86 citations · 685 across the 67 of their papers we have counts for
15 papers · 1 filter
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…
Tell Me What They're Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction
Daesik Kim, Gyujeong Lee, Jisoo Jeong +1
In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable kno…
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Jaeyoung Yoo, Hojun Lee, Inseop Chung +2
In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous…
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +3
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…
FEED: Feature-level Ensemble for Knowledge Distillation
SeongUk Park, Nojun Kwak
Knowledge Distillation (KD) aims to transfer knowledge in a teacher-student framework, by providing the predictions of the teacher network to the student network in the training st…
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
Hyojin Park, Lars Lowe Sjösund, YoungJoon Yoo +2
Designing a lightweight and robust portrait segmentation algorithm is an important task for a wide range of face applications. However, the problem has been considered as a subset…