6 citations · 13 across the 19 of their papers we have counts for
24 papers
Physics-Informed Modeling for Wood Thermal Analysis and Prediction
Jingren Xie, Alex John Buckthal, Ryan Anthony O'Connor +2
Wood materials exhibit complex, spatially varying thermal properties that challenge traditional architectural assumptions of material homogeneity. Although data-driven approaches c…
HiddenObjects: Scalable Diffusion-Distilled Spatial Priors for Object Placement
Marco Schouten, Ioannis Siglidis, Serge Belongie +1
We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement knowledge encoded in text-condit…
Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels
Juan Miguel Valverde, Dim P. Papadopoulos, Rasmus Larsen +1
Standard deep learning models for image segmentation cannot guarantee topology accuracy, failing to preserve the correct number of connected components or structures. This, in turn…
Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training
Kaixuan Lu, Mehmet Onurcan Kaya, Dim P. Papadopoulos
Video Instance Segmentation (VIS) faces significant annotation challenges due to its dual requirements of pixel-level masks and temporal consistency labels. While recent unsupervis…
MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding
Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose +2
Geo-spatial analysis of our world benefits from a multimodal approach, as every single geographic location can be described in numerous ways (images from various viewpoints, textua…
Visual Autoregressive Models Beat Diffusion Models on Inference Time Scaling
Erik Riise, Mehmet Onurcan Kaya, Dim P. Papadopoulos
While inference-time scaling through search has revolutionized Large Language Models, translating these gains to image generation has proven difficult. Recent attempts to apply sea…
Efficient Test-Time Scaling for Small Vision-Language Models
Mehmet Onurcan Kaya, Desmond Elliott, Dim P. Papadopoulos
Small Vision-Language Models (VLMs) provide a computationally efficient alternative to larger models, at the cost of weaker generalization abilities and downstream task performance…
AutoQ-VIS: Improving Unsupervised Video Instance Segmentation via Automatic Quality Assessment
Kaixuan Lu, Mehmet Onurcan Kaya, Dim P. Papadopoulos
Video Instance Segmentation (VIS) faces significant annotation challenges due to its dual requirements of pixel-level masks and temporal consistency labels. While recent unsupervis…
Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding Boxes
Andreas Lau Hansen, Lukas Wanzeck, Dim P. Papadopoulos
Monocular 3D object detection is an essential task in computer vision, and it has several applications in robotics and virtual reality. However, 3D object detectors are typically t…
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild
Jonas Myhre Schiøtt, Viktor Sebastian Petersen, Dim P. Papadopoulos
Computer vision models have seen increased usage in sports, and reinforcement learning (RL) is famous for beating humans in strategic games such as Chess and Go. In this paper, we…
POEM: Precise Object-level Editing via MLLM control
Marco Schouten, Mehmet Onurcan Kaya, Serge Belongie +1
Diffusion models have significantly improved text-to-image generation, producing high-quality, realistic images from textual descriptions. Beyond generation, object-level image edi…
Studying Image Diffusion Features for Zero-Shot Video Object Segmentation
Thanos Delatolas, Vicky Kalogeiton, Dim P. Papadopoulos
This paper investigates the use of large-scale diffusion models for Zero-Shot Video Object Segmentation (ZS-VOS) without fine-tuning on video data or training on any image segmenta…
Latent Directions: A Simple Pathway to Bias Mitigation in Generative AI
Carolina Lopez Olmos, Alexandros Neophytou, Sunando Sengupta +1
Mitigating biases in generative AI and, particularly in text-to-image models, is of high importance given their growing implications in society. The biased datasets used for traini…
CrashCar101: Procedural Generation for Damage Assessment
Jens Parslov, Erik Riise, Dim P. Papadopoulos
In this paper, we are interested in addressing the problem of damage assessment for vehicles, such as cars. This task requires not only detecting the location and the extent of the…
Learning the What and How of Annotation in Video Object Segmentation
Thanos Delatolas, Vicky Kalogeiton, Dim P. Papadopoulos
Video Object Segmentation (VOS) is crucial for several applications, from video editing to video data generation. Training a VOS model requires an abundance of manually labeled tra…
Learning Program Representations for Food Images and Cooking Recipes
Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko +3
In this paper, we are interested in modeling a how-to instructional procedure, such as a cooking recipe, with a meaningful and rich high-level representation. Specifically, we prop…
Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents
Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza +3
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident…
Scaling up instance annotation via label propagation
Dim P. Papadopoulos, Ethan Weber, Antonio Torralba
Manually annotating object segmentation masks is very time-consuming. While interactive segmentation methods offer a more efficient alternative, they become unaffordable at a large…
Detecting natural disasters, damage, and incidents in the wild
Ethan Weber, Nuria Marzo, Dim P. Papadopoulos +5
Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emer…
How to make a pizza: Learning a compositional layer-based GAN model
Dim P. Papadopoulos, Youssef Tamaazousti, Ferda Ofli +2
A food recipe is an ordered set of instructions for preparing a particular dish. From a visual perspective, every instruction step can be seen as a way to change the visual appeara…
Extreme clicking for efficient object annotation
Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller +1
Manually annotating object bounding boxes is central to building computer vision datasets, and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box […
How hard can it be? Estimating the difficulty of visual search in an image
Radu Tudor Ionescu, Bogdan Alexe, Marius Leordeanu +3
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the…
Programmable Crossbar Quantum-dot Cellular Automata Circuits
Vicky S. Kalogeiton, Dim P. Papadopoulos, Orestis Liolis +3
Quantum-dot fabrication and characterization is a well-established technology, which is used in photonics, quantum optics and nanoelectronics. Four quantum-dots placed at the corne…
We don't need no bounding-boxes: Training object class detectors using only human verification
Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller +1
Training object class detectors typically requires a large set of images in which objects are annotated by bounding-boxes. However, manually drawing bounding-boxes is very time con…