most citedTask Discrepancy Maximization for Fine-grained Few-Shot Classification

8 citations · 9 across the 3 of their papers we have counts for

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cs.CV20231 cited

VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting

Seunggu Kang, WonJun Moon, Euiyeon Kim +1

Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies prop…

cs.CV20231 cited

Task-Oriented Channel Attention for Fine-Grained Few-Shot Classification

SuBeen Lee, WonJun Moon, Hyun Seok Seong +1

The difficulty of the fine-grained image classification mainly comes from a shared overall appearance across classes. Thus, recognizing discriminative details, such as eyes and bea…

cs.CV20235 cited

Leveraging Hidden Positives for Unsupervised Semantic Segmentation

Hyun Seok Seong, WonJun Moon, SuBeen Lee +1

Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer…

cs.CV20239 cited

Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

WonJun Moon, Sangeek Hyun, SangUk Park +2

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is…

cs.CV2022

Difficulty-Aware Simulator for Open Set Recognition

WonJun Moon, Junho Park, Hyun Seok Seong +2

Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally…

cs.CV20221 cited

Tailoring Self-Supervision for Supervised Learning

WonJun Moon, Ji-Hwan Kim, Jae-Pil Heo

Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not…