Recently, horizontal representation-based panoramic semantic segmentation
approaches outperform projection-based solutions, because the distortions can
be effectively removed by compressing the spherical data in the vertical
direction. However, these methods ignore the distortion distribution prior and
are limited to unbalanced receptive fields, e.g., the receptive fields are
sufficient in the vertical direction and insufficient in the horizontal
direction. Differently, a vertical representation compressed in another
direction can offer implicit distortion prior and enlarge horizontal receptive
fields. In this paper, we combine the two different representations and propose
a novel 360{\deg} semantic segmentation solution from a complementary
perspective. Our network comprises three modules: a feature extraction module,
a bi-directional compression module, and an ensemble decoding module. First, we
extract multi-scale features from a panorama. Then, a bi-directional
compression module is designed to compress features into two complementary
low-dimensional representations, which provide content perception and
distortion prior. Furthermore, to facilitate the fusion of bi-directional
features, we design a unique self distillation strategy in the ensemble
decoding module to enhance the interaction of different features and further
improve the performance. Experimental results show that our approach
outperforms the state-of-the-art solutions with at least 10\% improvement on
quantitative evaluations while displaying the best performance on visual
appearance.

本文提出一种 360 度全景语义分割的新算法，通过特征提取、双向压缩、集成解码等三个模块，将两种互补的低维度压缩方法结合起来，并引入了自我蒸馏策略提高不同特征的交互，最终将全景图像分割，与现有算法相比，性能提升至少 10%。

室内 360° 语义分割的双向互补特征压缩与自蒸馏

Complementary Bi-directional Feature Compression for Indoor 360°  Semantic Segmentation with Self-distillation

This paper describes the submission of the NiuTrans end-to-end speech
translation system for the IWSLT 2021 offline task, which translates from the
English audio to German text directly without intermediate transcription. We
use the Transformer-based model architecture and enhance it by Conformer,
relative position encoding, and stacked acoustic and textual encoding. To
augment the training data, the English transcriptions are translated to German
translations. Finally, we employ ensemble decoding to integrate the predictions
from several models trained with the different datasets. Combining these
techniques, we achieve 33.84 BLEU points on the MuST-C En-De test set, which
shows the enormous potential of the end-to-end model.

本文描述了 NiuTrans 端到端语音翻译系统的提交，采用 Transformer 模型，结合 Conformer、相对位置编码和堆叠音频和文本编码来增强模型表现，在训练数据扩充方面采用了英文翻译到德文的方法，最终采用集成解码方法将多个模型的预测结果融合，达到了 33.84 BLEU 点，显现了端到端模型的巨大潜力。