Gopika Sudhakaran — accepted AI-conference papers

2025

ART: Adaptive Relation Tuning for Generalized Relation Prediction

Gopika Sudhakaran, Hikaru Shindo, Patrick Schramowski, Simone Schaub-Meyer, Kristian Kersting, Stefan Roth

ICCV 2025poster

Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize beyond the relations on which they are trained. While prompt tuning has been used to adapt vision-language models (VLMs…

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2024

DeiSAM: Segment Anything with Deictic Prompting

Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting

NeurIPS 2024poster

Large-scale, pre-trained neural networks have demonstrated strong capabilities in various tasks, including zero-shot image segmentation. To identify concrete objects in complex scenes, humans instinctively rely on deictic descriptions in natural language, i.e., referring to something depending on th…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2023

Vision Relation Transformer for Unbiased Scene Graph Generation

Gopika Sudhakaran, Devendra Singh Dhami, Kristian Kersting, Stefan Roth

ICCV 2023poster

Recent years have seen a growing interest in Scene Graph Generation (SGG), a comprehensive visual scene understanding task that aims to predict entity relationships using a relation encoder-decoder pipeline stacked on top of an object encoder-decoder backbone. Unfortunately, current SGG methods suff…

Cited by 21 PDF ↗code ↗Scholar ↗