17 accepted papers
With the expanding use of artificial intelligence generated images (AGIs) in scenarios such as gaming, art, and film production, evaluating their quality is essential to ensure their practical utility. To guarantee effective quality measurement, both content and distortion must be considered. Howeve
As a typical information medium, images are widely utilized across various scenarios. Measuring image quality accurately is meaningful for the subsequent usability of images. However, significant variations exist in image types and distortion types in different scenarios. And, acquiring labeled imag
Deep hashing is widely used for large-scale image retrieval but remains vulnerable to adversarial examples, raising practical security concerns. Existing targeted adversarial attacks on deep hashing typically rely on lp-norm constrained perturbations, which struggle to balance attack effectiveness a
Graph Neural Networks (GNNs) have been shown vulnerable to graph adversarial attacks. Current robust graph representation learning methods mainly defend against graph structure attack, and improves performance of GNNs. However node feature in graph can been easily attacked in reality. The joint defe…
Conventional image set methods typically learn from image sets stored in a single location. However, in real-world applications, image sets are often distributed across different locations. Learning from such distributed sets using deep neural networks poses challenges for efficient image set classi
Deep hashing have achieved exceptional performance in retrieval tasks due to their robust representational capabilities. However, they inherit the vulnerability of deep neural networks to adversarial attacks. These models are susceptible to finely crafted adversarial perturbations that can lead them…
Deep hashing models have achieved great success in retrieval tasks due to their powerful representation and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capabilit…
The low resolution (LR) problem is rather challenging in face analysis. Most existing face hallucination methods assume that LR face images have only one resolution, but multiple resolutions may be available from different sources. To solve this issue, we propose a novel simultaneous facial canonica…
Ancient Chinese poetry stands as a crucial treasure in Chinese culture. To address the absence of pre-trained models for ancient poetry, we introduced PoemBERT, a BERT-based model utilizing a corpus of classical Chinese poetry. Recognizing the unique emotional depth and linguistic precision of poetr…
As video-based social networks continue to grow exponentially, there is a rising interest in video retrieval using natural language. Cross-modal hashing, which learns compact hash code for encoding multi-modal data, has proven to be widely effective in large-scale cross-modal retrieval, e.g., image-…
Hashing utilizes hash code as a compact image representation, offering excellent performance in large-scale image retrieval due to its computational and storage advantages. However, the prevalence of degraded images on social media platforms, resulting from imperfections in the image capture process…
Conventional image set methods typically learn from image sets stored in one location. However, in real-world applications, image sets are often distributed or collected across different positions. Learning from such distributed image sets presents a challenge that has not been studied thus far. Mor…
Face super-resolution (FSR) is a powerful technique for restoring high-resolution face images from the captured low-resolution ones with the assistance of prior information. Existing FSR methods based on explicit or implicit covariance matrices are difficult to reveal complex nonlinear relationships…
Graph Neural Network (GNN) is powerful in graph embedding learning, but its performance has been shown to be heavily degraded under adversarial attacks. Deep graph structure learning (GSL) is proposed to defend attack by jointly learning graph structure and graph embedding, typically in node classif…
Canonical correlation analysis (CCA) matters in multiview representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of cov…
The pioneering work of sparse local embeddings for extreme classification (SLEEC) (Bhatia et al., 2015) has shown great promise in multi-label learning. Unfortunately, the statistical rate of convergence and oracle property of SLEEC are still not well understood. To fill this gap, we present a unifi…
The $k$-means clustering algorithm is a ubiquitous tool in data mining and machine learning that shows promising performance. However, its high computational cost has hindered its applications in broad domains. Researchers have successfully addressed these obstacles with dimensionality reduction met…