activity
20162026
most citedConvolutional Oriented Boundaries

140 citations · 574 across the 120 of their papers we have counts for

collaborators
Showing 2023 · cs.CVShow all

28 papers · 2 filters

cs.CV2023

Residual Learning for Image Point Descriptors

Rashik Shrestha, Ajad Chhatkuli, Menelaos Kanakis +1

Local image feature descriptors have had a tremendous impact on the development and application of computer vision methods. It is therefore unsurprising that significant efforts ar…

cs.CV2023★ 1 cited

Diffusion-Based Particle-DETR for BEV Perception

Asen Nachkov, Martin Danelljan, Danda Pani Paudel +1

The Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) due to its well suited compatibility to downstream t…

cs.CV2023★ 1 cited

Ternary-Type Opacity and Hybrid Odometry for RGB NeRF-SLAM

Junru Lin, Asen Nachkov, Songyou Peng +2

In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information,…

cs.CV2023

Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction

M. Eren Akbiyik, Nedko Savov, Danda Pani Paudel +5

Understanding drivers' decision-making is crucial for road safety. Although predicting the ego-vehicle's path is valuable for driver-assistance systems, existing methods mainly foc…

cs.CV2023

Model-aware 3D Eye Gaze from Weak and Few-shot Supervisions

Nikola Popovic, Dimitrios Christodoulou, Danda Pani Paudel +2

The task of predicting 3D eye gaze from eye images can be performed either by (a) end-to-end learning for image-to-gaze mapping or by (b) fitting a 3D eye model onto images. The fo…

cs.CV2023★ 24 cited

Contrastive Learning for Multi-Object Tracking with Transformers

Pierre-François De Plaen, Nicola Marinello, Marc Proesmans +2

The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Pr…