With the advancement of image-to-image diffusion models guided by text,
significant progress has been made in image editing. However, a persistent
challenge remains in seamlessly incorporating objects into images based on
textual instructions, without relying on extra user-provided guidance. Text and
images are inherently distinct modalities, bringing out difficulties in fully
capturing the semantic intent conveyed through language and accurately
translating that into the desired visual modifications. Therefore, text-guided
image editing models often produce generations with residual object attributes
that do not fully align with human expectations. To address this challenge, the
models should comprehend the image content effectively away from a disconnect
between the provided textual editing prompts and the actual modifications made
to the image. In our paper, we propose a novel method called Locate and Forget
(LaF), which effectively locates potential target concepts in the image for
modification by comparing the syntactic trees of the target prompt and scene
descriptions in the input image, intending to forget their existence clues in
the generated image. Compared to the baselines, our method demonstrates its
superiority in text-guided image editing tasks both qualitatively and
quantitatively.

借助图像到图像扩散模型和文本引导，本文提出了一种名为 “定位与遗忘”（LaF）的新方法，通过比较目标提示的句法树和输入图像中的场景描述，有效地定位图像中需要修改的潜在目标概念，并在生成的图像中消除其存在的线索，从而在定性和定量方面表现出在文本引导图像编辑任务中的卓越性能。

利用自动概念定位与遗忘进行文本引导的图像编辑

Text Guided Image Editing with Automatic Concept Locating and Forgetting

Images produced by text-to-image diffusion models might not always faithfully
represent the semantic intent of the provided text prompt, where the model
might overlook or entirely fail to produce certain objects. Existing solutions
often require customly tailored functions for each of these problems, leading
to sub-optimal results, especially for complex prompts. Our work introduces a
novel perspective by tackling this challenge in a contrastive context. Our
approach intuitively promotes the segregation of objects in attention maps
while also maintaining that pairs of related attributes are kept close to each
other. We conduct extensive experiments across a wide variety of scenarios,
each involving unique combinations of objects, attributes, and scenes. These
experiments effectively showcase the versatility, efficiency, and flexibility
of our method in working with both latent and pixel-based diffusion models,
including Stable Diffusion and Imagen. Moreover, we publicly share our source
code to facilitate further research.

通过对比背景下处理此问题的新方法，我们有效地展示了我们的方法在处理各种不同类型的文字到图像扩散模型中的对象、特征和背景组合时的多功能性、高效性和灵活性。

CONFORM：高保真文本到图像扩散模型所需的全部是对比度

CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image  Diffusion Models

Existing work on software patches often use features specific to a single
task. These works often rely on manually identified features, and human effort
is required to identify these features for each task. In this work, we propose
CC2Vec, a neural network model that learns a representation of code changes
guided by their accompanying log messages, which represent the semantic intent
of the code changes. CC2Vec models the hierarchical structure of a code change
with the help of the attention mechanism and uses multiple comparison functions
to identify the differences between the removed and added code.
To evaluate if CC2Vec can produce a distributed representation of code
changes that is general and useful for multiple tasks on software patches, we
use the vectors produced by CC2Vec for three tasks: log message generation, bug
fixing patch identification, and just-in-time defect prediction. In all tasks,
the models using CC2Vec outperform the state-of-the-art techniques.

使用人工识别特征并需要为每个任务付出人力劳动的软件补丁现有方法存在缺陷，本文提出一种基于神经网络的 CC2Vec 模型，通过伴随的日志消息学习代码更改的表示，以识别代码更改的层次结构并使用多个比较函数来识别删除和添加代码之间的差异，并证明该模型在多个软件补丁任务中均优于现有技术。