Search Results for author: Sima Behpour

Found 9 papers, 3 papers with code

A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets

2 code implementations10 Mar 2024 Thang Doan, Sima Behpour, Xin Li, Wenbin He, Liang Gou, Liu Ren

Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting.

Few-Shot Class-Incremental Learning Incremental Learning

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

no code implementations ICML Workshop LifelongML 2020 Seungwon Lee, Sima Behpour, Eric Eaton

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.

Active Learning in Video Tracking

no code implementations29 Dec 2019 Sima Behpour

We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos.

Active Learning Computational Efficiency +6

Efficient and Consistent Adversarial Bipartite Matching

no code implementations ICML 2018 Rizal Fathony, Sima Behpour, Xinhua Zhang, Brian 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.

Computational Efficiency Learning-To-Rank +2

ADA: A Game-Theoretic Perspective on Data Augmentation for Object Detection

no code implementations21 Oct 2017 Sima Behpour, Kris M. Kitani, Brian D. Ziebart

We aim to find an optimal adversarial perturbations of the ground truth data (i. e., the worst case perturbations) that forces the object bounding box predictor to learn from the hardest distribution of perturbed examples for better test-time performance.

Data Augmentation Object +3

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