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Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes, finding applications in various domains. To achieve the personalization capability, existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset, which can be non-trivial for general users, resource-intensive, and time-consuming. Despite attempts to develop finetuning-free methods, their generation quality is much lower compared to their finetuning counterparts. In this paper, we propose Joint-Image Diffusion (\jedi), an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning, we propose a scalable synthetic dataset generation technique. Once trained, our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines., Comment: CVPR 24

We propose a scalar dark matter model featuring a hidden gauge symmetry, denoted as U(1)_X, with two complex scalars, Phi and S. In this framework, Phi spontaneously breaks the U(1)_X gauge symmetry, while S serves as a viable dark matter candidate. Particularly, the kinetic and direct mixings between the U(1)_X and U(1)_Y gauge groups provide a portal between dark matter and the Standard Model particles. These mixings offer a plausible explanation for the W boson mass anomaly observed by the CDF Collaboration. We study the comprehensive phenomenological constraints of this model from colliders and dark matter detection experiments, including Z' searches at the LHC, the 125 GeV Higgs boson measurements, the relic density of dark matter and the indirect detection of dark matter annihilation. By randomly scanning the parameter space, we find that the regions where m_(Z') > 4750 GeV and m_(Z') < 4750 GeV for g_x close to 1 remain viable and can be tested by future experiments., Comment: 22 pages, 10 figures

Bulk magnetic and thermodynamic measurements, along with mean-field calculations, were conducted on the ferromagnetic K3Gd5(PO4)6 powders. No magnetic ordering was observed until 2 K, while the application of an external field B > 1 T resulted in the splitting of the Gd3+ ground state multiplet and induced a non-cooperative Schottky effect. The average nearest-neighbor exchange strength |J1/kB| is determined to be 0.017 K, which leads to a remarkably large low field magnetic entropy change {\Delta}Sm = 36.2 J kg-1 K-1 under applied field change B = 2 T at temperature T = 2 K, as well as a maximum adiabatic temperature change Tad = 10.9 K. We contend that ferromagnetic gadolinium orthophosphates serve as a promising reservoir for exploring advanced magnetic refrigerants applicable under low magnetic fields., Comment: 7 pages, 5 figures

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability., Comment: CVPR2024

Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors. We present MixNet, a hybrid architecture that combines the strengths of CNNs and Transformers, capable of accurately detecting small text from challenging natural scenes, regardless of the orientations, styles, and lighting conditions. MixNet incorporates two key modules: (1) the Feature Shuffle Network (FSNet) to serve as the backbone and (2) the Central Transformer Block (CTBlock) to exploit the 1D manifold constraint of the scene text. We first introduce a novel feature shuffling strategy in FSNet to facilitate the exchange of features across multiple scales, generating high-resolution features superior to popular ResNet and HRNet. The FSNet backbone has achieved significant improvements over many existing text detection methods, including PAN, DB, and FAST. Then we design a complementary CTBlock to leverage center line based features similar to the medial axis of text regions and show that it can outperform contour-based approaches in challenging cases when small scene texts appear closely. Extensive experimental results show that MixNet, which mixes FSNet with CTBlock, achieves state-of-the-art results on multiple scene text detection datasets.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our approach stands out for its uniqueness, as it relies solely on a single image coming from one patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical physicians to utilize their expertise, a geometry-based rendering of a single lesion image to generate the training set (the \emph{biggest} novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC data-set created by ourselves and achieved a mean Dice score of 0.888, which represents a significant advance toward clinical applications.

Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challenging to create a universal detector capable of generalizing to new, unseen generative models. In this paper, we propose to inject a universal adversarial signature into an arbitrary pre-trained generative model, in order to make its generated contents more detectable and traceable. First, the imperceptible optimal signature for each image can be found by a signature injector through adversarial training. Subsequently, the signature can be incorporated into an arbitrary generator by fine-tuning it with the images processed by the signature injector. In this way, the detector corresponding to the signature can be reused for any fine-tuned generator for tracking the generator identity. The proposed method is validated on the FFHQ and ImageNet datasets with various state-of-the-art generative models, consistently showing a promising detection rate. Code will be made publicly available at \url{https://github.com/zengxianyu/genwm}.

Effective light cones may emerge in non-relativistic local quantum systems from the Lieb-Robinson bounds, resulting in exponentially decaying commutator norms of two space-time separated operators in the Heisenberg picture. Here, we derive a mechanism for the emergence and consequences of a logarithmic light cone (LLC). As a possible way, the LLC can emerge from a phenomenological model of many-body-localization. We show that the information scrambling is logarithmically slow in the regime of the LLC. We prove that the bipartite entanglement entropy grows logarithmically with time for arbitrary finite space dimensions and arbitrary initial pure states. As an application in quantum information processing, the LLC supports long-lived quantum memory after unitary time evolution: a quantum code with macroscopic code distance and exponentially long lifetime., Comment: 7+3 pages, 2 figures.In version 2, typos were fixed; the proof of theorem 1 was slightly modified

The function $\mathrm{P}_{\mathbf{v}}(G)$, measuring the proportion of the elements of a finite group $G$ that are zeros of irreducible characters of $G$, takes (as proved in [12]) only values $\frac{m-1}{m}$, for $1 \leq m \leq 6$, in the interval $[0, \mathrm{P}_{\mathbf{v}}(A_7))$.In this paper, we give a complete classification of the finite groups $G$ such that $\mathrm{P}_{\mathbf{v}}(G)=\frac{m-1}{m}$ for $m=1,2,\cdots ,6$., Comment: arXiv admin note: text overlap with arXiv:2201.00550

Ising (Zeeman-type) spin-orbit coupling (SOC) generated by in-plane inverse asymmetry has attracted considerable attention, especially in Ising superconductors and spin-valley coupling physics. However, many unconventional observations and emerging physical phenomena remain to be elucidated. Here, we theoretically study the spin texture of {\sigma}_z (spin angular momentum projection along z) induced by Ising SOC in 1Td WTe2, and propose an unconventional spin-to-charge conversion named inverse Ising effect, in which the directions of the spin current, spin polarization and charge current are not orthogonal. In particular, we predict the Ising magnetoresistance, whose resistance depends on the out-of-plane magnetic momentum in WTe2/ferromagnetic heterostructure. The Ising magnetoresistance is believed to be an interesting counterpart to the well studied spin Hall magnetoresistance. Our predictions provide promising way to spin-momentum locking and spin-charge conversion based on emerging Ising SOC.

We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired controllability. The framework naturally reduces to text-to-image (T2I) at the lowest level with no shape information, and it becomes segmentation-to-image (S2I) at the highest level. By supporting the levels in-between, our framework is flexible in assisting users of different drawing expertise and at different stages of their creative workflow. We introduce several novel techniques to address the challenges coming with this new setup, including a pipeline for collecting training data; a precision-encoded mask pyramid and a text feature map representation to jointly encode precision level, semantics, and composition information; and a multi-scale guided diffusion model to synthesize images. To evaluate the proposed method, we collect a test dataset containing user-drawn layouts with diverse scenes and styles. Experimental results show that the proposed method can generate high-quality images following the layout at given precision, and compares favorably against existing methods. Project page \url{https://zengxianyu.github.io/scenec/}