DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model. It can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model. Based on this guidance strategy, we also build a multi-scale guidance to consider both semantic and geometric alignment. Moreover, a cross-branch self-attention is added to maintain the consistency between the original image and the editing result. Our method, through an efficient design, achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging. It is worth noting that all editing and content preservation signals come from the image itself, and the model does not require fine-tuning or additional modules. Our source code will be available at https://github.com/MC-E/DragonDiffusion

DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing

Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing remains challenging. In this paper, we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios, editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations, e.g., imagine new content. In our solution, we introduce image prompts in fine-grained image editing, cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency, we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition, we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling, further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks, including editing within a single image (e.g., object moving, resizing, and content dragging) and across images (e.g., appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion

Large-capacity and Flexible Video Steganography via Invertible Neural Network

Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility. The source code is available at https://github.com/MC-E/LF-VSN.Comment: Accepted by CVPR 202

Neural Video Fields Editing

Diffusion models have revolutionized text-driven video editing. However, applying these methods to real-world editing encounters two significant challenges: (1) the rapid increase in GPU memory demand as the number of frames grows, and (2) the inter-frame inconsistency in edited videos. To this end, we propose NVEdit, a novel text-driven video editing framework designed to mitigate memory overhead and improve consistent editing for real-world long videos. Specifically, we construct a neural video field, powered by tri-plane and sparse grid, to enable encoding long videos with hundreds of frames in a memory-efficient manner. Next, we update the video field through off-the-shelf Text-to-Image (T2I) models to impart text-driven editing effects. A progressive optimization strategy is developed to preserve original temporal priors. Importantly, both the neural video field and T2I model are adaptable and replaceable, thus inspiring future research. Experiments demonstrate the ability of our approach to edit hundreds of frames with impressive inter-frame consistency. Our project is available at: https://nvedit.github.io/

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate structure control is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and small T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, and achieve rich control and editing effects. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.Comment: Tech Report. GitHub: https://github.com/TencentARC/T2I-Adapte

Impact of ovarian preservation in women with endometrial cancer

AbstractBackgroundBilateral salpingo-oophorectomy (BSO) is standardly performed in the treatment of endometrial cancer. The purpose of this study was to evaluate the impact of ovarian preservation on the outcome of patients with endometrial cancer.MethodsA retrospective cohort study was performed using the 2000–2010 database of endometrial cancer patients who were treated at Taipei Veterans General Hospital. Information regarding patient age, pathologic reports, and follow-up results was abstracted from medical records.ResultsFive hundred and twenty-nine patients were reviewed in this study. Mean age and follow-up duration were 55.7 ± 11.4 years and 37.5 ± 30.1 months, respectively. The median disease-free survival was 31.2 months (range 0.2–126.9 months). There were no significant differences in disease-free survival between stage I patients with ovarian preservation versus those with oophorectomy (p = 0.473). In a multivariate Cox model, ovarian preservation had no effect on disease-free survival [hazard ratio (HR) = 2.72; 95% confidence interval (CI), 0.48–15.59]; however, it was not significantly related to stage and para-aortic lymph node involvement.ConclusionOvarian preservation may be considered in premenopausal women with early-stage low-risk endometrial cancer

The Effect of the Irreversible Inequality on Pro-social Behaviors of People With Disabilities

Inequalities have always been central to psychology, sociology and related fields such as social policy, gender studies, critical race studies, and human geography. Although inequality affects pro-social behaviors, there are still some controversies over this issue among people with disabilities. The current study aimed to investigate pro-social behaviors of people with disabilities and the effect of the irreversible inequality on pro-social behaviors. A dictator game was used to explore the difference of pro-social behaviors between people with disabilities and people without disabilities, when facing intra- or inter-group members. The results showed that compared to people with disabilities, people without disabilities were likely to show more pro-social behaviors. People with disabilities preferred intra-group cooperation, while people without disabilities preferred inter-group cooperation. Indeed, the intra-group cooperation was significantly greater than the expected cooperation of the intra-group members for people with disabilities. When facing the inter-group members, people without disabilities showed more than expected, that others would cooperate with them. These findings indicated that social avoidance was a common phenomenon for people with disabilities in China, but the situation would be different when they faced different groups. In addition, irreversible inequality could influence individuals’ cooperative strategies when facing individuals in a different status

Regulatory T Cells: Potential Target in Anticancer Immunotherapy

SummaryThe concept of regulatory T cells was first described in the early 1970s, and regulatory T cells were called suppressive T cells at that time. Studies that followed have demonstrated that these suppressive T cells negatively regulated tumor immunity and contributed to tumor growth in mice. Despite the importance of these studies, there was extensive skepticism about the existence of these cells, and the concept of suppressive T cells left the center stage of immunologic research for decades. Interleukin-2 receptor α-chain, CD25, was first demonstrated in 1995 to serve as a phenotypic marker for CD4+ regulatory cells. Henceforth, research of regulatory T cells boomed. Regulatory T cells are involved in the pathogenesis of cancer, autoimmune disease, transplantation immunology, and immune tolerance in pregnancy. Recent evidence has demonstrated that regulatory T cellmediated immunosuppression is one of the crucial tumor immune evasion mechanisms and the main obstacle of successful cancer immunotherapy. The mechanism and the potential clinical application of regulatory T cells in cancer immunotherapy are discussed