Recently, instruction-driven image editing methods have demonstrated promising capabilities, requiring only a brief text to guide image modifications. However, most of them often yield suboptimal results for object editing in complex scenes, due to two major defects: (1) Over-editing, where unintended regions of the image are inadvertently altered; (2) Inability to precisely adhere to instructions, particularly in scenes with numerous elements. To resolve these issues, we propose a Single object Editing scheme, termed SoEdit, which distills complex editing tasks into single-object editing within cropped regions through a pipeline that integrates task parsing, object localization, editing, and context blending. This approach minimizes interference from irrelevant areas, ensures proper object size and placement, and ultimately enhances model performance. Furthermore, we introduce a lightweightSpatially-Adaptive Mixture of Experts (SAMOE)to better model spatial heterogeneity, enabling tokenwise adaptive processing and further enhancing the overall editing capability with minimal additional parameters. Moreover, we introduce a large-scale object-centric dataset to further optimize the model. Extensive experiments demonstrate that SoEdit outperforms existing methods, especially in precise responses to fine-grained editing requirements, such as multi-action and quantity-sensitive object editing.

Video Compression Artifact Reduction aims to reduce the artifacts caused by video compression algorithms and improve the quality of compressed video frames. The critical challenge in this task is to make use of the redundant high-quality information in compressed frames for compensation as much as possible. Two important possible compensations: Motion compensation and global context, are not comprehensively considered in previous works, leading to inferior results. The key idea of this paper is to fuse the motion compensation and global context together to gain more compensation information to improve the quality of compressed videos. Here, we propose a novel Spatio-Temporal Compensation Fusion (STCF) framework with the Parallel Swin-CNN Fusion (PSCF) block, which can simultaneously learn and merge the motion compensation and global context to reduce the video compression artifacts. Specifically, a temporal self-attention strategy based on shifted windows is developed to capture the global context in an efficient way, for which we use the Swin transformer layer in the PSCF block. Moreover, an additional Ada-CNN layer is applied in the PSCF block to extract the motion compensation. Experimental results demonstrate that our proposed STCF framework outperforms the state-of-the-art methods up to 0.23dB (27% improvement) on the MFQEv2 dataset.

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic Image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://github.com/WuyangLuo/SIEDOB.

Face inpainting aims at plausibly predicting missing pixels of face images within a corrupted region. Most existing methods rely on generative models learning a face image distribution from a big dataset, which produces uncontrollable results, especially with large-scale missing regions. To introduce strong control for face inpainting, we propose a novel reference-guided face inpainting method that fills the large-scale missing region with identity and texture control guided by a reference face image. However, generating high-quality results under imposing two control signals is challenging. To tackle such difficulty, we propose a dual control one-stage framework that decouples the reference image into two levels for flexible control: High-level identity information and low-level texture information, where the identity information figures out the shape of the face and the texture information depicts the component-aware texture. To synthesize high-quality results, we design two novel modules referred to as Half-AdaIN and Component-Wise Style Injector (CWSI) to inject the two kinds of control information into the inpainting processing. Our method produces realistic results with identity and texture control faithful to reference images. To the best of our knowledge, it is the first work to concurrently apply identity and component-level controls in face inpainting to promise more precise and controllable results.

In this paper, we investigate the problem of optimizing the overall system secrecy spectral efficiency of cell-Free massive multiple-input multiple-output (CF-mMIMO) with network-assisted full-duplexing (NAFD) system, considering resisting interception by superimposing artificial noise (AN) on the transmitted signal. The access points (APs) are selected by binary mode selection vectors as transmitting or receiving APs flexibly to serve both uplink and downlink users simultaneously. Since optimization variables are tightly coupled, a double-loop strategy is developed to solve the non-covex combinatorial optimization problem. The outer loop is constructed according to greedy search for duplex mode selection, while the inner loop based on the proposed successive convex approximation (SCA) algorithm aims to optimize the transceivers and AN. Simulation results show that the proposed solution is superior to the fixed-mode duplex scheme in terms of secure spectral efficiency and able to achieve similar performance to that of the optimal exhaustive search scheme.