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- Pierre‐Marc JodoinSuggested from co-authorship
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Works117 from public data
- 880
- Interactive deep learning method for segmenting moving objects334
The first machine learning method for ground truthing a video is proposed, based on a multi-resolution convolutional neural network with a cascaded architecture, for segmenting foreground moving objects pictured in surveillance videos.
- 200
- A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds123
A novel self-training approach named Crowd-SDNet that enables a typical object detector trained only with point-level annotations to estimate both the center points and sizes of crowded objects and proposes a confidence and order-aware refinement scheme to continuously refine the initial pseudo object sizes.
- 113
- Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach99
The proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement on error rate, achieving >99% recognition accuracy on CCPD and AOLP datasets with 149 FPS inference speed and illustrates an outstanding generalization capability when evaluated on the unseen PKUData and CLPD datasets.
- A survey on occupancy perception for autonomous driving: The information fusion perspective84
This survey reviews the most recent works on 3D occupancy perception, and provides in-depth analyses of methodologies with various input modalities, and evaluates and analyze the occupancy perception performance of the state-of-the-art on the most popular datasets.
- 74
- TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd Counting73
A three-stream adaptive fusion network named TAFNet, which uses paired RGB and thermal images for crowd counting, which achieves more than 20% improvement on mean average error and root mean squared error compared with state-of-the-art method.
- A Survey of Embodied Learning for Object-centric Robotic Manipulation63
A comprehensive survey of the latest advancements in embodied learning for object-centric robotic manipulation and categorize the existing work into three main branches: 1) Embodied perceptual learning, which aims to predict object pose and affordance through various data representations.
- 58
- Interactive Change-Aware Transformer Network for Remote Sensing Image Change Captioning50
An Interactive Change-Aware Transformer Network (ICT-Net) is proposed, able to extract and incorporate the most critical changes of interest in each encoder layer to improve change description generation and achieves a state-of-the-art performance.
- Open World Object Detection: A Survey47
This survey paper offers a thorough review of the OWOD domain, covering essential aspects, including problem definitions, benchmark datasets, source codes, evaluation metrics, and a comparative study of existing methods.
- Convolutional Neural Networks With Dynamic Regularization41
This article proposes a dynamic regularization method for CNNs that can dynamically adjust the regularization strength in the training procedure, thereby balancing the underfitting and overfitting of CNNs.
- Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization33
A localization head is proposed to distinguish dense crowds trained by two loss functions, i.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which balances the positive/negative examples and handles the false-positive predictions.
- 32
- Weakly-Supervised Part-Attention and Mentored Networks for Vehicle Re-Identification31
This paper proposes a weakly-supervised Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle Re-ID and adopts Homoscedastic Uncertainty to learn the optimal weighing of ID losses.
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- NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results19
The NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration provides a common benchmark for evaluating restoration methods under realistic corruption settings and provides useful insights for future research on robust video restoration under practical bitstream corruption.
- SGIFormer: Semantic-Guided and Geometric-Enhanced Interleaving Transformer for 3D Instance Segmentation19
This paper introduces a novel method, named SGIFormer, for 3D instance segmentation, which is composed of the Semantic-guided Mix Query (SMQ) initialization and the Geometric-enhanced Interleaving Transformer (GIT) decoder.
- SignEye: Traffic Sign Interpretation From Vehicle First-Person View18
A traffic guidance assistant (TGA) scenario application is developed to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception) and results demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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