Selected Publications
Learning Target Candidate Association to Keep Track of What Not to Track
Novel tracking method that tracks both the target and distractor object using a target candidate association network.
ICCV, 2021
Towards Closing the Gap in Weakly Supervised Semantic Segmentation with DCNNs: Combining Local and Global Models
Training strategy to reduced the gap between a network trained on full or on weak annotations.
CVIU, 2021
Adversarial Feature Distribution Alignment for Semi-Supervised Learning
AFDA is a semi-supervised learning method that aligns feature distributions between labeled and unlabeled to boost the classification accuracy.
CVIU, 2021
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression
Novel network compression techique that unifies filter pruning and low-rank decomposition employing group sparsity regularization.
CVPR, 2020
Efficient Video Semantic Segmentation with Labels Propagation and Refinement
Efficient Video Segmentation (EVS) pipeline that combines a fast CPU optical flow method with two CNNs to predict dense semantic labels.
WACV, 2020
Adversarial Sampling for Active Learning
ASAL is a new active learning strategy that synthesizes uncertain samples instead of performing an exhaustive search in each active learning cycle.
WACV, 2020
Origami: A Convolutional Network Accelerator
Convolutional network accelerator which is able to handle GPU-CNN sizes within the power envelope of embedded systems.
GLSVLSI, 2015
