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Home/Authors/Ehsan Elhamifar

Ehsan Elhamifar

Topic trends: 32,543 papers · similarity ≥ 0.4 · year ≥ 2024 · Data sourced from Semantic Scholar

34,598 papers | Abstracts: 31,650 (91.5%) | Citations: 34,598 (100.0%) | arXiv: 26,074 (75.4%)

Built: Feb 14, 2026, 9:46 PM AMS

26

papers

190

total citations

papers (26)

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

Error Detection in Egocentric Procedural Task Videos

Learning to Predict Activity Progress by Self-Supervised Video Alignment

Semantic-Aware Multi-Label Adversarial Attacks

Two-Stage Active Learning for Efficient Temporal Action Segmentation

MOSCATO: Predicting Multiple Object State Change Through Actions

Understanding Multi-Task Activities from Single-Task Videos

DeCafNet: Delegate and Conquer for Efficient Temporal Grounding in Long Videos

Multi-Modal Few-Shot Temporal Action Segmentation

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

Zero-Shot Attribute Attacks on Fine-Grained Recognition Models

Compositional Targeted Multi-Label Universal Perturbations

A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning

Error Recognition in Procedural Videos using Generalized Task Graph

Interactive Multi-Label CNN Learning With Partial Labels

FACT: Frame-Action Cross-Attention Temporal Modeling for Efficient Action Segmentation

Progress-Aware Online Action Segmentation for Egocentric Procedural Task Videos

Learning to Segment Referred Objects from Narrated Egocentric Videos

Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention

Introvert: Human Trajectory Prediction via Conditional 3D Attention

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

Weakly-Supervised Action Segmentation and Alignment via Transcript-Aware Union-of-Subspaces Learning

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

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

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

Self-Supervised Multi-Task Procedure Learning from Instructional Videos