Search Results for author:
Found 21 papers, 11 papers with code
The 8th AI City Challenge
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.
ParFormer: Vision Transformer Baseline with Parallel Local Global Token Mixer and Convolution Attention Patch Embedding
The ParFormer models outperformed ConvNeXt and Swin Transformer for the pure convolution and transformer model in accuracy.
Addressing Long-Tail Noisy Label Learning Problems: a Two-Stage Solution with Label Refurbishment Considering Label Rarity
In the second stage, our label refurbishment method is applied to obtain soft labels for multi-expert ensemble learning, providing a principled solution to the long-tail noisy label problem.
Scale-Aware Crowd Count Network with Annotation Error Correction
Furthermore, the use of a fixed Gaussian kernel fails to account for the varying pixel distribution with respect to the camera distance.
MixNet: Toward Accurate Detection of Challenging Scene Text in the Wild
Detecting small scene text instances in the wild is particularly challenging, where the influence of irregular positions and nonideal lighting often leads to detection errors.
Ranked #1 on
Scene Text Detection
on SCUT-CTW1500
FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras.
Ranked #1 on
2D Object Detection
on FishEye8K
RATs-NAS: Redirection of Adjacent Trails on GCN for Neural Architecture Search
The RATs-NAS consists of two components: the Redirected Adjacent Trails GCN (RATs-GCN) and the Predictor-based Search Space Sampling (P3S) module.
SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not.
Ranked #3 on
Change Detection
on DSIFN-CD
Building change detection for remote sensing images
Change Detection
+3
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
Second, we develop a Similarity Matching Cascade (SMC) module with a novel GATE function for robust object matching across consecutive video frames, further enhancing MOT performance.
Ranked #1 on
Multi-Object Tracking
on MOT20
(using extra training data)
Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network
We then approximate the joint distribution of crowd density maps with the full covariance of multiple scales and derive a low-rank approximation for tractability and efficient implementation.
NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation
The first architecture search determines the inner cell structure, and the second architecture search considers exponentially growing paths to finalize the outer structure of the network.
Siamese-NAS: Using Trained Samples Efficiently to Find Lightweight Neural Architecture by Prior Knowledge
This idea causes it to surpass the current SOTA predictor on NASBench-201.
Class-Specific Channel Attention for Few-Shot Learning
Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data.
Few-Shot Classification and Segmentation
Few-Shot Image Classification
+3
Cooperative Reinforcement Learning on Traffic Signal Control
Two types of agents running to maximize rewards of different goals - one for local traffic optimization at each intersection and the other for global traffic waiting time optimization.
SFPN: Synthetic FPN for Object Detection
FPN (Feature Pyramid Network) has become a basic component of most SoTA one stage object detectors.
Learnable Discrete Wavelet Pooling (LDW-Pooling) For Convolutional Networks
We introduce the Learning Discrete Wavelet Pooling (LDW-Pooling) that can be applied universally to replace standard pooling operations to better extract features with improved accuracy and efficiency.
MS-DARTS: Mean-Shift Based Differentiable Architecture Search
Our approach can improve bot the stability and accuracy of DARTS, by smoothing the loss landscape and sampling architecture parameters within a suitable bandwidth.
CSL-YOLO: A New Lightweight Object Detection System for Edge Computing
To reduce the computation cost, how to generate redundant features plays a significant role.
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection
Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting.
Ranked #1 on
Object Detection
on UAVDT
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection.
Ranked #673 on
Image Classification
on ImageNet
Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection
A new structure "residual feature pyramid" is proposed in this paper.
