74 citations · 128 across the 7 of their papers we have counts for
8 papers
Quantization-Guided Training for Compact TinyML Models
Sedigh Ghamari, Koray Ozcan, Thu Dinh +4
We propose a Quantization Guided Training (QGT) method to guide DNN training towards optimized low-bit-precision targets and reach extreme compression levels below 8-bit precision.…
Counterfactual Visual Explanations
Yash Goyal, Ziyan Wu, Jan Ernst +3
In this work, we develop a technique to produce counterfactual visual explanations. Given a 'query' image for which a vision system predicts class , a counterfactual visual…
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,…
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…
Learning Compositional Visual Concepts with Mutual Consistency
Yunye Gong, Srikrishna Karanam, Ziyan Wu +3
Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we m…