44 accepted papers
Artificial Neural Networks (ANNs) are powerful tools for complex decision-making tasks. While existing activation mechanisms often promote sparsity through thresholding, they lack explicit awareness of feature channel relevance, causing networks to continuously suffer from interference by noisy chan…
Label-free image anomaly detection is difficult because anomalies must be separated from intra-normal variability. Diffusion models learn a manifold for normal data, and, under the common assumption that off-manifold anomalies are harder to generate and yield larger prediction errors, many methods b
Computational pathology has made progress in diagnosis and prognosis prediction from whole slide images (WSIs), yet pipelines still rely on patch-level feature extraction and aggregation, departing from the cell-centric reasoning used by pathologists. This gap limits sensitivity to micro-lesions and…
Real-time, high-fidelity monocular depth estimation from remote sensing imagery is crucial for numerous applications, yet existing methods face a stark trade-off between accuracy and efficiency. Although using Vision Transformer (ViT) backbones for dense prediction is fast, they often exhibit poor p
Deep models are vulnerable to performance degradation caused by various factors, such as imbalanced samples, inaccurate labels, and backdoor attacks. However, existing optimization methods that address these issues are typically designed in a scenario- or architecture-specific manner, and each optim…
Quantized large language models (LLMs) have gained increasing attention and significance for enabling deployment in resource-constrained environments. However, emerging studies on a few calibration dataset-free quantization methods suggest that quantization may compromise the safety capabilities of…
The applicability of drug molecules in various clinical scenarios is significantly influenced by a diverse range of molecular properties. By leveraging self-supervised conditions such as atom attributes and interatomic bonds, existing advanced molecular foundation models can generate expressive repr…
Fraudulent activities have caused substantial negative social impacts and are exhibiting emerging characteristics such as intelligence and industrialization, posing challenges of high-order interactions, intricate dependencies, and the sparse yet concealed nature of fraudulent entities. Existing gra…
With the swift progression of image generation technology, the widespread emergence of facial deepfakes poses significant challenges to the field of security, thus amplifying the urgent need for effective deepfake detection. Existing techniques for face forgery detection can broadly be categorized i
Dense object segmentation is essential for various applications, particularly in pathology image and remote sensing image analysis. However, distinguishing numerous similar and densely packed objects in this task presents significant challenges. Several methods, including CNN- and ViT-based approach
Few-shot object detection (FSOD), aiming to enhance the performance of novel object detection with limited labeled samples, has recently gained significant attention. Recent researches primarily focus on improving the generalization of novel classes and enhancing detector performance. However, the d…
Fraud is increasingly prevalent, and its patterns are frequently changing, posing challenges for fraud detection methods such as random forests and Graph Neural Networks (GNNs), which rely on bin-based and mixture features separately. The former may lose crucial graph-associated features, while the…
Pathology image segmentation plays a pivotal role in artificial digital pathology diagnosis and treatment. Existing approaches to pathology image segmentation are hindered by labor-intensive annotation processes and limited accuracy in tail-class identification, primarily due to the long-tail distri…
With the rise of AIGC technologies, particularly diffusion models, highly realistic fake images that can deceive human visual perception has become feasible. Consequently, various forgery detection methods have emerged. However, existing methods treat the generation process of fake images as either…
Pathology images are considered the ``gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information. Existing methods fail to simultaneously extract global structural and local detail features for comprehensive pathology image analysis e
The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusi…
Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task. Recently emerged, vision-based navigation has been a promising and feasible alternativ…
Deep learning-based methods significantly advance the exploration of associations among triple-wise biological entities (e.g., drug-target protein-adverse reaction), thereby facilitating drug discovery and safeguarding human health. However, existing researches only focus on entity-centric informati…
Fraud detection has increasingly become a prominent research field due to the dramatically increased incidents of fraud. The complex connections involving thousands, or even millions of nodes, present challenges for fraud detection tasks. Many researchers have developed various graph-based methods t…
The emergence of AIGC has brought attention to the issue of generating realistic deceptive content. While AIGC has the potential to revolutionize content creation, it also facilitates criminal activities. Specifically, the manipulation of speech has been exploited in tele-fraud and financial fraud s…
The design of Artificial Neural Network (ANN) is inspired by the working patterns of the human brain. Connections in biological neural networks are sparse, as they only exist between few neurons. Meanwhile, the sparse representation in ANNs has been shown to possess significant advantages. Activatio…
Artificial Neural Networks (ANNs) have gained widespread applications across various areas in recent years. The ANN design was initially inspired by principles of biology. The biological neural network's fundamental response process comprises information transmission and aggregation. The information…
Pathological slides are commonly gigapixel images with abundant information and are therefore significant for clinical diagnosis. However, the ultra-large size makes both training and evaluation extremely time-consuming. Most existing methods need to crop the slide into patches, which also leads to…
Convolutional Neural Networks (CNNs) are well-known for their vulnerability to adversarial attacks, posing significant security concerns. In response to these threats, various defense methods have emerged to bolster the model's robustness. However, most existing methods either focus on learning from…
"Knowledge distillation (KD) is a particular technique of model compression that exploits a large well-trained teacher neural network to train a small student network . Treating teacher’s feature as knowledge, prevailing methods train student by aligning its features with the teacher’s, e.g., by min…
With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new mo…
Compared to conventional semantic segmentation with pixel-level supervision, weakly supervised semantic segmentation (WSSS) with image-level labels poses the challenge that it commonly focuses on the most discriminative regions, resulting in a disparity between weakly and fully supervision scenarios…
At present, deep learning has made impressive achievements in various fields; however, effectively training deep neural networks on small data sets remains a significant challenge. Transfer learning, as a method of efficient training across multiple tasks, has been widely used to solve this problem.…
Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mech…
A surge of interest has emerged in utilizing Transformers in diverse vision tasks owing to its formidable performance. However, existing approaches primarily focus on optimizing internal model architecture designs that often entail significant trial and error with high burdens. In this work, we prop…
Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of i…
Microvascular invasion (MVI) is a critical factor for prognosis evaluation and cancer treatment. The current diagnosis of MVI relies on pathologists to manually find out cancerous cells from hundreds of blood vessels, which is time-consuming, tedious, and subjective. Recently, deep learning has achi…
Value Decomposition (VD) aims to deduce the contributions of agents for decentralized policies in the presence of only global rewards, and has recently emerged as a powerful credit assignment paradigm for tackling cooperative Multi-Agent Reinforcement Learning (MARL) problems. One of the main challe…
Deep learning with noisy labels is challenging and inevitable in many circumstances. Existing methods reduce the impact of noise samples by reducing loss weights of uncertain samples or by filtering out potential noise samples, which highly rely on the model's superior discriminative power for ident…
Deep learning has recently achieved remarkable performance in image classification tasks, which depends heavily on massive annotation. However, the classification mechanism of existing deep learning models seems to contrast to humans' recognition mechanism. With only a glance at an image of the obje…
Recently, Convolutional Neural Network (CNN) has achieved excellent performance in the classification task. It is widely known that CNN is deemed as a 'blackbox', which is hard for understanding the prediction mechanism and debugging the wrong prediction. Some model debugging and explanation works a…
Given a reference object of an unknown type in an image, human observers can effortlessly find the objects of the same category in another image and precisely tell their visual boundaries. Such visual cognition capability of humans seems absent from the current research spectrum of computer vision.…
The microvascular invasion (MVI) is a major prognostic factor in hepatocellular carcinoma, which is one of the malignant tumors with the highest mortality rate. The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, wh…
Detection and segmentation of nuclei are fundamental analysis operations in pathology images, the assessments derived from which serve as the gold standard for cancer diagnosis. Manual segmenting nuclei is expensive and time-consuming. What's more, accurate segmentation detection of nuclei can be ch…
When confronted with objects of unknown types in an image, humans can effortlessly and precisely tell their visual boundaries. This recognition mechanism and underlying generalization capability seem to contrast to state-of-the-art image segmentation networks that rely on large-scale category-aware…
Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like f…
The ability to disassemble the features of objects and background is crucial for many machine learning tasks, including image classification, image editing, visual concepts learning, and so on. However, existing (semi-)supervised methods all need a large amount of annotated samples, while unsupervis…
Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely…
The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that…