10 accepted papers
In light of recent breakthroughs in text-to-image (T2I) generation, particularly with diffusion transformers (DiT), subject-driven technologies are increasingly being employed for high-fidelity customized production that preserves subject identity from reference inputs, enabling thrilling design wor…
Automated radiology report generation is essential for improving diagnostic efficiency and reducing the workload of medical professionals. However, existing methods face significant challenges, such as disease class imbalance and insufficient cross-modal fusion. To address these issues, we propose t…
Malignant brain tumors have become an aggressive and dangerous disease that leads to death worldwide. Multi-modal MRI data is crucial for accurate brain tumor segmentation, but missing modalities common in clinical practice can severely degrade the segmentation performance. While incomplete multi-mo…
Medical Image Segmentation (MIS) plays a crucial role in medical therapy planning and robot navigation. Prototype learning methods in MIS focus on generating segmentation masks through pixel-to-prototype comparison. However, current approaches often overlook sample diversity by using a fixed prototy…
Recent thrilling progress in large-scale text-to-image (T2I) models has unlocked unprecedented synthesis quality of AI-generated content (AIGC) including image generation 3D and video composition. Further personalized techniques enable appealing customized production of a novel concept given only se…
Normalization and residual connections find extensive application within the intricate architecture of deep neural networks, contributing significantly to their heightened performance. Nevertheless, the precise factors responsible for this elevated performance have remained elusive. Our theoretical…
Online hashing for streaming data has attracted increasing attention recently. However, most existing algorithms focus on batch inputs and instance-balanced optimization, which is limited in the single datum input case and does not match the dynamic training in online hashing. Furthermore, constantl…
Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta…
Cross-modal sketch-photo recognition is of vital importance in law enforcement and public security. Most existing methods are dedicated to bridging the gap between the low-level visual features of sketches and photo images, which is limited due to intrinsic differences in pixel values. In this paper…
Person reidentification refers to the task of recognizing the same person under different non-overlapping camera views. Presently, person reidentification based on metric learning is proved to be effective among various techniques, which exploits the labeled data to learn a subspace that maximizes t…