Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task have its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN.

Creatives are an effective form of delivering product information on the E-commerce platform. Designing an exquisite creative is a time-consuming but crucial task for sellers. In order to accelerate the process, we propose an automatic creative generation framework, named CreaGAN, to make the design procedure easier, faster, and more accurate. Given a well-designed creative for one product, our method can generalize to other product materials by utilizing existing design elements (e.g., background material). The framework consists of two major parts: aesthetics-aware placement and creative inpainting model. The placement model aims to generate plausible locations for new products by considering aesthetic principles. And the inpainting model focus on filling the mismatched regions through contextual information. We conduct experiments on both the public dataset and the real-world creative dataset. Quantitative and qualitative results demonstrate that our method outperforms current state-of-the-art methods and obtains more reasonable and aesthetic visualization results.

On an e-commerce platform, virtual try-on not only improves consumers' shopping experience but also attracts more consumers. However, the existing public virtual try-on dataset can't be generalized to Taobao fashion items because of low resolution, racial differences, and small dataset size. In this work, we build a large-scale and high-resolution virtual try-on dataset for Taobao e-commerce. Based on the dataset, we propose a virtual try-on system to generate high-resolution and attractive virtual try-on images without time-consuming image preprocessing. The system has covered more than 10000 fashion items and produced millions of virtual try-on results in Taobao e-commerce scenario.

Image edge detection is considered as a cornerstone task in computer vision. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, there is no consensus way to integrate the hierarchical information. In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection. In the proposed framework, we focus on two main problems: 1) how to design a universal network for incorporating hierarchical information sufficiently; and 2) how to achieve effective information flow between different stages and gradually improve the edge map stage by stage. To tackle these problems, we design a consecutive bottom-up and top-down architecture, where a bottom-up branch can gradually remove detailed or sharp boundaries to enable accurate edge detection and a top-down branch offers a chance of error-correcting by revisiting the low-level features that contain rich textual and spatial information. And attended additive module (AAM) is designed to cumulatively refine edges by selecting pivotal features in each stage. Experimental results show that our proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.

Lots of recent progress have been made by using Convolutional Neural Networks (CNN) for edge detection. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, different side networks are isolated, and the final results are usually weighted sum of the side outputs with uneven qualities. To tackle these issues, we propose a Cumulative Network (C-Net), which learns the side network cumulatively based on current visual features and low-level side outputs, to gradually remove detailed or sharp boundaries to enable high-resolution and accurate edge detection. Therefore, the lower-level edge information is cumulatively inherited while the superfluous details are progressively abandoned. In fact, recursively Learningwhere to remove superfluous details from the current edge map with the supervision of a higher-level visual feature is challenging. Furthermore, we employ atrous convolution (AC) and atrous convolution pyramid pooling (ASPP) to robustly detect object boundaries at multiple scales and aspect ratios. Also, cumulatively refining edges using high-level visual information and lower-lever edge maps is achieved by our designed cumulative residual attention (CRA) block. Experimental results show that our C-Net sets new records for edge detection on both two benchmark datasets: BSDS500 (i.e., .819 ODS, .835 OIS and .862 AP) and NYUDV2 (i.e., .762 ODS, .781 OIS, .797 AP). C-Net has great potential to be applied to other deep learning based applications, e.g., image classification and segmentation.