we introduce attribute-aware fashion-editing, a novel task, to the fashion domain. We re-define the overall objectives in AttGAN and propose the Fashion-AttGAN model for this new task. A dataset is constructed for this task with 14,221 and 22 attributes, which has been made publically available. Experimental results show the effectiveness of our Fashion-AttGAN on fashion editing over the original AttGAN.
Southeast University（东南大学）；Advanced Ocean Institute of Southeast University（东南大学先进海洋研究院）；Northwestern Polytechnical University（西北工业大学）；Binjiang Institute of Artificial Intelligence, ZJUT（浙江工业大学滨江人工智能研究院）；Alibaba Group（阿里巴巴集团）
FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval
Louis Vuitton（路易威登）；Inria, École normale supérieure, CNRS, PSL Research University（法国国家信息与自动化研究所, 巴黎高等师范学院, 法国国家科学研究中心, 巴黎文理研究大学）；Courant Institute of Mathematical Sciences and Center for Data Science, New York University（纽约大学库朗数学科学研究所与数据科学中心）
Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the spread or variance of the meaning of embedded target components to be captured and considered in more detail. We examine the method of estimating embedded representations as probability distributions for the interpretation of fashion-speci…
Zhejiang University（浙江大学）；Arizona State University（亚利桑那州立大学）；Alibaba Group（阿里巴巴集团）
Intelligent fashion outfit composition becomes more and more popular in these years. Some deep learning based approaches reveal competitive composition recently. However, the unexplainable characteristic makes such deep learning based approach cannot meet the the designer, businesses and consumers' urge to comprehend the importance of different attributes in an outfit composition. To realize interpretable and customized fashion outfit compositions, we propose a partitioned embedding network to learn interpretable representations from clothing items. The overall network architecture consists of…
Extracting fashion attributes from images of people wearing clothing/fashion accessories is a very hard multi-class classification problem. Most often, even catalogues of fashion do not have all the fine-grained attributes tagged due to prohibitive cost of annotation. Using images of fashion articles, running multi-class attribute extraction with a single model for all kinds of attributes (neck design detailing, sleeves detailing, etc) requires classifiers that are robust to missing and ambiguously labelled data. In this work, we propose a progressive training approach for such multi-class cla…
Analyzing fashion attributes is essential in the fashion design process. Current fashion forecasting firms, such as WGSN utilizes information from all around the world (from fashion shows, visual merchandising, blogs, etc). They gather information by experience, by observation, by media scan, by interviews, and by exposed to new things. Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections. In fact, such abstraction ability is useful in many fashion careers with different purposes. Fashion forecasters abstr…
Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset …
Nanyang Technological University（南洋理工大学）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局资讯通信研究院）
In this paper, we propose a method for ranking fashion images to find the ones which might be liked by more people. We collect two new datasets from image sharing websites (Pinterest and Polyvore). We represent fashion images based on attributes: semantic attributes and data-driven attributes. To learn semantic attributes from limited training data, we use an algorithm on multi-task convolutional neural networks to share visual knowledge among different semantic attribute categories. To discover data-driven attributes unsupervisedly, we propose an algorithm to simultaneously discover visual cl…
We build a large-scale visual search system which finds similar product images given a fashion item. Defining similarity among arbitrary fashion-products is still remains a challenging problem, even there is no exact ground-truth. To resolve this problem, we define more than 90 fashion-related attributes, and combination of these attributes can represent thousands of unique fashion-styles. The fashion-attributes are one of the ingredients to define semantic similarity among fashion-product images. To build our system at scale, these fashion-attributes are again used to build an inverted indexi…
With a growing demand for the search by image, many works have studied the task of fashion instance-level image retrieval (FIR). Furthermore, the recent works introduce a concept of fashion attribute manipulation (FAM) which manipulates a specific attribute (e.g color) of a fashion item while maintaining the rest of the attributes (e.g shape, and pattern). In this way, users can search not only "the same" items but also "similar" items with the desired attributes. FAM is a challenging task in that the attributes are hard to define, and the unique characteristics of a query are hard to be prese…
Fashion attribute editing is a task that aims to convert the semantic attributes of a given fashion image while preserving the irrelevant regions. Previous works typically employ conditional GANs where the generator explicitly learns the target attributes and directly execute the conversion. These approaches, however, are neither scalable nor generic as they operate only with few limited attributes and a separate generator is required for each dataset or attribute set. Inspired by the recent advancement of diffusion models, we explore the classifier-guided diffusion that leverages the off-the-…
College of Computer and Information Engineering, Zhejiang Gongshang University（浙江工商大学计算机与信息工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；Alibaba Group（阿里巴巴集团）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items. For example, whether the collar designs of the two clothes are similar. It has potential value in many fashion related applications, such as fashion copyright protection. To this end, we propose an Attribute-Specific Embedding Network (ASEN) to jointly learn multiple attribute-specific embeddings, thus measure the fine-grained similarity in the corresponding space. The proposed ASEN is comprise…
Kennesaw State University（肯尼索州立大学）；Oracle Retail Global Business Unit（甲骨文零售全球业务部）
As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable. Online retailers, from leading companies to start-ups, can leverage such applications in order to increase profit margin and enhance the consumer experience. Many notable schemes have been proposed to classify fashion items, however, the majority of which focused upon classifying basic-level categories, such as T-shirts, pants, skirts, shoes, bags, and so forth. In contrast to most prior efforts, this paper aims to enabl…
Peking University（北京大学）；The Chinese University of Hong Kong（香港中文大学）；SenseTime Research（商汤科技研究院）；The University of Sydney（悉尼大学）
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 m…
Generating accurate descriptions for online fashion items is important not only for enhancing customers' shopping experiences, but also for the increase of online sales. Besides the need of correctly presenting the attributes of items, the expressions in an enchanting style could better attract customer interests. The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning. Different from popular work on image captioning, it is hard to identify and describe the rich attributes of fashion items. We seed the description of an item by first identi…
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often hinder their ability to innovate during the design thinking and ideation process. In the fashion industry, in particular, the growing diversity of customers' needs, the intense global competition, and the shrinking time-to-market (a.k.a., "fast fashion") further exacerbate this challenge for designers. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation and/or editing of design c…
Collecting fine-grained labels usually requires expert-level domain knowledge and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data augmentation strategy at attribute level to expand the fine-grained samples. The principle lies in that attribute features are shared among fine-grained sub-categories, and can be seamlessly transferred among images. Toward this goal, we propose an automatic attribute mining approach to discover attributes that belong to the same super-category, and Attribute Mix is operated by mixing semantically meaningful attribute features from two im…
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Harbin Institute of Technology（哈尔滨工业大学）；HiDream.ai Inc.（HiDream.ai公司）；Harbin Institute of Technology (Weihai) Qingdao Research Institute（哈尔滨工业大学（威海）青岛研究院）
DeepFashion is a widely used clothing dataset with 50 categories and more than overall 200k images where each image is annotated with fine-grained attributes. This dataset is often used for clothes recognition and although it provides comprehensive annotations, the attributes distribution is unbalanced and repetitive specially for training fine-grained attribute recognition models. In this work, we tailored DeepFashion for fine-grained attribute recognition task by focusing on each category separately. After selecting categories with sufficient number of images for training, we remove very sca…
A Multihead Continual Learning Framework for Fine-Grained Fashion Image Retrieval with Contrastive Learning and Exponential Moving Average Distillation
Graduate School of Information Science and Technology, Hokkaido University（信息科学与技术研究生院，北海道大学）；Graduate School of Information Science and Technology, The University of Tokyo（信息科学与技术研究生院，东京大学）
In this paper, we address the extraction of the fine-grained attributes of an instance as a `multi-attribute classification' problem. To this end, we propose an end-to-end architecture by adopting the bi-linear Convolutional Neural Network with the pairwise ranking loss. This is the first time such architecture is applied for the fine-grained attributes classification problem. We compared the proposed method with a competitive deep Convolutional Neural Network baseline. Extensive experiments show that the proposed method attains/outperforms the performance of compared baseline with significant…
University of Maryland（马里兰大学）；Snap Inc.（Snap公司）；Google Inc.（谷歌公司）
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatially-aware representation by combining its semantic word vector representation with its spatial representation derived from the convolutional maps of the fine-tuned network. The resulting spatially-aware representations are further used to cluster attributes into multi…
A key challenge in fine-grained recognition is how to find and represent discriminative local regions. Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions. In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions. By designing a novel reward strategy, we are able to learn to loca…
ISR, IST, Universidade de Lisboa（里斯本大学系统与机器人研究所、高等理工学院）
In this paper, we describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, information that is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology im…
Beihang University（北京航空航天大学）；Inception Institute of Artificial Intelligence（Inception人工智能研究院）；Guangdong University of Petrochemical Technology（广东石油化工学院）
In this paper, we present a two-stream multi-task network for fashion recognition. This task is challenging as fashion clothing always contain multiple attributes, which need to be predicted simultaneously for real-time industrial systems. To handle these challenges, we formulate fashion recognition into a multi-task learning problem, including landmark detection, category and attribute classifications, and solve it with the proposed deep convolutional neural network. We design two knowledge sharing strategies which enable information transfer between tasks and improve the overall performance.…
Understanding fashion styles and trends is of great potential interest to retailers and consumers alike. The photos people upload to social media are a historical and public data source of how people dress across the world and at different times. While we now have tools to automatically recognize the clothing and style attributes of what people are wearing in these photographs, we lack the ability to analyze spatial and temporal trends in these attributes or make predictions about the future. In this paper, we address this need by providing an automatic framework that analyzes large corpora of…
How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model co…
Cortexica Vision Systems Limited（Cortexica视觉系统有限公司）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；the University of Adelaide（阿德莱德大学）
In this paper, we present a method for instance ranking and retrieval at fine-grained level based on the global features extracted from a multi-attribute recognition model which is not dependent on landmarks information or part-based annotations. Further, we make this architecture suitable for mobile-device application by adopting the bilinear CNN to make the multi-attribute recognition model smaller (in terms of the number of parameters). The experiments run on the Dress category of DeepFashion In-Shop Clothes Retrieval and CUB200 datasets show that the results of instance retrieval at fine-g…
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval
Zhejiang Gongshang University（浙江工商大学）；Zhejiang University（浙江大学）；Peking University（北京大学）；Huazhong University of Science and Technology（华中科技大学）；University of Science and Technology of China（中国科学技术大学）
Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (eg, neck type, dress length, collar type) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (eg, party/ sports/ casual wear). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domai…
Fashion image retrieval task aims to search relevant clothing items of a query image from the gallery. The previous recipes focus on designing different distance-based loss functions, pulling relevant pairs to be close and pushing irrelevant images apart. However, these methods ignore fine-grained features (e.g. neckband, cuff) of clothing images. In this paper, we propose a novel fashion image retrieval method leveraging both global and fine-grained features, dubbed Multi-Granular Alignment (MGA). Specifically, we design a Fine-Granular Aggregator(FGA) to capture and aggregate detailed patter…
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）；Objecteye Inc.（Objecteye公司）
IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；NExT++ Research Centre, National University of Singapore（新加坡国立大学NExT++研究中心）；College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学与技术学院及人工智能学院）
Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval
Southeast University（东南大学）；Nanjing University of Science and Technology（南京理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）
GUIDED: Granular Understanding via Identification, Detection, and Discrimination for Fine-Grained Open-Vocabulary Object Detection
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Meituan（美团）；GuangDong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Research Institute, Sun Yat-sen University（中山大学深圳研究院）
Attribute editing has become an important and emerging topic of computer vision. In this paper, we consider a task: given a reference garment image A and another image B with target attribute (collar/sleeve), generate a photo-realistic image which combines the texture from reference A and the new attribute from reference B. The highly convoluted attributes and the lack of paired data are the main challenges to the task. To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e.g., collar and sleeves) without paired data…
DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing E-Hualu Information Technology Co., Ltd.（北京亿华鲁信息技术有限公司）；State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China（通用人工智能国家重点实验室，BIGAI，北京，中国）
Beyond Binary Preference: Aligning Diffusion Models to Fine-grained Criteria by Decoupling Attributes
Zhejiang University（浙江大学）；University of Electronic Science and Technology of China（电子科技大学）；Peking University（北京大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）
Learning Attribute-Aware Hash Codes for Fine-Grained Image Retrieval via Query Optimization
School of Computer Science and Engineering（计算机科学与工程学院）；Nanjing University of Science and Technology（南京理工大学）；Key Laboratory of New Generation Artificial Intelligence Technology（新一代人工智能技术重点实验室）；Southeast University（东南大学）
In this paper, we propose a multi-stage and high-resolution model for image synthesis that uses fine-grained attributes and masks as input. With a fine-grained attribute, the proposed model can detailedly constrain the features of the generated image through rich and fine-grained semantic information in the attribute. With mask as prior, the model in this paper is constrained so that the generated images conform to visual senses, which will reduce the unexpected diversity of samples generated from the generative adversarial network. This paper also proposes a scheme to improve the discriminato…
Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；Watrix Technology Limited Co. Ltd.（华瑞芯科技有限公司）
Many self-supervised learning (SSL) methods have been successful in learning semantically meaningful visual representations by solving pretext tasks. However, prior work in SSL focuses on tasks like object recognition or detection, which aim to learn object shapes and assume that the features should be invariant to concepts like colors and textures. Thus, these SSL methods perform poorly on downstream tasks where these concepts provide critical information. In this paper, we present an SSL framework that enables us to learn color and texture-aware features without requiring any labels during t…
