19 citations · 125 across the 28 of their papers we have counts for
29 papers
ADNet: Lane Shape Prediction via Anchor Decomposition
Lingyu Xiao, Xiang Li, Sen Yang +1
In this paper, we revisit the limitations of anchor-based lane detection methods, which have predominantly focused on fixed anchors that stem from the edges of the image, disregard…
GPA-3D: Geometry-aware Prototype Alignment for Unsupervised Domain Adaptive 3D Object Detection from Point Clouds
Ziyu Li, Jingming Guo, Tongtong Cao +2
LiDAR-based 3D detection has made great progress in recent years. However, the performance of 3D detectors is considerably limited when deployed in unseen environments, owing to th…
ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection
Jifeng Shen, Yifei Chen, Yue Liu +3
Effective feature fusion of multispectral images plays a crucial role in multi-spectral object detection. Previous studies have demonstrated the effectiveness of feature fusion usi…
VDD: Varied Drone Dataset for Semantic Segmentation
Wenxiao Cai, Ke Jin, Jinyan Hou +3
Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Ensuring high accura…
[CLS] Token is All You Need for Zero-Shot Semantic Segmentation
Letian Wu, Wenyao Zhang, Tengping Jiang +3
In this paper, we propose an embarrassingly simple yet highly effective zero-shot semantic segmentation (ZS3) method, based on the pre-trained vision-language model CLIP. First, ou…
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation
Jiren Mai, Fei Zhang, Junjie Ye +5
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable…