19 citations · 19 across the 12 of their papers we have counts for
13 papers
IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding
Shiwen Zhao, Qi Zhang, Sezer Karaoglu +2
Video Temporal Grounding (VTG) localizes the video segment that matches a natural-language query. Many queries describe an action performed by a particular entity. Existing methods…
Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery
Niels Sombekke, Rob G. J. Wijnhoven, Martin R. Oswald
We present a multi-modal classification framework that fuses satellite and street-level imagery through a Perceiver IO architecture operating on spatial patch tokens from a shared…
Joint Instance Segmentation and Geometric Attribute Regression for Roof Structures in Aerial Imagery
Luuk Versteeg, Rob G. J. Wijnhoven, Martin R. Oswald
We present a method for jointly predicting instance-level roof segment masks together with three continuous geometric attributes -- building height, roof slope, and roof azimuth --…
Cross-Attentive Multiview Fusion of Vision-Language Embeddings
Tomas Berriel Martins, Martin R. Oswald, Javier Civera
Vision-language models have been key to the development of open-vocabulary 2D semantic segmentation. Lifting these models from 2D images to 3D scenes, however, remains a challengin…
Edge-Centric Relational Reasoning for 3D Scene Graph Prediction
Yanni Ma, Hao Liu, Yulan Guo +2
3D scene graph prediction aims to abstract complex 3D environments into structured graphs consisting of objects and their pairwise relationships. Existing approaches typically adop…
3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation
Weijie Wei, Osman Ülger, Fatemeh Karimi Nejadasl +2
Open-Vocabulary Segmentation (OVS) methods offer promising capabilities in detecting unseen object categories, but the category must be known and needs to be provided by a human, e…
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning
Weijie Wei, Fatemeh Karimi Nejadasl, Theo Gevers +1
The scarcity of annotated data in LiDAR point cloud understanding hinders effective representation learning. Consequently, scholars have been actively investigating efficacious sel…
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
Vladimir Yugay, Yue Li, Theo Gevers +1
We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation. Our approach enables interactive-time reconstruction and ph…
Auto-Vocabulary Semantic Segmentation
Osman Ülger, Maksymilian Kulicki, Yuki Asano +1
Open-Vocabulary Segmentation (OVS) methods are capable of performing semantic segmentation without relying on a fixed vocabulary, and in some cases, without training or fine-tuning…
Union-over-Intersections: Object Detection beyond Winner-Takes-All
Aritra Bhowmik, Pascal Mettes, Martin R. Oswald +1
This paper revisits the problem of predicting box locations in object detection architectures. Typically, each box proposal or box query aims to directly maximize the intersection-…
Relational Prior Knowledge Graphs for Detection and Instance Segmentation
Osman Ülger, Yu Wang, Ysbrand Galama +3
Humans have a remarkable ability to perceive and reason about the world around them by understanding the relationships between objects. In this paper, we investigate the effectiven…
SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation
Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors. Despite considerable progress in removing hand-crafted…
APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
Weijie Wei, Martin R. Oswald, Fatemeh Karimi Nejadasl +1
In this paper, we focus on semantic segmentation method for point clouds of urban scenes. Our fundamental concept revolves around the collaborative utilization of diverse scene rep…