In this paper, we identify and study an important problem of gradient item
retrieval. We define the problem as retrieving a sequence of items with a
gradual change on a certain attribute, given a reference item and a
modification text. For example, after a customer saw a white dress, she/he
wants to buy a similar one but more floral on it. The extent of "more floral"
is subjective, thus prompting one floral dress is hard to satisfy the
customer's needs. A better way is to present a sequence of products with
increasingly floral attributes based on the white dress, and allow the customer
to select the most satisfactory one from the sequence. Existing item retrieval
methods mainly focus on whether the target items appear at the top of the
retrieved sequence, but ignore the demand for retrieving a sequence of products
with gradual change on a certain attribute. To deal with this problem, we
propose a weakly-supervised method that can learn a disentangled item
representation from user-item interaction data and ground the semantic meaning
of attributes to dimensions of the item representation. Our method takes a
reference item and a modification as a query. During inference, we start from
the reference item and "walk" along the direction of the modification in the
item representation space to retrieve a sequence of items in a gradient manner.
We demonstrate our proposed method can achieve disentanglement through weak
supervision. Besides, we empirically show that an item sequence retrieved by
our method is gradually changed on an indicated attribute and, in the item
retrieval task, our method outperforms existing approaches on three different
datasets.

本文提出一种基于弱监督学习的方法，实现了从用户 - 项目交互数据中学习到离散的项目表示，并在项目表示空间中 “沿着修改方向” 检索渐变方式的项目序列，达到渐变式变化，并在项目检索任务中运行得分优于现有方法。

可控梯度物品检索

Controllable Gradient Item Retrieval

3D shape completion for real data is important but challenging, since partial
point clouds acquired by real-world sensors are usually sparse, noisy and
unaligned. Different from previous methods, we address the problem of learning
3D complete shape from unaligned and real-world partial point clouds. To this
end, we propose a weakly-supervised method to estimate both 3D canonical shape
and 6-DoF pose for alignment, given multiple partial observations associated
with the same instance. The network jointly optimizes canonical shapes and
poses with multi-view geometry constraints during training, and can infer the
complete shape given a single partial point cloud. Moreover, learned pose
estimation can facilitate partial point cloud registration. Experiments on both
synthetic and real data show that it is feasible and promising to learn 3D
shape completion through large-scale data without shape and pose supervision.

本研究利用弱监督的方法，通过多视角几何约束来同时估计 3D 的标准形状和 6 自由度姿态，解决了学习未对准和真实世界部分点云的情况下，利用大量数据进行 3D 形状补全的问题。实验证明，学习姿势估计可以促进部分点云的配准。