Labeling pixel-level masks for fine-grained semantic segmentation tasks, e.g., human parsing, remains a challenging task. The ambiguous boundary between different semantic parts and those categories with similar appearances are usually confusing for annotators, leading to incorrect labels in ground-truth masks. These label noises will inevitably harm the training process and decrease the performance of the learned models. To tackle this, we introduce a noise-tolerant method, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models. In particular, starting from a model trained with inaccurate annotations, we design a cyclically learning scheduler to infer more reliable pseudo masks by iteratively aggregating the current learned model with the former sub-optimal one in an online manner. Besides, those corrected labels can reversely boost model performance. In this way, the models and the labels will reciprocally become more robust and accurate with self-correction learning cycles. Our SCHP is model-agnostic and can be applied to any human parsing models for further enhancing their performance. Benefiting the superiority of SCHP, we achieve the new state-of-the-art results on 6 benchmarks and win the 1st place for all human parsing tracks in the 3rd LIP Challenge.

Self-Correction for Human Parsing.

This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct inference (directly predicting each part of a human body using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively. In addition, the fusion of multi-source information is conditioned on the inputs, i.e., by estimating and considering the confidence of the sources. The whole model is end-to-end differentiable, explicitly modeling information flows and structures. Our approach is extensively evaluated on four popular datasets, outperforming the state-of-the-arts in all cases, with a fast processing speed of 23fps. Our code and results have been released to help ease future research in this direction.

/pdf/learning-compositional-neural-information-fusion-for-human-nimz0c2e9f.pdf

Learning Compositional Neural Information Fusion for Human Parsing

Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation, and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.

pdf/fashion-meets-computer-vision-a-survey-1d77yz0l4w.pdf

Fashion Meets Computer Vision: A Survey

According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirmed through the concatenation of their features with the parsing features. Driven by the insights, this paper studies how human semantic boundaries and keypoint locations can jointly improve human parsing. Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses. To capture such correlations, we propose a Correlation Parsing Machine (CorrPM) employing a heterogeneous non-local block to discover the spatial affinity among feature maps from the edge, pose and parsing. The proposed CorrPM allows us to report new state-of-the-art accuracy on three human parsing datasets. Importantly, comparative studies confirm the advantages of feature correlation over the concatenation.

/pdf/correlating-edge-pose-with-parsing-4ltbuxi5fw.pdf

Correlating Edge, Pose With Parsing

In this work, we introduce the new scene understanding task of Part-aware Panoptic Segmentation (PPS), which aims to understand a scene at multiple levels of abstraction, and unifies the tasks of scene parsing and part parsing. For this novel task, we provide consistent annotations on two commonly used datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task, using the metric and annotations, we set multiple baselines by merging results of existing state-of-the-art methods for panoptic segmentation and part segmentation. Finally, we conduct several experiments that evaluate the importance of the different levels of abstraction in this single task.

/pdf/part-aware-panoptic-segmentation-1kyk0x3395.pdf

Part-aware Panoptic Segmentation

Virtual try-on system could demonstrate the visual effect of wearing certain clothes, which is in a great demand for the online clothing customers. Due to the neglect of the original person structure, the previous try-on schemes usually encounter with the problems like the loss of body parts, the missing of body details and the deviation of clothing style. In this paper, we propose a novel image-based virtual try-on network, which could maintain the structural consistency between the generated image and the original image by human parsing. Our network consists of three components. Given the original person and the clothing images, the target human parsing maps are generated. Then, the parsing maps are matched with the target clothes to generate the warped clothes. Finally, according to the parsing results, the parts to be replaced the original images are intercepted, and more original information is retained as the input of the network to generate the final results. Experiments on an existing benchmark demonstrate our method maintains the consistency of structure and achieves the state-of-the-art performance.

Image-Based Virtual Try-on Network with Structural Coherence

To handle the colorization problem, we propose a deep patch-wise colorization model for grayscale images. Distinguished with some constructive color mapping models with complicated mathematical priors, we alternately apply two loss metric functions in the deep model to suppress the training errors under the convolutional neural network. To address the potential boundary artifacts, a refinement scheme is presented inspired by guided filtering. In the experiment section, we summarize our network parameters setting in practice, including the patch size, amount of layers and the convolution kernels. Our experiments demonstrate this model can output more satisfactory visual colorizations compared with the state-of-the-art methods. Moreover, we prove our method has extensive application domains and can be applied to stylistic colorization.

Deep patch-wise colorization model for grayscale images

An edge-refined vectorized deep colorization model for grayscale-to-color images

Clothing parsing is significant to many clothing applications. Recently, a lot of clothing parsing methods have been presented, which explore the innovation of the parsing pipeline or try to find more specific prior information. Although these methods perform well in some benchmarks, a few challenging problems have not been solved yet, such as the complicated mutual interference among labels. In this study, the authors propose a Conditional Progressive Network to parse clothing in different scales and prevent the mutual interference among labels. The authors’ solution consists of three sub-networks, including Conditional Parsing Network (CPN), Pose Estimation Network (PEN) and Label Transform Network (LTN). Specifically, the CPN module generates the intermediate parsing result in the form of the multiple progressive stages, which combines with the previous outputs in each stage and the specific prior conditions. The PEN module provides a series of heat maps about the human pose information. The LTN module suppresses the redundant labels to avoid the mutual interference among labels. They demonstrate their solution in parsing the fashion clothing cases on the ATR and the Fashion dataset. In their experiments, their method obtains a better performance than the state-of-the-art methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2018.5494

Conditional progressive network for clothing parsing

Clothing parsing provides some significant cues to analyze the dressing collocation and occasion In this paper, we propose a novel clothing parsing framework with deep end-to-end conditional feature coupling network for the photographic multi-persons in the fashion scene, and annotate a multi-persons clothing dataset for the effectiveness demonstration Our parsing framework has three sub-networks, including the coarse parsing network (CPN), the multi-pose feature network (MFN) and the coupling residual network (CRN) CPN and MFN generate a coarse segmentation intermediary and 28 pose-indicated heat maps, respectively CRN receives these auxiliary information and generates the fine-tuning clothing parsing result To verify the generality and effectiveness of our parsing framework, we compare our method with the state-of-the-art parsing and segmentation methods such as Deeplab [2] and Co-CNN [7] on our multi-persons clothing dataset and some fashion clothing benchmarks Experimental evaluations on these datasets demonstrate that our framework has a superior performance in the parsing task In particular, our CFCN achieves 8874% accuracy on the multi-persons clothing dataset, which is significantly higher than 8650% by Deeplab The project is available at https://githubcom/suzhuoi/CFCNet

Conditional Feature Coupling Network for Multi-persons Clothing Parsing

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