1 code implementation • 14 Mar 2024 • Vibashan VS, Shubhankar Borse, Hyojin Park, Debasmit Das, Vishal Patel, Munawar Hayat, Fatih Porikli
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework.
Ranked #1 on Open Vocabulary Panoptic Segmentation on ADE20K
no code implementations • CVPR 2023 • Shubhankar Borse, Debasmit Das, Hyojin Park, Hong Cai, Risheek Garrepalli, Fatih Porikli
Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information.
no code implementations • 24 Feb 2023 • Debasmit Das, Shubhankar Borse, Hyojin Park, Kambiz Azarian, Hong Cai, Risheek Garrepalli, Fatih Porikli
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
no code implementations • 12 Dec 2022 • Kambiz Azarian, Debasmit Das, Hyojin Park, Fatih Porikli
In this approach, we do not assume test-time access to the labeled source dataset.
no code implementations • CVPR 2022 • Shubhankar Borse, Hyojin Park, Hong Cai, Debasmit Das, Risheek Garrepalli, Fatih Porikli
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
no code implementations • CVPR 2022 • Hyojin Park, Alan Yessenbayev, Tushar Singhal, Navin Kumar Adhikari, Yizhe Zhang, Shubhankar Mangesh Borse, Hong Cai, Nilesh Prasad Pandey, Fei Yin, Frank Mayer, Balaji Calidas, Fatih Porikli
Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm.
1 code implementation • CVPR 2021 • Hyojin Park, Jayeon Yoo, Seohyeong Jeong, Ganesh Venkatesh, Nojun Kwak
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame.
1 code implementation • 9 Nov 2020 • Hyojin Park, Ganesh Venkatesh, Nojun Kwak
Our template matching method consists of short-term and long-term matching.
8 code implementations • 20 Nov 2019 • Hyojin Park, Lars Lowe Sjösund, Youngjoon Yoo, Nicolas Monet, Jihwan Bang, Nojun Kwak
To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.
Ranked #1 on Portrait Segmentation on EG1800
3 code implementations • 8 Aug 2019 • Hyojin Park, Lars Lowe Sjösund, Youngjoon Yoo, Jihwan Bang, Nojun Kwak
In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models.
2 code implementations • ICCV 2019 • Byeongho Heo, Jeesoo Kim, Sangdoo Yun, Hyojin Park, Nojun Kwak, Jin Young Choi
We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function.
Ranked #38 on Knowledge Distillation on ImageNet
2 code implementations • 12 Dec 2018 • Hyojin Park, Youngjoon Yoo, Geonseok Seo, Dongyoon Han, Sangdoo Yun, Nojun Kwak
To resolve this problem, we propose a new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution.
2 code implementations • 3 May 2018 • Hyojin Park, YoungJoon Yoo, Nojun Kwak
This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes.
1 code implementation • 30 Jun 2017 • Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
no code implementations • 26 May 2017 • Jisoo Jeong, Hyojin Park, Nojun Kwak
In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.