200 citations · 397 across the 11 of their papers we have counts for
11 papers
Learning Pseudo-Labeler beyond Noun Concepts for Open-Vocabulary Object Detection
Sunghun Kang, Junbum Cha, Jonghwan Mun +2
Open-vocabulary object detection (OVOD) has recently gained significant attention as a crucial step toward achieving human-like visual intelligence. Existing OVOD methods extend ta…
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…
Open-Vocabulary Object Detection using Pseudo Caption Labels
Han-Cheol Cho, Won Young Jhoo, Wooyoung Kang +1
Recent open-vocabulary detection methods aim to detect novel objects by distilling knowledge from vision-language models (VLMs) trained on a vast amount of image-text pairs. To imp…
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Dohwan Ko, Joonmyung Choi, Hyeong Kyu Choi +3
Foundation models have shown outstanding performance and generalization capabilities across domains. Since most studies on foundation models mainly focus on the pretraining phase,…
Noise-aware Learning from Web-crawled Image-Text Data for Image Captioning
Wooyoung Kang, Jonghwan Mun, Sungjun Lee +1
Image captioning is one of the straightforward tasks that can take advantage of large-scale web-crawled data which provides rich knowledge about the visual world for a captioning m…
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs
Junbum Cha, Jonghwan Mun, Byungseok Roh
We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existin…
Sparse DETR: Efficient End-to-End Object Detection with Learnable Sparsity
Byungseok Roh, JaeWoong Shin, Wuhyun Shin +1
DETR is the first end-to-end object detector using a transformer encoder-decoder architecture and demonstrates competitive performance but low computational efficiency on high reso…
Spatially Consistent Representation Learning
Byungseok Roh, Wuhyun Shin, Ildoo Kim +1
Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive p…
BABO: Background Activation Black-Out for Efficient Object Detection
Byungseok Roh, Han-Cheol Cho, Myung-Ho Ju +1
Recent advances in deep learning have enabled complex real-world use cases comprised of multiple vision tasks and detection tasks are being shifted to the edge side as a pre-proces…
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Sanghoon Hong, Byungseok Roh, Kye-Hyeon Kim +2
In object detection, reducing computational cost is as important as improving accuracy for most practical usages. This paper proposes a novel network structure, which is an order o…
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh +2
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recen…