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Dim P. Papadopoulos

10 accepted papers

2026

MMLandmarks: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding

Oskar Kristoffersen, Alba Reinders Sánchez, Morten Rieger Hannemose, Anders Bjorholm Dahl, Dim P. Papadopoulos
CVPR 2026

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, textual descriptions, geographic coordinates, etc.). Current benchmarks have limited coverage across modalities, leading to sp

Cited by 0Source ↗Scholar ↗
2026

Towards High-Quality Image Segmentation: Improving Topology Accuracy by Penalizing Neighbor Pixels

Juan Miguel Valverde, Dim P. Papadopoulos, Rasmus Larsen, Anders Bjorholm Dahl
CVPR 2026

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, affects the quality of the segmentations and compromises the reliability of the subsequent quantification analyses. Pr

Cited by 0Source ↗code ↗Scholar ↗
2022

Learning Program Representations for Food Images and Cooking Recipes

Dim P. Papadopoulos, Enrique Mora, Nadiia Chepurko, Kuan Wei Huang, Ferda Ofli, Antonio Torralba
CVPR 2022oral

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 propose to represent cooking recipes and food images as cooking programs. Programs provide a structured representation of th…

Cited by 41PDF ↗Scholar ↗
2020

Detecting Natural Disasters, Damage, and Incidents in the Wild

Ethan Weber, Nuria Marzo, Dim P. Papadopoulos, Aritro Biswas, Agata Lapedriza, Ferda Ofli +2
ECCV 2020poster

damage, and incidents in the wild","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 emerged as a low-latency data source to quickly understand disaster situations. While mo…

Cited by 77Source ↗PDF ↗Scholar ↗
2019

How to Make a Pizza: Learning a Compositional Layer-Based GAN Model

Dim P. Papadopoulos, Youssef Tamaazousti, Ferda Ofli, Ingmar Weber, Antonio Torralba
CVPR 2019poster

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 appearance of the dish by adding extra objects (e.g., adding an ingredient) or changing the appearance of the existing ones (e.…

Cited by 48PDF ↗Scholar ↗
2017

Extreme Clicking for Efficient Object Annotation

Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
ICCV 2017poster

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 [62]). It involves clicking on imaginary corners of a tight box around the object. This is difficult as these corners are…

Cited by 330PDF ↗Scholar ↗
2017

Training Object Class Detectors With Click Supervision

Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
CVPR 2017spotlight

Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotation time by proposing center-click annotations: we ask annotators to click on the…

Cited by 157PDF ↗Scholar ↗
2016

How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image

Radu Tudor Ionescu, Bogdan Alexe, Marius Leordeanu, Marius Popescu, Dim P. Papadopoulos, Vittorio Ferrari
CVPR 2016poster

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 PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties ca…

Cited by 164PDF ↗Scholar ↗
2016

We Don't Need No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification

Dim P. Papadopoulos, Jasper R. R. Uijlings, Frank Keller, Vittorio Ferrari
CVPR 2016spotlight

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 consuming. We propose a new scheme for training object detectors which only requires annotators to verify bounding-boxes pr…

Cited by 179PDF ↗Scholar ↗