742 citations · 2.5k across the 92 of their papers we have counts for
14 papers · 1 filter
Semi-Supervised Few-Shot Atomic Action Recognition
Xiaoyuan Ni, Sizhe Song, Yu-Wing Tai +1
Despite excellent progress has been made, the performance on action recognition still heavily relies on specific datasets, which are difficult to extend new action classes due to l…
HAA500: Human-Centric Atomic Action Dataset with Curated Videos
Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang +2
We contribute HAA500, a manually annotated human-centric atomic action dataset for action recognition on 500 classes with over 591K labeled frames. To minimize ambiguities in actio…
Pose-Guided High-Resolution Appearance Transfer via Progressive Training
Ji Liu, Heshan Liu, Mang-Tik Chiu +2
We propose a novel pose-guided appearance transfer network for transferring a given reference appearance to a target pose in unprecedented image resolution (1024 * 1024), given res…
GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision
Lei Ke, Shichao Li, Yanan Sun +2
We present a novel end-to-end framework named as GSNet (Geometric and Scene-aware Network), which jointly estimates 6DoF poses and reconstructs detailed 3D car shapes from single u…
Fully Convolutional Networks for Continuous Sign Language Recognition
Ka Leong Cheng, Zhaoyang Yang, Qifeng Chen +1
Continuous sign language recognition (SLR) is a challenging task that requires learning on both spatial and temporal dimensions of signing frame sequences. Most recent work accompl…
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
Qi Fan, Lei Ke, Wenjie Pei +2
Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The lear…
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
Jianfeng Yan, Zizhuang Wei, Hongwei Yi +5
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely HC-RMVSNet, for accurate dense po…
Dive Deeper Into Box for Object Detection
Ran Chen, Yong Liu, Mengdan Zhang +3
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.…
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Xuhua Huang, Jiarui Xu, Yu-Wing Tai +1
Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image…
Cascaded deep monocular 3D human pose estimation with evolutionary training data
Shichao Li, Lei Ke, Kevin Pratama +3
End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed t…
One-Shot Object Detection without Fine-Tuning
Xiang Li, Lin Zhang, Yau Pun Chen +2
Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich s…
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
Ho Kei Cheng, Jihoon Chung, Yu-Wing Tai +1
State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolutio…
Learning Video Object Segmentation from Unlabeled Videos
Xiankai Lu, Wenguan Wang, Jianbing Shen +3
We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensi…
Spatial-Scale Aligned Network for Fine-Grained Recognition
Lizhao Gao, Haihua Xu, Chong Sun +2
Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inf…