177 citations · 418 across the 17 of their papers we have counts for
18 papers
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
Siwei Yang, Longlong Jing, Junfei Xiao +3
The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization l…
Class-Level Confidence Based 3D Semi-Supervised Learning
Zhimin Chen, Longlong Jing, Liang Yang +2
Recent state-of-the-art method FlexMatch firstly demonstrated that correctly estimating learning status is crucial for semi-supervised learning (SSL). However, the estimation metho…
R4D: Utilizing Reference Objects for Long-Range Distance Estimation
Yingwei Li, Tiffany Chen, Maya Kabkab +4
Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important lo…
Depth Estimation Matters Most: Improving Per-Object Depth Estimation for Monocular 3D Detection and Tracking
Longlong Jing, Ruichi Yu, Henrik Kretzschmar +11
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception includ…
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Ziyue Feng, Liang Yang, Longlong Jing +3
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the misma…
Learning from Temporal Gradient for Semi-supervised Action Recognition
Junfei Xiao, Longlong Jing, Lin Zhang +5
Semi-supervised video action recognition tends to enable deep neural networks to achieve remarkable performance even with very limited labeled data. However, existing methods are m…
Multimodal Semi-Supervised Learning for 3D Objects
Zhimin Chen, Longlong Jing, Yang Liang +2
In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D task…
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Ziyue Feng, Longlong Jing, Peng Yin +2
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfacto…
Cross-modal Center Loss
Longlong Jing, Elahe Vahdani, Jiaxing Tan +1
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the feature…
Self-supervised Modal and View Invariant Feature Learning
Longlong Jing, Yucheng Chen, Ling Zhang +2
Most of the existing self-supervised feature learning methods for 3D data either learn 3D features from point cloud data or from multi-view images. By exploring the inherent multi-…
Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos
Elahe Vahdani, Longlong Jing, Yingli Tian +1
As part of the development of an educational tool that can help students achieve fluency in American Sign Language (ASL) through independent and interactive practice with immediate…
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Longlong Jing, Yucheng Chen, Ling Zhang +2
The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue,…
VideoSSL: Semi-Supervised Learning for Video Classification
Longlong Jing, Toufiq Parag, Zhe Wu +2
We propose a semi-supervised learning approach for video classification, VideoSSL, using convolutional neural networks (CNN). Like other computer vision tasks, existing supervised…
Recognizing American Sign Language Manual Signs from RGB-D Videos
Longlong Jing, Elahe Vahdani, Matt Huenerfauth +1
In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of…
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Longlong Jing, Yingli Tian
Large-scale labeled data are generally required to train deep neural networks in order to obtain better performance in visual feature learning from images or videos for computer vi…
LGAN: Lung Segmentation in CT Scans Using Generative Adversarial Network
Jiaxing Tan, Longlong Jing, Yumei Huo +2
Lung segmentation in computerized tomography (CT) images is an important procedure in various lung disease diagnosis. Most of the current lung segmentation approaches are performed…
Coarse-to-fine Semantic Segmentation from Image-level Labels
Longlong Jing, Yucheng Chen, Yingli Tian
Deep neural network-based semantic segmentation generally requires large-scale cost extensive annotations for training to obtain better performance. To avoid pixel-wise segmentatio…
Self-Supervised Spatiotemporal Feature Learning via Video Rotation Prediction
Longlong Jing, Xiaodong Yang, Jingen Liu +1
The success of deep neural networks generally requires a vast amount of training data to be labeled, which is expensive and unfeasible in scale, especially for video collections. T…