activity
20162024
most citedSemantic Image Based Geolocation Given a Map

19 citations · 19 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Q-GroundCAM: Quantifying Grounding in Vision Language Models via GradCAM

Navid Rajabi, Jana Kosecka

Vision and Language Models (VLMs) continue to demonstrate remarkable zero-shot (ZS) performance across various tasks. However, many probing studies have revealed that even the best…

cs.CL2024

Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking

Ivana Beňová, Jana Košecká, Michal Gregor +3

The dominant probing approaches rely on the zero-shot performance of image-text matching tasks to gain a finer-grained understanding of the representations learned by recent multim…

cs.CV2023

Labeling Indoor Scenes with Fusion of Out-of-the-Box Perception Models

Yimeng Li, Navid Rajabi, Sulabh Shrestha +2

The image annotation stage is a critical and often the most time-consuming part required for training and evaluating object detection and semantic segmentation models. Deployment o…

cs.CV2023

Graph-CoVis: GNN-based Multi-view Panorama Global Pose Estimation

Negar Nejatishahidin, Will Hutchcroft, Manjunath Narayana +5

In this paper, we address the problem of wide-baseline camera pose estimation from a group of 360 panoramas under upright-camera assumption. Recent work has demonstrated th…

cs.CV2023

U2RLE: Uncertainty-Guided 2-Stage Room Layout Estimation

Pooya Fayyazsanavi, Zhiqiang Wan, Will Hutchcroft +4

While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this…

cs.CV201619 cited

Semantic Image Based Geolocation Given a Map

Arsalan Mousavian, Jana Kosecka

The problem visual place recognition is commonly used strategy for localization. Most successful appearance based methods typically rely on a large database of views endowed with l…