47 citations · 199 across the 17 of their papers we have counts for
21 papers
Breaking the "Object" in Video Object Segmentation
Pavel Tokmakov, Jie Li, Adrien Gaidon
The appearance of an object can be fleeting when it transforms. As eggs are broken or paper is torn, their color, shape and texture can change dramatically, preserving virtually no…
Object Permanence Emerges in a Random Walk along Memory
Pavel Tokmakov, Allan Jabri, Jie Li +1
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence. A central question is th…
Fully Attentional Network for Semantic Segmentation
Qi Song, Jie Li, Chenghong Li +2
Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation. These methods usually form a similarity map of R…
Denoised Non-Local Neural Network for Semantic Segmentation
Qi Song, Jie Li, Hao Guo +1
The non-local network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most o…
LocTex: Learning Data-Efficient Visual Representations from Localized Textual Supervision
Zhijian Liu, Simon Stent, Jie Li +2
Computer vision tasks such as object detection and semantic/instance segmentation rely on the painstaking annotation of large training datasets. In this paper, we propose LocTex th…
Is Pseudo-Lidar needed for Monocular 3D Object detection?
Dennis Park, Rares Ambrus, Vitor Guizilini +2
Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors. These…
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation
Tommi Kerola, Jie Li, Atsushi Kanehira +3
Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simul…
Geometric Unsupervised Domain Adaptation for Semantic Segmentation
Vitor Guizilini, Jie Li, Rares Ambrus +1
Simulators can efficiently generate large amounts of labeled synthetic data with perfect supervision for hard-to-label tasks like semantic segmentation. However, they introduce a d…
Learning to Track with Object Permanence
Pavel Tokmakov, Jie Li, Wolfram Burgard +1
Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and association steps. As a result, it strongly depends on the qualit…
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Vitor Guizilini, Rui Hou, Jie Li +2
Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning re…
Probabilistic 3D Multi-Object Tracking for Autonomous Driving
Hsu-kuang Chiu, Antonio Prioletti, Jie Li +1
3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we p…
Real-Time Panoptic Segmentation from Dense Detections
Rui Hou, Jie Li, Arjun Bhargava +5
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot…
HighEr-Resolution Network for Image Demosaicing and Enhancing
Kangfu Mei, Juncheng Li, Jiajie Zhang +3
Neural-networks based image restoration methods tend to use low-resolution image patches for training. Although higher-resolution image patches can provide more global information,…
Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances
Vitor Guizilini, Jie Li, Rares Ambrus +2
Dense depth estimation from a single image is a key problem in computer vision, with exciting applications in a multitude of robotic tasks. Initially viewed as a direct regression…
Two Stream Networks for Self-Supervised Ego-Motion Estimation
Rares Ambrus, Vitor Guizilini, Jie Li +2
Learning depth and camera ego-motion from raw unlabeled RGB video streams is seeing exciting progress through self-supervision from strong geometric cues. To leverage not only appe…
Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
Yalong Liu, Jie Li, Miaomiao Wang +3
Accurate segmentation of punctate white matter lesions (PWML) in preterm neonates by an automatic algorithm can better assist doctors in diagnosis. However, the existing algorithms…
PosNeg-Balanced Anchors with Aligned Features for Single-Shot Object Detection
Qiankun Tang, Shice Liu, Jie Li +1
We introduce a novel single-shot object detector to ease the imbalance of foreground-background class by suppressing the easy negatives while increasing the positives. To achieve t…
Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants
Yalong Liu, Jie Li, Ying Wang +5
Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PW…
SPIGAN: Privileged Adversarial Learning from Simulation
Kuan-Hui Lee, German Ros, Jie Li +1
Deep Learning for Computer Vision depends mainly on the source of supervision.Photo-realistic simulators can generate large-scale automatically labeled syntheticdata, but introduce…
Gaussian Processes Semantic Map Representation
Maani Ghaffari Jadidi, Lu Gan, Steven A. Parkison +2
In this paper, we develop a high-dimensional map building technique that incorporates raw pixelated semantic measurements into the map representation. The proposed technique uses G…
A survey of dimensionality reduction techniques based on random projection
Haozhe Xie, Jie Li, Hanqing Xue
Dimensionality reduction techniques play important roles in the analysis of big data. Traditional dimensionality reduction approaches, such as principal component analysis (PCA) an…