Because of predicting all the target tokens in parallel, the
non-autoregressive models greatly improve the decoding efficiency of speech
recognition compared with traditional autoregressive models. In this work, we
present dynamic alignment Mask CTC, introducing two methods: (1) Aligned Cross
Entropy (AXE), finding the monotonic alignment that minimizes the cross-entropy
loss through dynamic programming, (2) Dynamic Rectification, creating new
training samples by replacing some masks with model predicted tokens. The AXE
ignores the absolute position alignment between prediction and ground truth
sentence and focuses on tokens matching in relative order. The dynamic
rectification method makes the model capable of simulating the non-mask but
possible wrong tokens, even if they have high confidence. Our experiments on
WSJ dataset demonstrated that not only AXE loss but also the rectification
method could improve the WER performance of Mask CTC.

本研究提出动态对齐 Mask CTC 方法，利用 Aligned Cross Entropy（AXE）和 Dynamic Rectification 两种方法，使非自回归模型在语音识别中的解码效率得到很大提升。AXE 忽略绝对位置对齐，关心的是相对顺序的匹配，而动态校正方法使模型能够模拟可能错误但有高置信度的非遮罩标记。实验表明，AXE 损失和校正方法都可以提高 Mask CTC 的 WER 性能。

动态对齐掩码 CTC: 带有对齐交叉熵的改进型掩码 CTC

Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy

Masked Autoencoders (MAE) have been prevailing paradigms for large-scale
vision representation pre-training. By reconstructing masked image patches from
a small portion of visible image regions, MAE forces the model to infer
semantic correlation within an image. Recently, some approaches apply
semantic-rich teacher models to extract image features as the reconstruction
target, leading to better performance. However, unlike the low-level features
such as pixel values, we argue the features extracted by powerful teacher
models already encode rich semantic correlation across regions in an intact
image.This raises one question: is reconstruction necessary in Masked Image
Modeling (MIM) with a teacher model? In this paper, we propose an efficient MIM
paradigm named MaskAlign. MaskAlign simply learns the consistency of visible
patch features extracted by the student model and intact image features
extracted by the teacher model. To further advance the performance and tackle
the problem of input inconsistency between the student and teacher model, we
propose a Dynamic Alignment (DA) module to apply learnable alignment. Our
experimental results demonstrate that masked modeling does not lose
effectiveness even without reconstruction on masked regions. Combined with
Dynamic Alignment, MaskAlign can achieve state-of-the-art performance with much
higher efficiency. Code and models will be available at
this https URL

本文提出了一种名为 MaskAlign 的高效 MIM 范例，将可视化小片段功能与老师模型提取的整体图像特征相一致，证明即使在掩蔽区域没有重建的情况下，蒙面建模也不会失去效率。与 Dynamic Alignment 相结合，MaskAlign 能够实现高效的最新性能。

注视所见：不需重建的遮蔽图像建模

Stare at What You See: Masked Image Modeling without Reconstruction

Few-shot learning (FSL), which aims to recognise new classes by adapting the
learned knowledge with extremely limited few-shot (support) examples, remains
an important open problem in computer vision. Most of the existing methods for
feature alignment in few-shot learning only consider image-level or
spatial-level alignment while omitting the channel disparity. Our insight is
that these methods would lead to poor adaptation with redundant matching, and
leveraging channel-wise adjustment is the key to well adapting the learned
knowledge to new classes. Therefore, in this paper, we propose to learn a
dynamic alignment, which can effectively highlight both query regions and
channels according to different local support information. Specifically, this
is achieved by first dynamically sampling the neighbourhood of the feature
position conditioned on the input few shot, based on which we further predict a
both position-dependent and channel-dependent Dynamic Meta-filter. The filter
is used to align the query feature with position-specific and channel-specific
knowledge. Moreover, we adopt Neural Ordinary Differential Equation (ODE) to
enable a more accurate control of the alignment. In such a sense our model is
able to better capture fine-grained semantic context of the few-shot example
and thus facilitates dynamical knowledge adaptation for few-shot learning. The
resulting framework establishes the new state-of-the-arts on major few-shot
visual recognition benchmarks, including miniImageNet and tieredImageNet.

本文提出了一种基于动态对齐的特征调整方法，从视觉识别的角度解决了 few-shot learning 的问题，采用神经常微分方程技术，使模型能更好地捕捉少量样本的微粒子级别的语义上下文，大幅提高了在 miniImageNet 和 tieredImageNet 数据集上的识别准确率。