Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal
retrieval task for searching natural images given free-hand sketches under the
zero-shot scenario. Most existing methods solve this problem by simultaneously
projecting visual features and semantic supervision into a low-dimensional
common space for efficient retrieval. However, such low-dimensional projection
destroys the completeness of semantic knowledge in original semantic space, so
that it is unable to transfer useful knowledge well when learning semantic from
different modalities. Moreover, the domain information and semantic information
are entangled in visual features, which is not conducive for cross-modal
matching since it will hinder the reduction of domain gap between sketch and
image. In this paper, we propose a Progressive Domain-independent Feature
Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of
original semantic knowledge, PDFD decomposes visual features into domain
features and semantic ones, and then the semantic features are projected into
common space as retrieval features for ZS-SBIR. The progressive projection
strategy maintains strong semantic supervision. Besides, to guarantee the
retrieval features to capture clean and complete semantic information, the
cross-reconstruction loss is introduced to encourage that any combinations of
retrieval features and domain features can reconstruct the visual features.
Extensive experiments demonstrate the superiority of our PDFD over
state-of-the-art competitors.

本文提出了一种逐步领域无关特征分解（PDFD）网络，用于零样本基于草图的图像检索（ZS-SBIR），通过将视觉特征分解为域特征和语义特征来维护语义知识的完整性，并使语义特征投影到公共空间，最终的检索特征能够捕获干净且完整的语义信息。实验证明了 PDFD 与现有方法相比在 ZS-SBIR 中具有优越性。

零样本基于草图的图像检索的渐进领域无关特征分解网络

Progressive Domain-Independent Feature Decomposition Network for  Zero-Shot Sketch-Based Image Retrieval

The conventional paradigm in neural question answering (QA) for narrative
content is limited to a two-stage process: first, relevant text passages are
retrieved and, subsequently, a neural network for machine comprehension
extracts the likeliest answer. However, both stages are largely isolated in the
status quo and, hence, information from the two phases is never properly fused.
In contrast, this work proposes RankQA: RankQA extends the conventional
two-stage process in neural QA with a third stage that performs an additional
answer re-ranking. The re-ranking leverages different features that are
directly extracted from the QA pipeline, i.e., a combination of retrieval and
comprehension features. While our intentionally simple design allows for an
efficient, data-sparse estimation, it nevertheless outperforms more complex QA
systems by a significant margin: in fact, RankQA achieves state-of-the-art
performance on 3 out of 4 benchmark datasets. Furthermore, its performance is
especially superior in settings where the size of the corpus is dynamic. Here
the answer re-ranking provides an effective remedy against the underlying
noise-information trade-off due to a variable corpus size. As a consequence,
RankQA represents a novel, powerful, and thus challenging baseline for future
research in content-based QA.

RankQA 是一种新的神经问答模型，利用三阶段过程（检索、理解和重新排序）来自动回答问题，为后续基于内容的问答研究提供了有力的基线模型。