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Alexander Wong

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6 papers

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JBHI Journal 2026 Journal Article

Physically Informed 3D Food Reconstruction: Methods and Results

Jiangpeng He
Yuhao Chen
Gautham Vinod
Xiaoyan Zhang
Talha Ibn Mahmud
Ahmad AlMughrabi
Umair Haroon
Ricardo Marques

Accurate food portion size estimation is a critical challenge in nutrition analysis and dietary assessment. Recent 3D reconstruction methods primarily focus on surface geometry, often neglecting the volumetric accuracy necessary for precise portion size estimation. In this work, we present three methods, NS-DRS, HR-CMS, and GS-GP, for real-scale 3D food reconstruction and volume estimation from limited 2D inputs. All three methods follow a shared three-stage pipeline consisting of 3D reconstruction, scale estimation using physical references such as visible checkerboards, and mesh refinement. While this high-level structure is consistent, the methods differ in their reconstruction backbones and scale-recovery mechanisms, and are designed for different input conditions, including both single-view and multi-view images. We evaluate each method on a diverse set of food items exhibiting variations in texture, shape, and camera pose, and assess their performance in terms of volume estimation and geometric accuracy. The results reveal complementary strengths among the pipelines, with NS-DRS performing better in volume estimation and GS-GP being more effective in 3D reconstruction. Across all settings, the three methods achieve 18–23% lower volume estimation error compared to the current state-of-the-art model. These findings demonstrate the effectiveness of physically informed and explainable reconstruction pipelines for accurate portion estimation, and support their potential use as scalable tools for dietary monitoring and clinical nutrition analysis.

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NeurIPS Conference 2025 Conference Paper

3D Human Pose Estimation with Muscles

Kevin Zhu
AliAsghar MohammadiNasrabadi
Alexander Wong
John McPhee

We introduce MusclePose as an end-to-end learnable physics-infused 3D human pose estimator that incorporates muscle-dynamics modeling to infer human dynamics from monocular video. Current physics pose estimators aim to predict physically plausible poses by enforcing the underlying dynamics equations that govern motion. Since this is an underconstrained problem without force-annotated data, methods often estimate kinetics with external physics optimizers that may not be compatible with existing learning frameworks, or are too slow for real-time inference. While more recent methods use a regression-based approach to overcome these issues, the estimated kinetics can be seen as auxiliary predictions, and may not be physically plausible. To this end, we build on existing regression-based approaches, and aim to improve the biofidelity of kinetic inference with a multihypothesis approach --- by inferring joint torques via Lagrange’s equations and via muscle dynamics modeling with muscle torque generators. Furthermore, MusclePose predicts detailed human anthropometrics based on values from biomechanics studies, in contrast to existing physics pose estimators that construct their human models with shape primitives. We show that MusclePose is competitive with existing 3D pose estimators in positional accuracy, while also able to infer plausible human kinetics and muscle signals consistent with values from biomechanics studies, without requiring an external physics engine.

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TMLR Journal 2025 Journal Article

Neuron-based explanations of neural networks sacrifice completeness and interpretability

Nolan Simran Dey
Eric Taylor
Alexander Wong
Bryan P. Tripp
Graham W. Taylor

High quality explanations of neural networks (NNs) should exhibit two key properties. Completeness ensures that they accurately reflect a network’s function and interpretability makes them understandable to humans. Many existing methods provide explanations of individual neurons within a network. In this work we provide evidence that for AlexNet pretrained on ImageNet, neuron-based explanation methods sacrifice both completeness and interpretability compared to activation principal components. Neurons are a poor basis for AlexNet embeddings because they don’t account for the distributed nature of these representations. By examining two quantitative measures of completeness and conducting a user study to measure interpretability, we show the most important principal components provide more complete and interpretable explanations than the most important neurons. Much of the activation variance may be explained by examining relatively few high-variance PCs, as opposed to studying every neuron. These principal components also strongly affect network function, and are significantly more interpretable than neurons. Our findings suggest that explanation methods for networks like AlexNet should avoid using neurons as a basis for embeddings and instead choose a basis, such as principal components, which accounts for the high dimensional and distributed nature of a network's internal representations. Interactive demo and code available at https://ndey96.github.io/neuron-explanations-sacrifice.

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JBHI Journal 2022 Journal Article

VidAF: A Motion-Robust Model for Atrial Fibrillation Screening From Facial Videos

Xuenan Liu
Xuezhi Yang
Dingliang Wang
Alexander Wong
Likun Ma
Longwei Li

Atrial fibrillation (AF) is the most common arrhythmia, but an estimated 30% of patients with AF are unaware of their conditions. The purpose of this work is to design a model for AF screening from facial videos, with a focus on addressing typical motion disturbances in our real life, such as head movements and expression changes. This model detects a pulse signal from the skin color changes in a facial video by a convolution neural network, incorporating a phase-driven attention mechanism to suppress motion signals in the space domain. It then encodes the pulse signal into discriminative features for AF classification by a coding neural network, using a de-noise coding strategy to improve the robustness of the features to motion signals in the time domain. The proposed model was tested on a dataset containing 1200 samples of 100 AF patients and 100 non-AF subjects. Experimental results demonstrated that VidAF had significant robustness to facial motions, predicting clean pulse signals with the mean absolute error of inter-pulse intervals less than 100 milliseconds. Besides, the model achieved promising performance in AF identification, showing an accuracy of more than 90% in multiple challenging scenarios. VidAF provides a more convenient and cost-effective approach for opportunistic AF screening in the community.

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AAAI Conference 2017 Conference Paper

Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption

Tameem Adel
Han Zhao
Alexander Wong

Domain adaptation addresses learning tasks where training is performed on data from one domain whereas testing is performed on data belonging to a different but related domain. Assumptions about the relationship between the source and target domains should lead to tractable solutions on the one hand, and be realistic on the other hand. Here we propose a generative domain adaptation model that allows for modelling different assumptions about this relationship, among which is a newly introduced assumption that replaces covariate shift with a possibly more realistic assumption without losing tractability due to the efﬁcient variational inference procedure developed. In addition to the ability to model less restrictive relationships between source and target, modelling can be performed without any target labeled data (unsupervised domain adaptation). We also provide a Rademacher complexity bound of the proposed algorithm. We evaluate the model on the Amazon reviews and the CVC pedestrian detection datasets.

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AAAI Conference 2015 Conference Paper

A Probabilistic Covariate Shift Assumption for Domain Adaptation

Tameem Adel
Alexander Wong

The aim of domain adaptation algorithms is to establish a learner, trained on labeled data from a source domain, that can classify samples from a target domain, in which few or no labeled data are available for training. Covariate shift, a primary assumption in several works on domain adaptation, assumes that the labeling functions of source and target domains are identical. We present a domain adaptation algorithm that assumes a relaxed version of covariate shift where the assumption that the labeling functions of the source and target domains are identical holds with a certain probability. Assuming a source deterministic large margin binary classifier, the farther a target instance is from the source decision boundary, the higher the probability that covariate shift holds. In this context, given a target unlabeled sample and no target labeled data, we develop a domain adaptation algorithm that bases its labeling decisions both on the source learner and on the similarities between the target unlabeled instances. The source labeling function decisions associated with probabilistic covariate shift, along with the target similarities are concurrently expressed on a similarity graph. We evaluate our proposed algorithm on a benchmark sentiment analysis (and domain adaptation) dataset, where state-of-the-art adaptation results are achieved. We also derive a lower bound on the performance of the algorithm.

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