21 accepted papers
Infrared and visible image fusion aims to integrate complementary information, such as thermal saliency from infrared imagery and fine-grained texture details from visible imagery. However, real-world multi-modal misalignment and geometric deformation often introduce severe artifacts. Most existing
Recent advances in multi-instance learning (MIL) have demonstrated impressive performance in whole slide image (WSI) analysis. However, current methods search for cues and draw conclusions from all instances or regions, resulting in excessive redundant computation and suboptimal representation quali
Rain significantly degrades the performance of computer vision systems, particularly in applications like autonomous driving and video surveillance. While existing deraining methods have made considerable progress, they often struggle with fidelity of semantic and spatial details. To address these l
Cyclic peptides offer inherent advantages in pharmaceuticals. For example, cyclic peptides are more resistant to enzymatic hydrolysis compared to linear peptides and usually exhibit excellent stability and affinity. Although deep generative models have achieved great success in linear peptide design…
Recent advances in selective state space models (Mamba) have shown great promise in whole slide image (WSI) classification. Despite this, WSIs contain explicit local redundancy (similar patches) and irrelevant regions (uninformative instances), posing significant challenges for Mamba-based multi-ins…
The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in d…
Accurate estimation of background error (i.e., forecast error) distribution is critical for effective data assimilation (DA) in numerical weather prediction (NWP). In state-of-the-art operational DA systems, it is common to account for the temporal evolution of background errors by employing hybrid…
Multi-instance learning (MIL) has demonstrated impressive performance in whole slide image (WSI) analysis. However, existing approaches struggle with undesirable results and unbearable computational overhead due to the quadratic complexity of Transformers. Recently, Mamba has offered a feasible solu…
Bag-label-based multi-instance learning (MIL) has demonstrated significant performance in whole slide image (WSI) analysis, particularly in pseudo-label-based learning schemes. However, due to inaccurate feature representation and interference, existing MIL methods often yield unreliable pseudo-labe…
Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited capacity and insufficient representation power, yet remain un…
Data assimilation (DA) is an essential statistical technique for generating accurate estimates of a physical system's states by combining prior model predictions with observational data, especially in the realm of weather forecasting. Effectively modeling the prior distribution while adapting to div…
Data-driven weather forecasting models are advancing rapidly, yet they rely on initial states (i.e., analysis states) typically produced by traditional data assimilation algorithms. Four-dimensional variational assimilation (4DVar) is one of the most widely adopted data assimilation algorithms in nu…
AI's widespread integration has led to neural networks (NN) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompre…
On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a represen…
Deep neural networks boost many successful colorization methods, including automatic, interactive, and exemplar-based methods. Among them, interactive methods with global and/or local inputs are probably the most flexible to accurately add colors to a gray image. However, due to the sparseness of in…
User-guided colorization can predict the colors of a grayscale image according to user inputs, including exemplar images, local inputs and global inputs. Global inputs-based methods are probably the easiest ones to use, but are hard to distribute the input colors into correct regions, due to the lac…
Image colorization recovers a colorful image from a grayscale one. Trained by large-scale datasets, recent deep neural networks based methods can produce impressive colorful images. However, they usually directly train a single network using training images of fixed resolution. It is hard for such a…
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretabi
Sketch recognition using deep neural networks have become a recent trend. However, traditional pixel (image) based convolutional neural networks show poor recognizing performance on low resolution (LR) sketch image due to the loss of image details. To solve this problem, we propose a joint pixel and…
The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image. However, the existing methods often generate contents with blurry textures and distorted structures due to the discontinuity of the local pixels. From a semantic…
Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it's difficult to differentiate…