In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in the video that are relevant to the target image. This problem combines the traditional video object segmentation task with an additional image indicating the content that users are concerned with. Since no existing dataset is perfectly suitable for this new task, we specifically construct a large-scale dataset called LiveVideos, which contains 2418 pairs of target images and live videos with instance-level annotations. In addition, we propose a transformer-based method for this task. We revisit Swin Transformer and design a dual-path structure to fuse video and image features. Then, a transformer decoder is employed to generate object proposals for segmentation and tracking from the fused features. Extensive experiments on LiveVideos dataset show the superiority of our proposed method.

运动转移旨在将驱动视频的运动转移到源图像。当驾驶视频中的对象与源图像中的对象之间存在很大差异时，传统的单个域运动转移方法通常会产生显着的伪影。例如，合成的图像可能无法保留源图像的人类形状（参见图1（a））。为了解决这个问题，在这项工作中，我们提出了一种运动和外观适应（MAA）进行跨域运动转移的方法，在该方法中，我们将合成图像中的对象正规化，以捕获驾驶框架中对象的运动，而仍保留对象在源图像中的形状和外观。一方面，考虑合成图像和驾驶框架的对象形状可能有所不同，我们设计了一个形状不变的运动适应模块，该模块可以在两个图像中强制对象零件的角度的一致性来捕获运动信息。另一方面，我们引入了一个结构引导的外观一致性模块，旨在使合成图像的相应贴片和源图像之间的相似性正式化，而不会影响合成图像中学习的运动。我们提出的MAA模型可以通过循环重建损失以端到端的方式进行训练，并最终产生令人满意的运动转移结果（参见图1（b））。我们在人类舞蹈数据集Mixamo-Video上进行了广泛的实验，以便于时尚视频和人脸数据集vox-celeb到cufs；在这两个方面，我们的MAA模型在定量和定性上都优于现有方法。

图像动画旨在使用从驾驶视频中学到的运动来对源图像进行动画映像。当前的最新方法通常使用卷积神经网络（CNN）来预测运动信息，例如运动关键点和相应的局部变换。但是，这些基于CNN的方法并未明确对运动之间的相互作用进行建模。结果，可能会忽略重要的基础运动关系，这可能会导致生成的动画视频中产生明显的伪影。为此，我们提出了一种新方法，即运动变压器，这是基于视觉变压器构建运动估计器的首次尝试。更具体地说，我们在提出的方法中介绍了两种类型的令牌：i）由补丁特征和相应位置编码形成的图像令牌； ii）用运动信息编码的运动令牌。两种类型的令牌都被发送到视觉变压器中，以通过多头自我注意力块促进它们之间的基本相互作用。通过采用此过程，可以更好地学习运动信息以提高模型性能。然后，最终嵌入式运动令牌用于预测相应的运动关键点和局部变换。基准数据集上的广泛实验表明，我们提出的方法为最先进的基准取得了令人鼓舞的结果。我们的源代码将公开可用。

布局生成是计算机视觉中的一项新任务，它结合了对象本地化和美学评估中的挑战，在广告，海报和幻灯片设计中广泛使用。准确而愉快的布局应考虑布局元素内的内域关系以及布局元素与图像之间的域间关系。但是，大多数以前的方法只是专注于图像 - 范围 - 不平衡的布局生成，而无需利用图像中复杂的视觉信息。为此，我们探索了一个名为“图像条件的布局生成”的新颖范式，该范式旨在以语义连贯的方式将文本叠加层添加到图像中。具体而言，我们提出了一个图像条件的变分变压器（ICVT），该变形变压器（ICVT）在图像中生成各种布局。首先，采用自我注意的机制来对布局元素内的上下文关系进行建模，而交叉注意机制用于融合条件图像的视觉信息。随后，我们将它们作为有条件变异自动编码器（CVAE）的构件，表现出吸引人的多样性。其次，为了减轻布局元素域和视觉域之间的差距，我们设计了一个几何对齐模块，其中图像的几何信息与布局表示形式对齐。此外，我们构建了一个大规模的广告海报布局设计数据集，并具有精致的布局和显着图。实验结果表明，我们的模型可以在图像的非侵入区域中自适应生成布局，从而产生和谐的布局设计。

在本文中，我们研究了为给定图像生成高质量视觉文本演示设计的图形布局生成问题。我们注意到，不仅包含全局语义和空间信息的图像组成在很大程度上会影响布局结果。因此，我们提出了一个深层生成模型，称为组成感知图形布局GAN（CGL-GAN），以基于输入图像的全局和空间视觉内容来合成布局。为了从已经包含手动设计的图形布局数据的图像中获取训练图像，先前的工作建议将设计元素（例如文本和点缀）作为模型输入，这不可避免地会留下地面真相的提示。我们研究训练输入（带有提示掩码）和测试输入（没有掩模）之间的错位，并设计一个新型的域比对模块（DAM）以缩小此间隙。为了培训，我们构建了一个大规模布局数据集，该数据集由60,548张广告海报组成，并带有带注释的布局信息。为了评估生成的布局，我们根据美学直觉提出了三个新型指标。通过定量和定性评估，我们证明了所提出的模型可以根据图像组成合成高质量的图形布局。

As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation CORGI-PM, which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. Moreover, we address three challenges for automatic textual gender bias mitigation, which requires the models to detect, classify, and mitigate textual gender bias. We also conduct experiments with state-of-the-art language models to provide baselines. To our best knowledge, CORGI-PM is the first sentence-level Chinese corpus for gender bias probing and mitigation.

We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thought not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness.

Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but current assessment method only uses coronal projection image and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch, a two-step framework to detect vertebral structures in 3D ultrasound volume by utilizing unlabeled data in semi-supervised manner. The first step is to detect the possible positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The second step is to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the first step. VertMatch develops three novel components for semi-supervised learning: for position detection in the first step, (1) anatomical prior is used to screen pseudo labels generated from confidence threshold method; (2) multi-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices; (3) for patch identification in the second step, the categories are rebalanced in each batch to solve imbalance problem. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. VertMatch is also validated in clinical application on forty ultrasound scans, and it can be a promising approach for 3D assessment of scoliosis.

To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ large-scale text-video dataset for fine-tuning. However, such paradigm is computationally expensive. Humans have the amazing ability to learn new visual concepts from just one single exemplar. We hereby study a new T2V generation problem$\unicode{x2014}$One-Shot Video Generation, where only a single text-video pair is presented for training an open-domain T2V generator. Intuitively, we propose to adapt the T2I diffusion model pretrained on massive image data for T2V generation. We make two key observations: 1) T2I models are able to generate images that align well with the verb terms; 2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency. To further learn continuous motion, we propose Tune-A-Video with a tailored Sparse-Causal Attention, which generates videos from text prompts via an efficient one-shot tuning of pretrained T2I diffusion models. Tune-A-Video is capable of producing temporally-coherent videos over various applications such as change of subject or background, attribute editing, style transfer, demonstrating the versatility and effectiveness of our method.

We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models.