Author name cluster

Sima Behpour

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8 papers

2 author rows

AAAI Conference 2024 Conference Paper

Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection

Thang Doan
Xin Li
Sima Behpour
Wenbin He
Liang Gou
Liu Ren

Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown objects while integrating learned knowledge for future tasks. However, the level of "unknownness" varies significantly depending on the context. For example, a tree is typically considered part of the background in a self-driving scene, but it may be significant in a household context. We argue that this contextual information should already be embedded within the known classes. In other words, there should be a semantic or latent structure relationship between the known and unknown items to be discovered. Motivated by this observation, we propose Hyp-OW, a method that learns and models hierarchical representation of known items through a SuperClass Regularizer. Leveraging this representation allows us to effectively detect unknown objects using a similarity distance-based relabeling module. Extensive experiments on benchmark datasets demonstrate the effectiveness of Hyp-OW, achieving improvement in both known and unknown detection (up to 6 percent). These findings are particularly pronounced in our newly designed benchmark, where a strong hierarchical structure exists between known and unknown objects.

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NeurIPS Conference 2023 Conference Paper

GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients

Sima Behpour
Thang Long Doan
Xin Li
Wenbin He
Liang Gou
Liu Ren

Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approaches primarily rely on the feature maps or the full gradient space information to derive OOD scores neglecting the role of \textbf{most important parameters} of the pre-trained network over In-Distribution data. In this study, we propose a novel approach called GradOrth to facilitate OOD detection based on one intriguing observation that the important features to identify OOD data lie in the lower-rank subspace of in-distribution (ID) data. In particular, we identify OOD data by computing the norm of gradient projection on \textit{the subspaces considered \textbf{important} for the in-distribution data}. A large orthogonal projection value (i. e. a small projection value) indicates the sample as OOD as it captures a weak correlation of the in-distribution (ID) data. This simple yet effective method exhibits outstanding performance, showcasing a notable reduction in the average false positive rate at a 95\% true positive rate (FPR95) of up to 8\% when compared to the current state-of-the-art methods.

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NeurIPS Conference 2023 Conference Paper

UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models

Xin Li
Sima Behpour
Thang Long Doan
Wenbin He
Liang Gou
Liu Ren

In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing performance for undefined downstream tasks with a limited annotation budget. Previous approaches to data pre-selection relied solely on visual features extracted from foundation models, such as CLIP and BLIP-2, but largely ignored the powerfulness of text features. In this work, we argue that, with proper design, the joint feature space of both vision and text can yield a better representation for data pre-selection. To this end, we introduce UP-DP, a simple yet effective unsupervised prompt learning approach that adapts vision-language models, like BLIP-2, for data pre-selection. Specifically, with the BLIP-2 parameters frozen, we train text prompts to extract the joint features with improved representation, ensuring a diverse cluster structure that covers the entire dataset. We extensively compare our method with the state-of-the-art using seven benchmark datasets in different settings, achieving up to a performance gain of 20\%. Interestingly, the prompts learned from one dataset demonstrate significant generalizability and can be applied directly to enhance the feature extraction of BLIP-2 from other datasets. To the best of our knowledge, UP-DP is the first work to incorporate unsupervised prompt learning in a vision-language model for data pre-selection.

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ICML Conference 2021 Conference Paper

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer

Seungwon Lee
Sima Behpour
Eric Eaton

Effective lifelong learning across diverse tasks requires the transfer of diverse knowledge, yet transferring irrelevant knowledge may lead to interference and catastrophic forgetting. In deep networks, transferring the appropriate granularity of knowledge is as important as the transfer mechanism, and must be driven by the relationships among tasks. We first show that the lifelong learning performance of several current deep learning architectures can be significantly improved by transfer at the appropriate layers. We then develop an expectation-maximization (EM) method to automatically select the appropriate transfer configuration and optimize the task network weights. This EM-based selective transfer is highly effective, balancing transfer performance on all tasks with avoiding catastrophic forgetting, as demonstrated on three algorithms in several lifelong object classification scenarios.

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ICML Conference 2019 Conference Paper

Active Learning for Probabilistic Structured Prediction of Cuts and Matchings

Sima Behpour
Anqi Liu 0001
Brian D. Ziebart

Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector ma-chines can be tractably applied to predicting cuts and bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for cuts and matching. We evaluate this approach algorithmically in two important structured prediction problems: multi-label classification and object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.

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AAAI Conference 2018 Conference Paper

ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-Label Classification

Sima Behpour
Wei Xing
Brian Ziebart

Many structured prediction tasks arising in computer vision and natural language processing tractably reduce to making minimum cost cuts in graphs with edge weights learned using maximum margin methods. Unfortunately, the hinge loss used to construct these methods often provides a particularly loose bound on the loss function of interest (e. g. , the Hamming loss). We develop Adversarial Robust Cuts (ARC), an approach that poses the learning task as a minimax game between predictor and “label approximator” based on minimum cost graph cuts. Unlike maximum margin methods, this game-theoretic perspective always provides meaningful bounds on the Hamming loss. We conduct multi-label and semi-supervised binary prediction experiments that demonstrate the beneﬁts of our approach.

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ICML Conference 2018 Conference Paper

Efficient and Consistent Adversarial Bipartite Matching

Rizal Fathony
Sima Behpour
Xinhua Zhang
Brian D. Ziebart

Many important structured prediction problems, including learning to rank items, correspondence-based natural language processing, and multi-object tracking, can be formulated as weighted bipartite matching optimizations. Existing structured prediction approaches have significant drawbacks when applied under the constraints of perfect bipartite matchings. Exponential family probabilistic models, such as the conditional random field (CRF), provide statistical consistency guarantees, but suffer computationally from the need to compute the normalization term of its distribution over matchings, which is a #P-hard matrix permanent computation. In contrast, the structured support vector machine (SSVM) provides computational efficiency, but lacks Fisher consistency, meaning that there are distributions of data for which it cannot learn the optimal matching even under ideal learning conditions (i. e. , given the true distribution and selecting from all measurable potential functions). We propose adversarial bipartite matching to avoid both of these limitations. We develop this approach algorithmically, establish its computational efficiency and Fisher consistency properties, and apply it to matching problems that demonstrate its empirical benefits.

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UAI Conference 2015 Conference Paper

Adversarial Cost-Sensitive Classification

Kaiser Asif
Wei Xing
Sima Behpour
Brian D. Ziebart

In many classification settings, mistakes incur different application-dependent penalties based on the predicted and actual class labels. Costsensitive classifiers minimizing these penalties are needed. We propose a robust minimax approach for producing classifiers that directly minimize the cost of mistakes as a convex optimization problem. This is in contrast to previous methods that minimize the empirical risk using a convex surrogate for the cost of mistakes, since minimizing the empirical risk of the actual cost-sensitive loss is generally intractable. By treating properties of the training data as uncertain, our approach avoids these computational difficulties. We develop theory and algorithms for our approach and demonstrate its benefits on cost-sensitive classification tasks.

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