9 accepted papers
Object detection in drone view images remains challenging due to small-scale objects distributed non-uniformly and exhibiting weak semantic features. Additionally, external factors such as lighting conditions, viewing angles, and background clutter further complicate the detection of small objects.…
Paired image-mask synthesis is essential for automating nanoparticle phenotyping in drug research, as it creates large-scale annotated image datasets for effective learning. However, challenges of complex spatial and morphological variations, such as dense, sparse, and overlapping structures, hinder…
The existing arbitrary style transfer methods mainly suffer two challenges. One is content integrity, as most methods focus too much on style, resulting in incomplete content information and missing details. The other is style consistency, which requires more exploration of style information to alle…
3D open-vocabulary semantic segmentation is a challenge in the task of 3D scene understanding, as most current models trained on closed-set datasets struggle to effectively identify categories that were not seen during training. To address this, we introduce a framework called LSWKD. It distills kno…
Since the diverse geometric appearances and densities of rain streaks, local-global information is equally essential for single image deraining. Balancing local-global information becomes a challenge. Thus, a Multi-Scale Fusion of Gated Neighborhood Attention Transformers (MSF-GNAT) for single image…
Fast and efficient semantic segmentation of large-scale LiDAR point clouds is a fundamental problem in autonomous driving. To achieve this goal, the existing point-based methods mainly choose to adopt Random Sampling strategy to process large-scale point clouds. However, our quantative and qualitati…
In tiny object detection, the main challenges are tiny objects’ weak feature responses and possible semantic disappearance in deep networks. To address the problems, we proposed an Instance-level, Scale-adaptive, Shape-preserved, and Semantic-consistent Supervision (I4S) module for better locating t…
Most existing image enhancement methods require paired images, and rarely consider the aesthetic quality. This paper proposes a bi-directional normalization and color attention-guided generative adversarial network (BNCAGAN) for unsupervised image enhancement. An auxiliary attention classifier (AAC)…
Deep neural networks-based scene text detection has obtained increasing attention in recent years. However, the existing scene text detection methods cannot effectively solve the problem of unclear text features. In this paper, a Multi-scale Residual Orthogonal-Channel Attention Network (MS-ROCANet)…