Search Results for author:
Found 13 papers, 5 papers with code
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
CenterFormer: Center-based Transformer for 3D Object Detection
It then uses the feature of the center candidate as the query embedding in the transformer.
Ranked #2 on
3D Object Detection
on waymo cyclist
Grid-GCN for Fast and Scalable Point Cloud Learning
Compared with popular sampling methods such as Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50X speed-up.
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds
We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds.
ES-Net: An Efficient Stereo Matching Network
Dense stereo matching with deep neural networks is of great interest to the research community.
Are Face and Object Recognition Independent? A Neurocomputational Modeling Exploration
Our results show that, as in the behavioral data, the correlation between subordinate level face and object recognition accuracy increases as experience grows.
Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition
Our results suggest that the relative order of importance of using central visual field information is face recognition>object recognition>scene recognition, and vice-versa for peripheral information.
Basic Level Categorization Facilitates Visual Object Recognition
We instantiate this idea by training a deep CNN to perform basic level object categorization first, and then train it on subordinate level categorization.
Suspicious Object Recognition Method in Video Stream Based on Visual Attention
Bottom up and top down attention are applied respectively in the process of acquiring interested object(saliency map) and object recognition.
LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network
This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022.
LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception
LidarMultiNet is extensively tested on both Waymo Open Dataset and nuScenes dataset, demonstrating for the first time that major LiDAR perception tasks can be unified in a single strong network that is trained end-to-end and achieves state-of-the-art performance.
MonoEdge: Monocular 3D Object Detection Using Local Perspectives
We propose a novel approach for monocular 3D object detection by leveraging local perspective effects of each object.
LiDARFormer: A Unified Transformer-based Multi-task Network for LiDAR Perception
The proposed LiDARFormer utilizes cross-space global contextual feature information and exploits cross-task synergy to boost the performance of LiDAR perception tasks across multiple large-scale datasets and benchmarks.
