38 citations · 67 across the 8 of their papers we have counts for
8 papers
DetRefiner: Model-Agnostic Detection Refinement with Feature Fusion Transformer
Soichiro Okazaki, Tatsuya Sasaki, Hiroki Ohashi
Open-vocabulary object detection (OVOD) aims to detect both seen and unseen categories, yet existing methods often struggle to generalize to novel objects due to limited integratio…
Visually Similar Pair Alignment for Robust Cross-Domain Object Detection
Onkar Krishna, Hiroki Ohashi
Domain gaps between training data (source) and real-world environments (target) often degrade the performance of object detection models. Most existing methods aim to bridge this g…
Data Cleansing for GANs
Naoyuki Terashita, Hiroki Ohashi, Satoshi Hara
As the application of generative adversarial networks (GANs) expands, it becomes increasingly critical to develop a unified approach that improves performance across various genera…
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
Onkar Krishna, Hiroki Ohashi, Saptarshi Sinha
Cross-domain object detection is challenging, and it involves aligning labeled source and unlabeled target domains. Previous approaches have used adversarial training to align feat…
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
Saptarshi Sinha, Hiroki Ohashi
Long-tailed datasets, where head classes comprise much more training samples than tail classes, cause recognition models to get biased towards the head classes. Weighted loss is on…
Class-Difficulty Based Methods for Long-Tailed Visual Recognition
Saptarshi Sinha, Hiroki Ohashi, Katsuyuki Nakamura
Long-tailed datasets are very frequently encountered in real-world use cases where few classes or categories (known as majority or head classes) have higher number of data samples…
QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information
Masato Tamura, Hiroki Ohashi, Tomoaki Yoshinaga
We propose a simple, intuitive yet powerful method for human-object interaction (HOI) detection. HOIs are so diverse in spatial distribution in an image that existing CNN-based met…
Influence Estimation for Generative Adversarial Networks
Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka +1
Identifying harmful instances, whose absence in a training dataset improves model performance, is important for building better machine learning models. Although previous studies h…