25 citations · 56 across the 8 of their papers we have counts for
8 papers
FairNVT: Fair Classification via Noise Injection in Vision Transformers
Qiaoyue Tang, Sepidehsadat Hosseini, Mengyao Zhai +2
This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. FairNV…
Radar: Fast Long-Context Decoding for Any Transformer
Yongchang Hao, Mengyao Zhai, Hossein Hajimirsadeghi +2
Transformer models have demonstrated exceptional performance across a wide range of applications. Though forming the foundation of Transformer models, the dot-product attention doe…
Prompting-based Temporal Domain Generalization
Sepidehsadat Hosseini, Mengyao Zhai, Hossein Hajimirsadegh +1
Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution…
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising
Mohammad Amin Shabani, Sepidehsadat Hosseini, Yasutaka Furukawa
The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a si…
Floorplan Restoration by Structure Hallucinating Transformer Cascades
Sepidehsadat Hosseini, Yasutaka Furukawa
This paper presents an extreme floorplan reconstruction task, a new benchmark for the task, and a neural architecture as a solution. Given a partial floorplan reconstruction inferr…
House-GAN++: Generative Adversarial Layout Refinement Networks
Nelson Nauata, Sepidehsadat Hosseini, Kai-Hung Chang +3
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relationa…
Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
Sepidehsadat Hosseini, Mohammad Amin Shabani, Nam Ik Cho
We propose a new semi-supervised learning method on face-related tasks based on Multi-Task Learning (MTL) and data distillation. The proposed method exploits multiple datasets with…
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
Sepidehsadat Hosseini, Seok Hee Lee, Nam Ik Cho
Since the convolutional neural network (CNN) is be- lieved to find right features for a given problem, the study of hand-crafted features is somewhat neglected these days. In this…