Revisiting face forgery detection towards generalization.

Face forgery detection aims to distinguish AI generated fake faces with real faces. With the rapid development of face forgery creation algorithms, a large number of generative models have been proposed, which gradually reduce the local distortion phenomenon or the specific frequency traces in these models. At the same time, in the process of face data compression and transmission, distortion phenomenon and specific frequency cues could be eliminated, which brings severe challenges to the performance and generalization ability of face forgery detection.

Comprehensive Proteomic Profiling of Exfoliation Glaucoma Via Mass Spectrometry Reveals SVEP1 as a Potential Biomarker.

Purpose: This study investigated the proteomic landscape of exfoliation glaucoma to find potential biomarkers.

Methods: The study enrolled 34 patients diagnosed with either exfoliation syndrome with/without glaucoma or age-related cataract. Plasma proteins were analyzed through mass spectrometry and Mendelian randomization (MR) based on data from deCODE, FinnGen, Atherosclerosis Risk in Communities (ARIC), eQTLGen, and UK Biobank (UKB) cohorts to infer relationships.

RTF: Recursive TransFusion for Multi-Modal Image Synthesis.

Multi-modal image synthesis is crucial for obtaining complete modalities due to the imaging restrictions in reality. Current methods, primarily CNN-based models, find it challenging to extract global representations because of local inductive bias, leading to synthetic structure deformation or color distortion. Despite the significant global representation ability of transformer in capturing long-range dependencies, its huge parameter size requires considerable training data.

Improving Adversarial Training from the Perspective of Class-Flipping Distribution.

Adversarial training has been proposed and widely recognized as a very effective method to defend against adversarial noise. However, the label flipping pattern on different classes still need deeper exploration to identify potential problems and assist in further enhancing robustness. In this work, we model the class-flipping distribution via statistical investigations and find this distribution reveals two shortcomings: the highly misleading category is present in the model's predictions for data in each class, and the trend in class flipping are significantly different across classes.

Concept-Aware Graph Convolutional Network for Compositional Zero-Shot Learning.

Compositional zero-shot learning (CZSL) aims to identify unobservable compositional concepts with prior knowledge of known primitives (attributes and objects). Due to distribution differences between seen and unseen components, existing methods for CZSL often ignore intrinsic variations between primitives and suffer from domain bias problems. To address this challenge, we proposed a concept-aware graph convolutional network (GCN) that utilizes cross-attentions to extract features unique to attributes and objects from paired concept-sharing inputs.

Dense Information Learning based Semi-Supervised Object Detection.

Semi-Supervised Object Detection (SSOD) aims to improve the utilization of unlabeled data, and various methods, such as adaptive threshold techniques, have been extensively studied to increase exploitable information. However, these methods are passive, relying solely on the original image data. Additionally, existing approaches prioritize the predicted categories of the teacher model while overlooking the relationships between different categories in the prediction.

Asymmetric Adaptive Heterogeneous Network for Multi-Modality Medical Image Segmentation.

Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature extraction and fusion. They often overlook the different contributions to visual representation and intelligent decisions among multi-modality images. Motivated by this discovery, this paper proposes an asymmetric adaptive heterogeneous network for multi-modality image feature extraction with modality discrimination and adaptive fusion.

Diffusion Model-Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment.

Existing free-energy guided No-Reference Image Quality Assessment (NR-IQA) methods continue to face challenges in effectively restoring complexly distorted images. The features guiding the main network for quality assessment lack interpretability, and efficiently leveraging high-level feature information remains a significant challenge. As a novel class of state-of-the-art (SOTA) generative model, the diffusion model exhibits the capability to model intricate relationships, enhancing image restoration effectiveness.

GBCT: Efficient and Adaptive Clustering via Granular-Ball Computing for Complex Data.

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in the human brain, resulting in those methodsbad performance in efficiency, generalization ability, and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering via granular-ball computing.

Manifold Based Multi-View K-Means.

Although numerous clustering algorithms have been developed, many existing methods still rely on the K-means technique to identify clusters of data points. However, the performance of K-means is highly dependent on the accurate estimation of cluster centers, which is challenging to achieve optimally. Furthermore, it struggles to handle linearly non-separable data.

I NQ: Inter and Intra Nonuniform Quantization for Single Image Super-Resolution.

Quantizing neural network is an efficient model compression technique that converts weights and activations from floating-point to integer. However, existing model quantization methods are primarily designed for high-level visual tasks. They do not sufficiently consider the unique characteristics of feature distribution in image super-resolution (SR) reconstruction models.

Early high postoperative IOP improved the predictability of trabeculectomy in patients with POAG: a retrospective cohort study.

Introduction: To investigate the effect of different levels of early intraocular pressure (IOP) on long-term outcomes of patients with primary open-angle glaucoma (POAG) who were treated with primary trabeculectomy.

Methods: This was a retrospective cohort study, with a total of 74 patients (90 eyes) with primary open-angle glaucoma (POAG) who were treated with trabeculectomy surgery at a single center from 2021 to 2022. Based on IOP at 1 day after surgery, they were divided into the high IOP group (≥ 15mmHg) and the low IOP group (< 15mmHg).

Flow Cytometry-Based Isolation and Therapeutic Evaluation of Tumor-Infiltrating Lymphocytes in a Mouse Model of Pancreatic Cancer.

Pancreatic cancer is an aggressive malignancy with a dismal prognosis and limited therapeutic options. Adoptive cell therapy, which involves isolating and activating a patient's own immune cells, such as tumor-infiltrating lymphocytes (TILs), before re-infusing them, represents a promising experimental approach. However, techniques for adoptive cell transfer in preclinical pancreatic cancer models are not well established.

Treatment modalities and trends for hospitalized patients with neovascular glaucoma: A retrospective study of 10 years.

To analyze the treatment modalities and trends for neovascular glaucoma (NVG) over the past 10 years, we conducted a retrospective analysis at Zhongshan Ophthalmic Center on 1331 NVG inpatients who received 1459 treatments for 1383 eyes between January 1, 2012, and December 31, 2021. Over time, we observed a progressive annual increase in both the number of patients and the volume of surgeries for NVG, with an annual percentage change (APC) of 10.23 % (95 % confidence interval [CI]: 5.

RC-DETR: Improving DETRs in crowded pedestrian detection via rank-based contrastive learning.

The variants of DEtection TRansformer (DETRs) have achieved impressive performance in general object detection. However, they suffer notable performance degradation in scenarios involving crowded pedestrian detection. This decline primarily occurs during the training phase, where DETRs are constrained solely by pedestrian labels.

Difficulty-Guided Variant Degradation Learning for Blind Image Super-Resolution.

Recent blind super-resolution (BSR) methods are explored to handle unknown degradations and achieve impressive performance. However, the prevailing assumption in most BSR methods is the spatial invariance of degradation kernels across the entire image, which leads to significant performance declines when faced with spatially variant degradations caused by object motion or defocusing. Additionally, these methods do not account for the human visual system's tendency to focus differently on areas of varying perceptual difficulty, as they uniformly process each pixel during reconstruction.

Lightweight medical image segmentation network with multi-scale feature-guided fusion.

In the field of computer-aided medical diagnosis, it is crucial to adapt medical image segmentation to limited computing resources. There is tremendous value in developing accurate, real-time vision processing models that require minimal computational resources. When building lightweight models, there is always a trade-off between computational cost and segmentation performance.

Investigating the uncertain causal link between gut microbiota and glaucoma: A genetic correlation and Mendelian randomisation study.

Background: Glaucoma is the most common cause of irreversible blindness, and gut microbiota (GM) is associated with glaucoma. Whether this association represents a causal role remains unknown. This study aims to assess the potential association and causal link between GM and various forms of glaucoma, emphasising the need for cautious interpretation of the strength of these associations.

Boundary-Aware Prototype in Semi-Supervised Medical Image Segmentation.

The true label plays an important role in semi-supervised medical image segmentation (SSMIS) because it can provide the most accurate supervision information when the label is limited. The popular SSMIS method trains labeled and unlabeled data separately, and the unlabeled data cannot be directly supervised by the true label. This limits the contribution of labels to model training.

Adapting Few-Shot Classification via In-Process Defense.

Most few-shot learning methods employ either adaptive approaches or parameter amortization techniques. However, their reliance on pre-trained models presents a significant vulnerability. When an attacker's trigger activates a hidden backdoor, it may result in the misclassification of images, profoundly affecting the model's performance.

The Burden and Clinical Features of Neovascular Glaucoma in a Major Tertiary Care Center in China.

Prcis: This research presents the burden and clinical characteristics of NVG in Zhongshan Ophthalmic Center, employing the most extensive sample size and the longest uninterrupted temporal duration so far, which may provide a theoretical reference for the effective prevention and diagnosis of NVG.

Purpose: To summarize the burden and clinical characteristics of neovascular glaucoma (NVG) in a major tertiary care center in China.

Methods: The clinical data of NVG patients in Zhongshan Ophthalmic Center (ZOC) between 2012 and 2021 were collected retrospectively, including their age, sex, the affected eye, best-corrected visual acuity (BCVA), intraocular pressure (IOP), clinical stage and etiology.

Image-Level Adaptive Adversarial Ranking for Person Re-Identification.

The potential vulnerability of deep neural networks and the complexity of pedestrian images, greatly limits the application of person re-identification techniques in the field of smart security. Current attack methods often focus on generating carefully crafted adversarial samples or only disrupting the metric distances between targets and similar pedestrians. However, both aspects are crucial for evaluating the security of methods adapted for person re-identification tasks.

Local artifacts amplification for deepfakes augmentation.

With the rapid and continuous development of AIGC, It is becoming increasingly difficult to distinguish between real and forged facial images, which calls for efficient forgery detection systems. Although many detection methods have noticed the importance of local artifacts, there has been a lack of in-depth discussion regarding the selection of locations and their effective utilization. Besides, the traditional image augmentation methods that are widely used have limited improvements for forgery detection tasks and require more specialized augmentation methods specifically designed for forgery detection tasks.