114 citations · 322 across the 11 of their papers we have counts for
11 papers
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Shaoyu Chen, Tianheng Cheng, Jiemin Fang +4
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightwei…
Monocular Road Planar Parallax Estimation
Haobo Yuan, Teng Chen, Wei Sui +4
Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. It is commonly solved either by using 3D sens…
Refiner: Refining Self-attention for Vision Transformers
Daquan Zhou, Yujun Shi, Bingyi Kang +6
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs. Yet, they generally require much more data for model pre-training. Most…
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Xinyu Gong, Wuyang Chen, Yifan Jiang +5
We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…
AFDet: Anchor Free One Stage 3D Object Detection
Runzhou Ge, Zhuangzhuang Ding, Yihan Hu +4
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to so…
FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search
Jiemin Fang, Yuzhu Sun, Qian Zhang +4
Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural n…
FasterSeg: Searching for Faster Real-time Semantic Segmentation
Wuyang Chen, Xinyu Gong, Xianming Liu +3
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods. Utilizing neura…
Fast Neural Network Adaptation via Parameter Remapping and Architecture Search
Jiemin Fang, Yuzhu Sun, Kangjian Peng +4
Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural n…
Context-Aware Dynamic Feature Extraction for 3D Object Detection in Point Clouds
Yonglin Tian, Lichao Huang, Xuesong Li +3
Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by c…
Object Detection in Video with Spatial-temporal Context Aggregation
Hao Luo, Lichao Huang, Han Shen +3
Recent cutting-edge feature aggregation paradigms for video object detection rely on inferring feature correspondence. The feature correspondence estimation problem is fundamentall…
Densely Connected Search Space for More Flexible Neural Architecture Search
Jiemin Fang, Yuzhu Sun, Qian Zhang +3
Neural architecture search (NAS) has dramatically advanced the development of neural network design. We revisit the search space design in most previous NAS methods and find the nu…