Search Results for author: Ehsan Elhamifar

Found 25 papers, 7 papers with code

Self-Supervised Multi-Task Procedure Learning from Instructional Videos

no code implementations ECCV 2020 Ehsan Elhamifar, Dat Huynh

We address the problem of unsupervised procedure learning from instructional videos of multiple tasks using Deep Neural Networks (DNNs).

Procedure Learning Video Classification

BIT: Bi-Level Temporal Modeling for Efficient Supervised Action Segmentation

no code implementations28 Aug 2023 Zijia Lu, Ehsan Elhamifar

We address the task of supervised action segmentation which aims to partition a video into non-overlapping segments, each representing a different action.

Action Segmentation Segmentation

Exploring the Role of Audio in Video Captioning

no code implementations21 Jun 2023 YuHan Shen, Linjie Yang, Longyin Wen, Haichao Yu, Ehsan Elhamifar, Heng Wang

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Towards Effective Multi-Label Recognition Attacks via Knowledge Graph Consistency

no code implementations11 Jul 2022 Hassan Mahmood, Ehsan Elhamifar

We show that the naive extensions of multi-class attacks to the multi-label setting lead to violating label relationships, modeled by a knowledge graph, and can be detected using a consistency verification scheme.

Multi-Label Learning

Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order Consistency

1 code implementation CVPR 2022 Zijia Lu, Ehsan Elhamifar

We address the problem of set-supervised action learning, whose goal is to learn an action segmentation model using weak supervision in the form of sets of actions occurring in training videos.

Pseudo Label Weakly Supervised Action Segmentation (Action Set))

Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task Videos

1 code implementation CVPR 2022 YuHan Shen, Ehsan Elhamifar

To compute the SRE loss, we develop a flexible transcript prediction (FTP) method that uses the output of the action classifier to find both the length of the transcript and the sequence of actions occurring in an unlabeled video.

Action Segmentation Weakly-supervised Learning

Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

1 code implementation CVPR 2022 Dat Huynh, Jason Kuen, Zhe Lin, Jiuxiang Gu, Ehsan Elhamifar

To address this, we propose a cross-modal pseudo-labeling framework, which generates training pseudo masks by aligning word semantics in captions with visual features of object masks in images.

Instance Segmentation Semantic Segmentation

Introvert: Human Trajectory Prediction via Conditional 3D Attention

no code implementations CVPR 2021 Nasim Shafiee, Taskin Padir, Ehsan Elhamifar

Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars.

Self-Driving Cars Trajectory Prediction

Learning To Segment Actions From Visual and Language Instructions via Differentiable Weak Sequence Alignment

1 code implementation CVPR 2021 YuHan Shen, Lu Wang, Ehsan Elhamifar

We address the problem of unsupervised localization of key-steps and feature learning in instructional videos using both visual and language instructions.

Compositional Fine-Grained Low-Shot Learning

no code implementations21 May 2021 Dat Huynh, Ehsan Elhamifar

In addition, instead of building holistic features for classes, we use our attribute features to form dense representations capable of capturing fine-grained attribute details of classes.

Attribute Few-Shot Learning +1

Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations

1 code implementation ICCV 2021 Dat Huynh, Ehsan Elhamifar

We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some interactions do not have training images.

Human-Object Interaction Detection Multi-label zero-shot learning +1

Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition

no code implementations NeurIPS 2020 Dat Huynh, Ehsan Elhamifar

We propose a feature composition framework that learns to extract attribute-based features from training samples and combines them to construct fine-grained features for unseen classes.

Attribute Compositional Zero-Shot Learning

Weighting Is Worth the Wait: Bayesian Optimization with Importance Sampling

no code implementations23 Feb 2020 Setareh Ariafar, Zelda Mariet, Ehsan Elhamifar, Dana Brooks, Jennifer Dy, Jasper Snoek

Casting hyperparameter search as a multi-task Bayesian optimization problem over both hyperparameters and importance sampling design achieves the best of both worlds: by learning a parameterization of IS that trades-off evaluation complexity and quality, we improve upon Bayesian optimization state-of-the-art runtime and final validation error across a variety of datasets and complex neural architectures.

Bayesian Optimization

Deep Supervised Summarization: Algorithm and Application to Learning Instructions

no code implementations NeurIPS 2019 Chengguang Xu, Ehsan Elhamifar

We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representatives.

Representation Learning

Unsupervised Procedure Learning via Joint Dynamic Summarization

no code implementations ICCV 2019 Ehsan Elhamifar, Zwe Naing

To extract procedure key-steps, we develop an optimization framework that finds a sequence of a small number of states that well represents all videos and is compatible with the state transition model.

Procedure Learning

Subset Selection and Summarization in Sequential Data

no code implementations NeurIPS 2017 Ehsan Elhamifar, M. Clara De Paolis Kaluza

In this paper, we develop a new framework for sequential subset selection that finds a set of representatives compatible with the dynamic models of data.

Time Series Time Series Analysis +1

Online Summarization via Submodular and Convex Optimization

no code implementations CVPR 2017 Ehsan Elhamifar, M. Clara De Paolis Kaluza

As the proposed optimization is, in general, NP-hard and non-convex, we study a greedy approach based on unconstrained submodular optimization and also propose an efficient convex relaxation.

Video Summarization

High-Rank Matrix Completion and Clustering under Self-Expressive Models

no code implementations NeurIPS 2016 Ehsan Elhamifar

We propose efficient algorithms for simultaneous clustering and completion of incomplete high-dimensional data that lie in a union of low-dimensional subspaces.

Clustering Low-Rank Matrix Completion +2

Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained $\ell_1$-Minimization

no code implementations23 Dec 2014 Ehsan Elhamifar, Mahdi Soltanolkotabi, Shankar Sastry

High-dimensional data often lie in low-dimensional subspaces corresponding to different classes they belong to.

Clustering

Dissimilarity-based Sparse Subset Selection

no code implementations25 Jul 2014 Ehsan Elhamifar, Guillermo Sapiro, S. Shankar Sastry

The solution of our optimization finds representatives and the assignment of each element of the target set to each representative, hence, obtaining a clustering.

Clustering Recommendation Systems +1

Robust subspace clustering

no code implementations11 Jan 2013 Mahdi Soltanolkotabi, Ehsan Elhamifar, Emmanuel J. Candès

Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space.

Clustering

Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery

no code implementations NeurIPS 2012 Ehsan Elhamifar, Guillermo Sapiro, René Vidal

Given pairwise dissimilarities between data points, we consider the problem of finding a subset of data points called representatives or exemplars that can efficiently describe the data collection.

Sparse Subspace Clustering: Algorithm, Theory, and Applications

4 code implementations5 Mar 2012 Ehsan Elhamifar, Rene Vidal

In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.

Clustering Face Clustering +1

Sparse Manifold Clustering and Embedding

no code implementations NeurIPS 2011 Ehsan Elhamifar, René Vidal

We propose an algorithm called Sparse Manifold Clustering and Embedding (SMCE) for simultaneous clustering and dimensionality reduction of data lying in multiple nonlinear manifolds.

Clustering Dimensionality Reduction

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