110 citations · 334 across the 37 of their papers we have counts for
15 papers · 1 filter
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…
Gaussian Grouping: Segment and Edit Anything in 3D Scenes
Mingqiao Ye, Martin Danelljan, Fisher Yu +1
The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of the 3D scenes. However, it is solely concentrated on the appearance and geometry modeling,…
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing
Jan-Nico Zaech, Martin Danelljan, Tolga Birdal +1
Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which…
R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras
Aron Schmied, Tobias Fischer, Martin Danelljan +2
Dense 3D reconstruction and ego-motion estimation are key challenges in autonomous driving and robotics. Compared to the complex, multi-modal systems deployed today, multi-camera s…
MolGrapher: Graph-based Visual Recognition of Chemical Structures
Lucas Morin, Martin Danelljan, Maria Isabel Agea +5
The automatic analysis of chemical literature has immense potential to accelerate the discovery of new materials and drugs. Much of the critical information in patent documents and…
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
Chunming He, Kai Li, Yachao Zhang +5
Camouflaged object detection (COD) is the challenging task of identifying camouflaged objects visually blended into surroundings. Albeit achieving remarkable success, existing COD…
Cascade-DETR: Delving into High-Quality Universal Object Detection
Mingqiao Ye, Lei Ke, Siyuan Li +4
Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not comp…
Prompting Diffusion Representations for Cross-Domain Semantic Segmentation
Rui Gong, Martin Danelljan, Han Sun +2
While originally designed for image generation, diffusion models have recently shown to provide excellent pretrained feature representations for semantic segmentation. Intrigued by…
Segment Anything Meets Point Tracking
Frano Rajič, Lei Ke, Yu-Wing Tai +3
The Segment Anything Model (SAM) has established itself as a powerful zero-shot image segmentation model, enabled by efficient point-centric annotation and prompt-based models. Whi…
Segment Anything in High Quality
Lei Ke, Mingqiao Ye, Martin Danelljan +4
The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being…
StyleGenes: Discrete and Efficient Latent Distributions for GANs
Evangelos Ntavelis, Mohamad Shahbazi, Iason Kastanis +3
We propose a discrete latent distribution for Generative Adversarial Networks (GANs). Instead of drawing latent vectors from a continuous prior, we sample from a finite set of lear…
OVTrack: Open-Vocabulary Multiple Object Tracking
Siyuan Li, Tobias Fischer, Lei Ke +3
The ability to recognize, localize and track dynamic objects in a scene is fundamental to many real-world applications, such as self-driving and robotic systems. Yet, traditional m…
Mask-Free Video Instance Segmentation
Lei Ke, Martin Danelljan, Henghui Ding +3
The recent advancement in Video Instance Segmentation (VIS) has largely been driven by the use of deeper and increasingly data-hungry transformer-based models. However, video masks…
NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions
Mohamad Shahbazi, Evangelos Ntavelis, Alessio Tonioni +4
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors. Rec…
How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön
Many important computer vision applications are naturally formulated as regression problems. Within medical imaging, accurate regression models have the potential to automate vario…