Author name cluster

Wael Abd-Almageed

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

ICML Conference 2023 Conference Paper

A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment

Hanchen Xie
Jiageng Zhu
Mahyar Khayatkhoei
Jiazhi Li 0001
Mohamed E. Hussein 0001
Wael Abd-Almageed

Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning physics. To solve this problem, Region Proposal Convolutional Interaction Network (RPCIN), a vision-based model, was proposed and achieved state-of-the-art performance in long-term prediction. RPCIN only takes raw images and simple object descriptions, such as the bounding box and segmentation mask of each object, as input. However, despite its success, the model’s capability can be compromised under conditions of environment misalignment. In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split. The datasets cover two domains and two contexts. Using RPCIN as a probe, experiments conducted on the combinations of the proposed datasets reveal potential weaknesses of the vision-based long-term dynamics prediction model. Furthermore, we propose a promising direction to mitigate the Cross-Domain challenge and provide concrete evidence supporting such a direction, which provides dramatic alleviation of the challenge on the proposed datasets.

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ICML Conference 2023 Conference Paper

Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions

Mahyar Khayatkhoei
Wael Abd-Almageed

Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models. Given their central role in comparing and improving generative models, understanding their limitations are crucially important. To that end, in this work, we identify a critical flaw in the common approximation of these metrics using k-nearest-neighbors, namely, that the very interpretations of fidelity and diversity that are assigned to Precision and Recall can fail in high dimensions, resulting in very misleading conclusions. Specifically, we empirically and theoretically show that as the number of dimensions grows, two model distributions with supports at equal point-wise distance from the support of the real distribution, can have vastly different Precision and Recall regardless of their respective distributions, hence an emergent asymmetry in high dimensions. Based on our theoretical insights, we then provide simple yet effective modifications to these metrics to construct symmetric metrics regardless of the number of dimensions. Finally, we provide experiments on real-world datasets to illustrate that the identified flaw is not merely a pathological case, and that our proposed metrics are effective in alleviating its impact.

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NeurIPS Conference 2018 Conference Paper

Unsupervised Adversarial Invariance

Ayush Jaiswal
Rex Yue Wu
Wael Abd-Almageed
Prem Natarajan

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting. We present a novel unsupervised invariance induction framework for neural networks that learns a split representation of data through competitive training between the prediction task and a reconstruction task coupled with disentanglement, without needing any labeled information about nuisance factors or domain knowledge. We describe an adversarial instantiation of this framework and provide analysis of its working. Our unsupervised model outperforms state-of-the-art methods, which are supervised, at inducing invariance to inherent nuisance factors, effectively using synthetic data augmentation to learn invariance, and domain adaptation. Our method can be applied to any prediction task, eg. , binary/multi-class classification or regression, without loss of generality.

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ICRA Conference 2008 Conference Paper

Human detection using iterative feature selection and logistic principal component analysis

Wael Abd-Almageed
Larry S. Davis

We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of interest at different locations and scales. The algorithm iteratively estimates the belongness probability of image pixels to foreground of the image. To prove the validity of the algorithm, we apply it to a human detection problem. The edge map is filtered using a feature selection algorithm. The filtered edge map is then projected onto an eigen space of human shapes to determine if the image contains a human. Since the edge maps are binary in nature, Logistic Principal Component Analysis is used to obtain the eigen human shape space. Experimental results illustrate the accuracy of the human detector.

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IROS Conference 2006 Conference Paper

Tracking Articulating Objects from Ground Vehicles using Mixtures of Mixtures

Wael Abd-Almageed
Mohamed E. Hussein 0001
Larry S. Davis

An algorithm for tracking articulating objects from moving camera platforms is presented. Mixtures of mixtures are used to model the appearance of the object and the background. The state of the object is tracked using a particle filter. Egomotion information are estimated and used to set the state variance of the particle filter. Results of tracking human objects from an unmanned ground vehicle are used to evaluate the tracking algorithm

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IROS Conference 2002 Conference Paper

Contour migration: solving object ambiguity with shape-space visual guidance

Wael Abd-Almageed
Christopher E. Smith

A fundamental problem in computer vision is the issue of shape ambiguity. Simply stated, a silhouette cannot uniquely identify an object or an object's classification since many unique objects can present identical occluding contours. This problem has no solution in the general case for a monocular vision system. This paper presents a method for disambiguating objects during silhouette matching using a visual servoing system. This method identifies the camera motion(s) that gives disambiguating views of the objects. These motions are identified through a new technique called contour migration. The occluding contour's shape is used to identify objects or object classes that are potential matches for that shape. A contour migration is then determined that disambiguates the possible matches by purposive viewpoint adjustment. The technique is demonstrated using an example set of objects.

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