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20162025
most citedSelf-supervised Segmentation via Background Inpainting

2 citations · 3 across the 5 of their papers we have counts for

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9 papers · 1 filter

cs.CV2025

Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting

Daiwei Zhang, Joaquin Gajardo, Tomislav Medic +5

Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions b…

cs.CV2024

GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control

Mariam Hassan, Sebastian Stapf, Ahmad Rahimi +17

We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, ou…

cs.CV2022

Dyadic Human Motion Prediction

Isinsu Katircioglu, Costa Georgantas, Mathieu Salzmann +1

Prior work on human motion forecasting has mostly focused on predicting the future motion of single subjects in isolation from their past pose sequence. In the presence of closely…

cs.CV2020

Human Detection and Segmentation via Multi-view Consensus

Isinsu Katircioglu, Helge Rhodin, Jörg Spörri +2

Self-supervised detection and segmentation of foreground objects aims for accuracy without annotated training data. However, existing approaches predominantly rely on restrictive a…

cs.CV20202 cited

Self-supervised Segmentation via Background Inpainting

Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3

While supervised object detection and segmentation methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they h…

cs.CV20191 cited

Self-supervised Training of Proposal-based Segmentation via Background Prediction

Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3

While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained…