8 citations · 23 across the 13 of their papers we have counts for
14 papers · 1 filter
3DPillars: Pillar-based two-stage 3D object detection
Jongyoun Noh, Junghyup Lee, Hyekang Park +1
PointPillars is the fastest 3D object detector that exploits pseudo image representations to encode features for 3D objects in a scene. Albeit efficient, PointPillars is typically…
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Jeimin Jeon, Youngmin Oh, Junghyup Lee +4
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training stra…
Toward INT4 Fixed-Point Training via Exploring Quantization Error for Gradients
Dohyung Kim, Junghyup Lee, Jeimin Jeon +2
Network quantization generally converts full-precision weights and/or activations into low-bit fixed-point values in order to accelerate an inference process. Recent approaches to…
Scheduling Weight Transitions for Quantization-Aware Training
Junghyup Lee, Jeimin Jeon, Dohyung Kim +1
Quantization-aware training (QAT) simulates a quantization process during training to lower bit-precision of weights/activations. It learns quantized weights indirectly by updating…
AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search
Junghyup Lee, Bumsub Ham
Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies, capturing network characteristics related to the final performance…
RankMixup: Ranking-Based Mixup Training for Network Calibration
Jongyoun Noh, Hyekang Park, Junghyup Lee +1
Network calibration aims to accurately estimate the level of confidences, which is particularly important for employing deep neural networks in real-world systems. Recent approache…
Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation
Donghyeon Baek, Youngmin Oh, Sanghoon Lee +2
Class-incremental semantic segmentation (CISS) labels each pixel of an image with a corresponding object/stuff class continually. To this end, it is crucial to learn novel classes…
OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search
Sanghoon Lee, Youngmin Oh, Donghyeon Baek +2
We address the task of person search, that is, localizing and re-identifying query persons from a set of raw scene images. Recent approaches are typically built upon OIMNet, a pion…
Video-based Person Re-identification with Spatial and Temporal Memory Networks
Chanho Eom, Geon Lee, Junghyup Lee +1
Video-based person re-identification (reID) aims to retrieve person videos with the same identity as a query person across multiple cameras. Spatial and temporal distractors in per…
Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences
Hyunjong Park, Sanghoon Lee, Junghyup Lee +1
We address the problem of visible-infrared person re-identification (VI-reID), that is, retrieving a set of person images, captured by visible or infrared cameras, in a cross-modal…
Distance-aware Quantization
Dohyung kim, Junghyup Lee, Bumsub Ham
We address the problem of network quantization, that is, reducing bit-widths of weights and/or activations to lighten network architectures. Quantization methods use a rounding fun…
Network Quantization with Element-wise Gradient Scaling
Junghyup Lee, Dohyung Kim, Bumsub Ham
Network quantization aims at reducing bit-widths of weights and/or activations, particularly important for implementing deep neural networks with limited hardware resources. Most m…
Learning with Privileged Information for Efficient Image Super-Resolution
Wonkyung Lee, Junghyup Lee, Dohyung Kim +1
Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on ac…
Learning Semantic Correspondence Exploiting an Object-level Prior
Junghyup Lee, Dohyung Kim, Wonkyung Lee +2
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We pr…