130 citations · 344 across the 57 of their papers we have counts for
9 papers · 1 filter
Incremental Scene Synthesis
Benjamin Planche, Xuejian Rong, Ziyan Wu +5
We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior,…
Learning without Memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng +2
Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model. The key problem i…
Re-Identification with Consistent Attentive Siamese Networks
Meng Zheng, Srikrishna Karanam, Ziyan Wu +1
We propose a new deep architecture for person re-identification (re-id). While re-id has seen much recent progress, spatial localization and view-invariant representation learning…
Sharpen Focus: Learning with Attention Separability and Consistency
Lezi Wang, Ziyan Wu, Srikrishna Karanam +4
Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks. Despite good localizatio…
Learning Local RGB-to-CAD Correspondences for Object Pose Estimation
Georgios Georgakis, Srikrishna Karanam, Ziyan Wu +1
We consider the problem of 3D object pose estimation. While much recent work has focused on the RGB domain, the reliance on accurately annotated images limits their generalizabilit…
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition
Benjamin Planche, Sergey Zakharov, Ziyan Wu +3
While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their train…
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
Sergey Zakharov, Benjamin Planche, Ziyan Wu +3
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images. Howe…
Tell Me Where to Look: Guided Attention Inference Network
Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng +2
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps…
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching
Georgios Georgakis, Srikrishna Karanam, Ziyan Wu +2
Finding correspondences between images or 3D scans is at the heart of many computer vision and image retrieval applications and is often enabled by matching local keypoint descript…