most citedVLN-Trans: Translator for the Vision and Language Navigation Agent

1 citations · 6 across the 10 of their papers we have counts for

collaborators

10 papers

cs.AI20241 cited

Find The Gap: Knowledge Base Reasoning For Visual Question Answering

Elham J. Barezi, Parisa Kordjamshidi

We analyze knowledge-based visual question answering, for which given a question, the models need to ground it into the visual modality and retrieve the relevant knowledge from a g…

cs.AI2024

Consistent Joint Decision-Making with Heterogeneous Learning Models

Hossein Rajaby Faghihi, Parisa Kordjamshidi

This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer…

cs.CL2024

NavHint: Vision and Language Navigation Agent with a Hint Generator

Yue Zhang, Quan Guo, Parisa Kordjamshidi

Existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of…

cs.CV20231 cited

GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot Learning

Guangyue Xu, Joyce Chai, Parisa Kordjamshidi

Pre-trained vision-language models (VLMs) have achieved promising success in many fields, especially with prompt learning paradigm. In this work, we propose GIP-COL (Graph-Injected…

cs.CL20231 cited

Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments

Danial Kamali, Parisa Kordjamshidi

Compositional generalization, the ability of intelligent models to extrapolate understanding of components to novel compositions, is a fundamental yet challenging facet in AI resea…

cs.CL20231 cited

MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition

Guangyue Xu, Parisa Kordjamshidi, Joyce Chai

Humans have the ability to learn novel compositional concepts by recalling and generalizing primitive concepts acquired from past experiences. Inspired by this observation, in this…