This paper is dedicated to team VAA's approach submitted to the Fashion-IQ
challenge in CVPR 2020. Given a pair of the image and the text, we present a
novel multimodal composition method, RTIC, that can effectively combine the
text and the image modalities into a semantic space. We extract the image and
the text features that are encoded by the CNNs and the sequential models (e.g.,
LSTM or GRU), respectively. To emphasize the meaning of the residual of the
feature between the target and candidate, the RTIC is composed of N-blocks with
channel-wise attention modules. Then, we add the encoded residual to the
feature of the candidate image to obtain a synthesized feature. We also
explored an ensemble strategy with variants of models and achieved a
significant boost in performance comparing to the best single model. Finally,
our approach achieved 2nd place in the Fashion-IQ 2020 Challenge with a test
score of 48.02 on the leaderboard.

本文提出了新的多模态组合方法 RTIC，通过应用卷积神经网络和时序模型的文本和图像特征，达到了在 Fashion-IQ 挑战赛中识别衣服与描述的成果。在提取了候选图像的特征后，向其添加编码后的残差，并在变型模型中采用集合策略，取得了第二名的成绩。