8 accepted papers
While vision-language-action (VLA) models for embodied agents integrate perception, reasoning, and control, they remain constrained by two critical weaknesses: first, during grasping tasks, the action tokens generated by the language model often exhibit subtle spatial deviations from the target obje…
Achieving high-quality shadow removal with strong generalizability is challenging in scenes with complex global illumination. Due to the limited diversity in shadow removal datasets, current methods are prone to overfitting training data, often leading to reduced performance on unseen cases. To addr…
As a fundamental visual task, optical flow estimation has widespread applications in computer vision. However, it faces significant challenges under adverse lighting conditions, where low texture and noise make accurate optical flow estimation particularly difficult.In this paper, we propose an opti…
Shadows can originate from occlusions in both direct and indirect illumination. Although most current shadow removal research focuses on shadows caused by direct illumination, shadows from indirect illumination are often just as pervasive, particularly in indoor scenes. A significant challenge in re…
Lane detection methods based on anchors have received increasing attention, but fixed-shape anchors make it difficult to model complex lane line shapes. To solve this problem, we propose an Enhanced Curve Perception Network (ECPNet). Specifically, we propose a Layer-by-layer Context Fusion (LCF) mod…
Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a nove…
Current video saliency prediction methods have made great progress relying on the feature extraction capability of CNN, but there are still many defects in hierarchical feature fusion, limiting the further improvement of accuracy. To address this issue, we propose a 3D convolutional Hierarchical Spa…
Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and RTK GPS) and high-resolution map. However, low-cost production ca…