We present Piecewise Rectified Flow (PeRFlow), a flow-based method for
accelerating diffusion models. PeRFlow divides the sampling process of
generative flows into several time windows and straightens the trajectories in
each interval via the reflow operation, thereby approaching piecewise linear
flows. PeRFlow achieves superior performance in a few-step generation.
Moreover, through dedicated parameterizations, the obtained PeRFlow models show
advantageous transfer ability, serving as universal plug-and-play accelerators
that are compatible with various workflows based on the pre-trained diffusion
models. The implementations of training and inference are fully open-sourced.
this https URL

基于分段线性流的 Piecewise Rectified Flow (PeRFlow) 方法通过将采样过程分为多个时间窗口并在每个时间间隔内通过 reflow 操作直线化轨迹的方式加速扩散模型，取得了优越性能，在少量步骤的生成方面表现出色。此外，通过专用参数化，所得到的 PeRFlow 模型具有有利的传递能力，可作为通用的即插即用加速器与基于预训练扩散模型的各种工作流兼容。训练和推理的实现完全开源。

PeRFlow：分段修正流作为通用即插即用加速器

PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator

Recent research has shown that multi-task pre-training greatly improves the
model's robustness and transfer ability, which is crucial for building a
high-quality dialog system. However, most previous works on multi-task
pre-training rely heavily on human-defined input format or prompt, which is not
optimal in quality and quantity. In this work, we propose to use Task-based
Automatic Prompt generation (TAP) to automatically generate high-quality
prompts. Using the high-quality prompts generated, we scale the corpus of the
pre-trained conversation model to 122 datasets from 15 dialog-related tasks,
resulting in Universal Pre-trained Conversation Model (UniPCM), a powerful
foundation model for various conversational tasks and different dialog systems.
Extensive experiments have shown that UniPCM is robust to input prompts and
capable of various dialog-related tasks. Moreover, UniPCM has strong transfer
ability and excels at low resource scenarios, achieving SOTA results on 9
different datasets ranging from task-oriented dialog to open-domain
conversation. Furthermore, we are amazed to find that TAP can generate prompts
on par with those collected with crowdsourcing. The code is released with the
paper.

使用任务驱动的自动提示生成（TAP）生成高质量提示来扩展预先训练的会话模型的数据集，从而构建了适用于各种对话任务和不同对话系统的通用会话预训练模型（UniPCM），该模型表现出强鲁棒性、卓越的迁移能力，可在低资源场景下实现九个不同数据集上的最新结果。

UniPCM：具备任务感知自动提示的通用预训练对话模型

UniPCM: Universal Pre-trained Conversation Model with Task-aware  Automatic Prompt

Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we tackle this problem by exploiting the intrinsic relationship between
the semantic space manifold and the transfer ability of visual-semantic
mapping. We formalize their connection and cast zero-shot recognition as a
joint optimization problem. Motivated by this, we propose a novel framework for
zero-shot recognition, which contains dual visual-semantic mapping paths. Our
analysis shows this framework can not only apply prior semantic knowledge to
infer underlying semantic manifold in the image feature space, but also
generate optimized semantic embedding space, which can enhance the transfer
ability of the visual-semantic mapping to unseen classes. The proposed method
is evaluated for zero-shot recognition on four benchmark datasets, achieving
outstanding results.

本研究旨在通过利用语义空间流形与视觉 - 语义映射之间的内在关系来解决零样本识别问题，并提出了一个包含两条双重视觉 - 语义映射路径的框架，可以提高视觉 - 语义映射的迁移能力，并在四个基准数据集上取得了优异的结果。