1 citations · 2 across the 11 of their papers we have counts for
12 papers
ZeroDSS: Semantic Segmentation from Class Names Alone
Tian Ming, Marzi Heidari, Yuhong Guo
Semantic segmentation depends heavily on real images with pixel-level annotations, making dense supervision a major obstacle to deployment in new domains. We study task-specific da…
Bi-Level Optimization for Single Domain Generalization
Marzi Heidari, Hanping Zhang, Hao Yan +1
Generalizing from a single labeled source domain to unseen target domains, without access to any target data during training, remains a fundamental challenge in robust machine lear…
Learning to Clean: Reinforcement Learning for Noisy Label Correction
Marzi Heidari, Hanping Zhang, Yuhong Guo
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This pape…
Target-Oriented Single Domain Generalization
Marzi Heidari, Yuhong Guo
Deep models trained on a single source domain often fail catastrophically under distribution shifts, a critical challenge in Single Domain Generalization (SDG). While existing meth…
Single Domain Generalization with Adversarial Memory
Hao Yan, Marzi Heidari, Yuhong Guo
Domain Generalization (DG) aims to train models that can generalize to unseen testing domains by leveraging data from multiple training domains. However, traditional DG methods rel…
A Unified Framework for Heterogeneous Semi-supervised Learning
Marzi Heidari, Abdullah Alchihabi, Hao Yan +1
In this work, we introduce a novel problem setup termed as Heterogeneous Semi-Supervised Learning (HSSL), which presents unique challenges by bridging the semi-supervised learning…
Single Domain Generalization with Model-aware Parametric Batch-wise Mixup
Marzi Heidari, Yuhong Guo
Single Domain Generalization (SDG) remains a formidable challenge in the field of machine learning, particularly when models are deployed in environments that differ significantly…
Reinforcement Learning-Guided Semi-Supervised Learning
Marzi Heidari, Hanping Zhang, Yuhong Guo
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especi…
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
Marzi Heidari, Hanping Zhang, Yuhong Guo
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL…
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the targe…
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Marzi Heidari, Abdullah Alchihabi, Qing En +1
Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test task…
Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts
Marzieh Heidari, Mehdi Ghatee, Ahmad Nickabadi +1
With great advances in vision and natural language processing, the generation of image captions becomes a need. In a recent paper, Mathews, Xie and He [1], extended a new model to…