Continual learning empowers models to adapt autonomously to the ever-changing
environment or data streams without forgetting old knowledge. Prompt-based
approaches are built on frozen pre-trained models to learn the task-specific
prompts and classifiers efficiently. Existing prompt-based methods are
inconsistent between training and testing, limiting their effectiveness. Two
types of inconsistency are revealed. Test predictions are made from all
classifiers while training only focuses on the current task classifier without
holistic alignment, leading to Classifier inconsistency. Prompt inconsistency
indicates that the prompt selected during testing may not correspond to the one
associated with this task during training. In this paper, we propose a novel
prompt-based method, Consistent Prompting (CPrompt), for more aligned training
and testing. Specifically, all existing classifiers are exposed to prompt
training, resulting in classifier consistency learning. In addition, prompt
consistency learning is proposed to enhance prediction robustness and boost
prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based
counterparts and achieves state-of-the-art performance on multiple continual
learning benchmarks. Detailed analysis shows that improvements come from more
consistent training and testing.

本文提出了一种新的基于提示的方法，Consistent Prompting（CPrompt），用于更加一致的训练和测试，在多个持续学习基准测试中实现了最新的性能表现。

无插值复习的连续学习一致提示

Consistent Prompting for Rehearsal-Free Continual Learning

While recent years have witnessed great progress on using diffusion models
for video generation, most of them are simple extensions of image generation
frameworks, which fail to explicitly consider one of the key differences
between videos and images, i.e., motion. In this paper, we propose a novel
motion-aware video generation (MoVideo) framework that takes motion into
consideration from two aspects: video depth and optical flow. The former
regulates motion by per-frame object distances and spatial layouts, while the
later describes motion by cross-frame correspondences that help in preserving
fine details and improving temporal consistency. More specifically, given a key
frame that exists or generated from text prompts, we first design a diffusion
model with spatio-temporal modules to generate the video depth and the
corresponding optical flows. Then, the video is generated in the latent space
by another spatio-temporal diffusion model under the guidance of depth, optical
flow-based warped latent video and the calculated occlusion mask. Lastly, we
use optical flows again to align and refine different frames for better video
decoding from the latent space to the pixel space. In experiments, MoVideo
achieves state-of-the-art results in both text-to-video and image-to-video
generation, showing promising prompt consistency, frame consistency and visual
quality.

提出一种新的运动感知视频生成 (MoVideo) 框架，从视频深度和光流两个方面考虑运动，并通过稀疏 - 时间扩散模型生成视频深度和光流，然后在潜在空间中生成视频，最后利用光流对不同帧进行对齐和细化，实现了文本到视频和图像到视频生成中最先进的结果。

MoVideo：使用扩散模型的运动感知视频生成

MoVideo: Motion-Aware Video Generation with Diffusion Models

One of the most impressive results of recent NLP history is the ability of
pre-trained language models to solve new tasks in a zero-shot setting. To
achieve this, NLP tasks are framed as natural language prompts, generating a
response indicating the predicted output. Nonetheless, the performance in such
settings often lags far behind its supervised counterpart, suggesting a large
space for potential improvement. In this paper, we explore methods to utilize
unlabeled data to improve zero-shot performance. Specifically, we take
advantage of the fact that multiple prompts can be used to specify a single
task, and propose to regularize prompt consistency, encouraging consistent
predictions over this diverse set of prompts. Our method makes it possible to
fine-tune the model either with extra unlabeled training data, or directly on
test input at inference time in an unsupervised manner. In experiments, our
approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al.,
2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points
in terms of accuracy. The gains are often attained with a small number of
unlabeled examples.

本文探讨了如何利用未标记的数据以提高自然语言处理任务的零样本性能，并通过规范提示一致性来鼓励模型的一致预测，并取得了实验结果上的进展和成果。