8 citations · 31 across the 26 of their papers we have counts for
9 papers · 1 filter
Knowledge-aware Evolutionary Graph Neural Architecture Search
Chao Wang, Jiaxuan Zhao, Lingling Li +4
Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin…
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
Michal Nazarczuk, Sibi Catley-Chandar, Thomas Tanay +27
This paper reviews the challenge on Sparse Neural Rendering that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2024. This manuscript…
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
Fan Zhang, Lingling Li, Licheng Jiao +4
Satellite imagery, due to its long-range imaging, brings with it a variety of scale-preferred tasks, such as the detection of tiny/small objects, making the precise localization an…
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
Zihan Gao, Lingling Li, Licheng Jiao +5
Understanding 3D scenes is a crucial challenge in computer vision research with applications spanning multiple domains. Recent advancements in distilling 2D vision-language foundat…
Multiplane Prior Guided Few-Shot Aerial Scene Rendering
Zihan Gao, Licheng Jiao, Lingling Li +4
Neural Radiance Fields (NeRF) have been successfully applied in various aerial scenes, yet they face challenges with sparse views due to limited supervision. The acquisition of den…
Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing
Yi Zuo, Lingling Li, Licheng Jiao +5
Existing diffusion-based methods have achieved impressive results in human motion editing. However, these methods often exhibit significant ghosting and body distortion in unseen i…
Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification
Yanbiao Ma, Licheng Jiao, Fang Liu +3
In semi-supervised learning, methods that rely on confidence learning to generate pseudo-labels have been widely proposed. However, increasing research finds that when faced with n…
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Yanbiao Ma, Licheng Jiao, Fang Liu +5
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is p…
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
Yanbiao Ma, Licheng Jiao, Fang Liu +3
Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the generalization ability of the model. Although numerous approaches of class re-bal…