Hieu Le

Hieu Le (Minh Hiếu Lê)

Assistant Professor
College of Computing and Informatics (CCI)
University of North Carolina at Charlotte (UNC Charlotte)
Office: Woodward Hall 410D

Email: hle40@charlotte.edu
[Google Scholar] [Github]

📢 I’m recruiting several PhD students

Interested in building controllable & reliable machine vision systems? Some questions we’ll explore together:

🔧 How can we guide models to produce outputs that follow constraints?
🔎 How can we make models easier to interpret and explain?
📊 How can we measure and reduce uncertainty in predictions?

If this fits you, please email your CV (with publication list, transcripts, and SOP) to hle40@charlotte.edu with the subject line [PhDApplicant-YourName].

News

[2025] Iterative meshing of UDFs has been accepted to NeurIPS 2025.

[2025] Two papers have been accepted to ICCV 2025, both are selected for Oral presentation (64 out of 11,239 submissions).

[2025] Pair-wise implicit constraint has been early accepted (top 9%) to MICCAI 2025.

[2025] Qt-DoG 🐶 has been accepted to ICML 2025.

[2025] A paper on personalized scanpath prediction has been accepted to CVPR 2025.

[2025] A paper on identifying reliable seeds for diffusion models has been accepted to ICLR 2025 as a Spotlight paper.

[2024] Two papers accepted to WACV 2025: Material-Consistent Shadow Edges, and Multi-Class Object Counting.

[2024] Pseudo-Density has been accepted to TMLR. Similar to Latent Density Score, this paper reveals the learning mechanism of generative models.

[2024] Four papers accepted to ECCV 2024: Latent Density Score, Neural Surface Localization, Pseudo-Pixel Weighting, and Beyond Pixels.

[2024] A paper on 3D heart generation has been accepted to MICCAI 2024.

[2024] Our paper on uncertainty estimation has been accepted to ICML 2024.

[2024] Zigzag has been accepted by TMLR. Zigzag is a universal uncertainty estimation method that requires only two inference passes.

[2023] Two papers accepted to CVPR 2023: Zero-Shot Object Counting and Few-Shot Object Detection.

[2023] Selected as an outstanding reviewer at ACCV 2022.

[2022] Our paper on Few-Shot Classification via Generating Representative Prototypes has been accepted to CVPR 2022.

[2022] Our journal paper on Physics-Based Shadow Removal has been accepted by TPAMI 2022.

[2022] Our journal paper on detecting penguin colonies from satellite images has been accepted by Remote Sensing 2022.

[2021] Our paper on Few-Shot Classification has been accepted to ICCV 2021.

[2021] Outstanding Reviewer - CVPR 2021.

[2020] Ph.D. completed.

[2020] Outstanding Reviewer - ECCV 2020.

[2020] Our paper on Weakly-Supervised Shadow Removal has been accepted to ECCV 2020. Project Page.

[2019] Our paper on Shadow Removal has been accepted to ICCV 2019. Project Page.

[2019] Our paper on Semi-Supervised Segmentation has been accepted to CVPR 2019 - CV4GC.

[2019] Our paper on Policy Mining Using Neural Networks has been accepted to SACMAT 2019.

[2018] Two papers accepted to ECCV 2018: ADNet for shadow detection and Iterative Crowd Counting.

[2017] Our paper on Object Co-Localization has been accepted to ICCV 2017, Workshop on CEFRL, Venice, Italy.

[2017] Our poster on Object Co-Localization was presented at VSS 2017 (Florida, US). [Poster]

[2016] Geodesic Features for Video Segmentation has been accepted to ACCV 2016.

Publications

	High Resolution UDF Meshing via Iterative Networks Federico Stella, Nicolas Talabot, Hieu Le, Pascal Fua NeurIPS 2025. [Preprint] TL;DR: We use iterative network to mesh UDF at high resolution. At each iteration, it conditions on the prediction from the previous step.
	Importance-based Token Merging for Efficient Image and Video Generation Haoyu Wu, Jingyi Xu, Hieu Le, Dimitris Samaras ICCV 2025. [Preprint] [Project Page] TL;DR: A novel token-merging method speeds up diffusion models by preserving important tokens, enhancing sample quality with minimal computational cost.
	Counting Stacked Objects Corentin Dumery, Noa Ette, Aoxiang Fan, Ren Li, Jingyi Xu, Hieu Le, Pascal Fua ICCV 2025. [Preprint] TL;DR: We do 3D volume reconstruction from multi-view images to count hidden objects
	Gradient Distance Function Hieu Le, Federico Stella, Benoit Guillard, and Pascal Fua ICCV - Wild3D Workshop 2025. [Preprint]
	Pairwise-Constrained Implicit Functions for 3D Human Heart Modeling Hieu Le, Jingyi Xu, Nicolas Talabot, Jiancheng Yang, Pascal Fua. MICCAI 2025. [Preprint] We use monte a carlo sampling method for finding relevant points to refine pairs of SDF surfaces, making them contact each other without penetrating.
	QT-DoG: Quantization-Aware Training for Domain Generalization Saqib Javed, Hieu Le, Mathieu Salzmann ICML 2025. [Preprint] [Project Page] [Code] TL;DR: QT-DoG uses weight quantization as a regularizer to encourage flatter minima, enhancing domain generalization. We also introduce ensembles of quantized models, achieving SoTA performance in DG.
	Few-shot Personalized Scanpath Prediction Ruoyu Xue, Jingyi Xu, Sounak Mondal, Hieu Le, Greg Zelinsky, Minh Hoai, Dimitris Samaras, 2025 CVPR 2025. [Preprint]
	Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds Shuangqi Li, Hieu Le, Jingyi Xu, Mathieu Salzmann, 2025 ICLR 2025. [Preprint] TL;DR: Noises are important for diffusion-based models. We show that some random seeds are much more reliable than others, which can be used to generate useful training data.
	Learning to Count from Pseudo-Labeled Segmentation Jingyi Xu, Hieu Le, Dimitris Samaras WACV 2025. [Preprint] Existing methods suffer from the counting-everything issues. We introduce a benchmark with multiple countable objects in each image and show that we can mitigate this issues by using synthetic data.
	Shadow Removal Refinement via Material-Consistent Shadow Edges Shilin Hu, Hieu Le, ShahRukh Athar, Sagnik Das, Dimitris Samaras WACV 2025. [Preprint][ Project Page] [ Testing Set ] We proposed a new shadow removal method leveraging supervision from the shadow edges. Plus, we introduced a new benchmark for shadow removal with no shadow-free images needed!
	Assessing Sample Quality via the Latent Space of Generative Models Jingyi Xu, Hieu Le, Dimitris Samaras ECCV 2024. [ Preprint] [ Code] [ Project Page] Latent points located in dense regions of the latent manifold tend to produce higher-quality samples, while those in sparser regions yield lower-quality ones.
	Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density Shuangqi Li, Chen Liu, Tong Zhang, Hieu Le, Sabine Susstrunk, Mathieu Salzmann TMLR 2024. [ Openreview]
	Neural Surface Localization for Unsigned Distance Fields Federico Stella, Nicolas Talabot, Hieu Le, Pascal Fua ECCV 2024. [ Preprint] [ Project Page]
	Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation Prantik Howlader, Hieu Le, Dimitris Samaras ECCV 2024. [ Preprint] [ Code]
	Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier Prantik Howlader, Srijan Das, Hieu Le, Dimitris Samaras ECCV 2024. [ Preprint] [ Code]
	Generating Anatomically Accurate Heart Structures via Neural Implicit Fields Jiancheng Yang, Ekaterina Sedykh, Jason Adhinarta, Hieu Le, Pascal Fua MICCAI 2024 [Paper] [Poster]
	Enabling Uncertainty Estimation in Iterative Neural Networks Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua ICML 2024. [ Preprint] [ Code]
	Zigzag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference Nikita Durasov,Nik Dorndorf, Hieu Le, Pascal Fua Transactions on Machine Learning Research (TMLR), 2024. [ Openreview] [ Code]
	Zero-Shot Object Counting Jingyi Xu, Hieu Le, Vu Nguyen, Viresh Ranjan, Dimitris Samaras CVPR 2023. [ Code] [ Stable-Diffusion extension]
	Generating Features With Increased Crop-related Diversity For Few-shot Object Detection Jingyi Xu, Hieu Le, Dimitris Samaras CVPR 2023. [ Code (TBD)]
	Generating Representative Samples for Few-Shot Classification Jingyi Xu, Hieu Le CVPR 2022. [Code] [Paper ] [ Blog post ]
	Physics-based Shadow Image Decomposition for Shadow Removal Hieu Le, Dimitris Samaras. TPAMI 2022. [Github ] [Paper ]
	A convolutional neural network architecture designed for the automated survey of seabird colonies Hieu Le, Dimitris Samaras, Heather J. Lynch Remote Sensing in Ecology and Conservation , 2022. [Paper ]
	Variational Feature Disentangling for Fine-Grained Few-Shot Classification Jingyi Xu, Hieu Le, Mingzhen Huang, ShahRukh Athar, Dimitris Samaras. IEEE International Conference on Computer Vision (ICCV), 2021. [PyTorch Code]
	Aerial-trained deep learning networks for surveying cetaceans from satellite imagery Alex Borowicz, Hieu Le, Grant Humphries, Georg Nehls, Caroline Hoschle, Vladislav Kosarev, Heather J. Lynch Plos One , 2019. [Paper ]
	From Shadow Segmentation to Shadow Removal Hieu Le, Dimitris Samaras. European Conference on Computer Vision (ECCV), 2020. [ Paper Project Page ]
	Shadow Removal via Shadow Image Decomposition Hieu Le, Dimitris Samaras. International Conference on Computer Vision (ICCV), 2019. [Paper Project Page ]
	Weakly Labeling the Antarctic: The Penguin Colony Case Hieu Le, Bento Gonçalves, Dimitris Samaras, Heather Lynch. IEEE Conference on Computer Vision and Pattern Recognition - Workshop (CVPRW), 2019. [Paper ]
	A+D Net: Training a Shadow Detector with Adversarial Shadow Attenuation. Hieu Le, Tomas F. Yago Vicente, Vu Nguyen, Minh Hoai, Dimitris Samaras. European Conference on Computer Vision (ECCV), 2018. [Paper]
	Iterative Crowd Counting. Viresh Ranjan, Hieu Le, and Minh Hoai. European Conference on Computer Vision (ECCV), 2018. [Paper]
	Co-localization with Category-Consistent CNN Features and Geodesic Distance Propagation Hieu Le, Chen-Ping Yu, Gregory Zelinsky, Dimitris Samaras. International Conference on Computer Vision - Workshop (ICCVW), 2017. [Paper]
	Geodesic Distance Histogram Feature for Video Segmentation Hieu Le, Vu Nguyen, Chen-Ping Yu, Dimitris Samaras Asian Conference on Computer Vision (ACCV), 2016. [link] [Paper] [Poster]
	Efficient video segmentation using parametric graph partitioning Chen-Ping Yu, Hieu Le, Gregory Zelinsky, Dimitris Samaras International Conference on Computer Vision (ICCV), 2015. [ paper]

	Learning to Weight Parameters for Data Attribution Shuangqi Li, Hieu Le, Jingyi Xu, Mathieu Salzmann, 2025 [Preprint] We study data attribution in generative models, aiming to identify which training examples most influence a given output.
	Improving Contrastive Learning for Referring Expression Counting Kostas Triaridis, Panagiotis Kaliosis, E-Ro Nguyen, Jingyi Xu, Hieu Le, Dimitris Samaras, 2025 [Preprint]
	Learning Frame-Wise Emotion Intensity for Audio-Driven Talking-Head Generation Jingyi Xu, Hieu Le, Zhixin Shu, Yang Wang, Yi-Hsuan Tsai, Dimitris Samaras, 2025 [Preprint] We introduce a novel method for generating talking-head videos driven by audio input, incorporating frame-wise emotion intensity to enhance realism and expressiveness in visual output. The intensity is pseudo-labeled and we introduce a latent space that facilitates generating videos with varied frame-wise intensities.
	Instance-Aware Generalized Referring Expression Segmentation E-Ro Nguyen, Hieu Le, Dimitris Samaras, Michael Ryoo, 2025 [Preprint] [Project Page] We introduces a novel approach to referring expression segmentation with instance-awareness, automatically finding and linking object instances in the image with the textual entities describing them.
	MedTet: An Online Motion Model for 4D Heart Reconstruction Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le, Pascal Fua, 2025 [Preprint] [Code] [Project Page] TL;DR: This paper introduces a versatile framework for reconstructing 3D cardiac motion from limited real-time data, such as 2D slices or even 1D signals. Using a deformable tetrahedral grid, the method ensures anatomically consistent reconstructions suitable for intraoperative scenarios.