Search Results for author:
Found 47 papers, 27 papers with code
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish
Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0. 62cm in images with no occlusion and 1. 38cm in images with occlusion.
Multi-modal classification of forest biodiversity potential from 2D orthophotos and 3D airborne laser scanning point clouds
We introduce the BioVista dataset, comprising 44. 378 paired samples of orthophotos and ALS point clouds from temperate forests in Denmark, designed to explore multi-modal fusion approaches for biodiversity potential classification.
Verifying Machine Unlearning with Explainable AI
We investigate the effectiveness of Explainable AI (XAI) in verifying Machine Unlearning (MU) within the context of harbor front monitoring, focusing on data privacy and regulatory compliance.
Agglomerative Token Clustering
We present Agglomerative Token Clustering (ATC), a novel token merging method that consistently outperforms previous token merging and pruning methods across image classification, image synthesis, and object detection & segmentation tasks.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Underwater Uncertainty: A Multi-Annotator Image Dataset for Benthic Habitat Classification
Continuous inspection and mapping of the seabed allows for monitoring the impact of anthropogenic activities on benthic ecosystems.
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified
In this study, we formulate the task of Video Anomaly Detection as a probabilistic analysis of object bounding boxes.
Ranked #1 on
Anomaly Detection
on Street Scene
An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
Our suite of benchmarking experiments use encoders pretrained solely on ImageNet-1k with either supervised or self-supervised training techniques, deployed on image datasets that were not seen during training, and clustered with conventional clustering algorithms.
Foundation Models for Video Understanding: A Survey
Additionally, we offer an in-depth performance analysis of these models for the 6 most common video tasks.
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
We present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point cloud dataset, designed to advance research and development in underground utility surveying and mapping.
T-DEED: Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in Sports Videos
In this paper, we introduce T-DEED, a Temporal-Discriminability Enhancer Encoder-Decoder for Precise Event Spotting in sports videos.
A noisy elephant in the room: Is your out-of-distribution detector robust to label noise?
The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems.
Image Classification with Label Noise
Out of Distribution (OOD) Detection
ASTRA: An Action Spotting TRAnsformer for Soccer Videos
In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches.
Raw Instinct: Trust Your Classifiers and Skip the Conversion
Using RAW-images in computer vision problems is surprisingly underexplored considering that converting from RAW to RGB does not introduce any new capture information.
SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach
Explainable AI (XAI) aids in deciphering 'black-box' models.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
CL-MAE: Curriculum-Learned Masked Autoencoders
In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.
Which Tokens to Use? Investigating Token Reduction in Vision Transformers
While different methods have been explored to achieve this goal, we still lack understanding of the resulting reduction patterns and how those patterns differ across token reduction methods and datasets.
Beyond AUROC & co. for evaluating out-of-distribution detection performance
While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated.
BrackishMOT: The Brackish Multi-Object Tracking Dataset
BrackishMOT consists of 98 sequences captured in the wild.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Ranked #5 on
Anomaly Detection
on CUHK Avenue
MOTCOM: The Multi-Object Tracking Dataset Complexity Metric
There exists no comprehensive metric for describing the complexity of Multi-Object Tracking (MOT) sequences.
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #3 on
Anomaly Detection
on CUHK Avenue
Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes
Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images.
Video Transformers: A Survey
Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video.
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Ranked #1 on
Anomaly Detection
on CUHK Avenue
(TBDC metric)
Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder
In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized.
Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactions
Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for.
Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images
In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.
Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark
To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML.
Real-World Super-Resolution of Face-Images from Surveillance Cameras
Experimental results on both real and artificially corrupted face images show that our method results in more detailed reconstructions with less noise compared to existing State-of-the-Art (SoTA) methods.
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method
Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models.
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.
Ranked #1 on
Camera shot segmentation
on SoccerNet-v2
3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset
In this work we present a novel publicly available stereo based 3D RGB dataset for multi-object zebrafish tracking, called 3D-ZeF.
3D Multi-Object Tracking
3D Object Detection From Stereo Images
SIDU: Similarity Difference and Uniqueness Method for Explainable AI
A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability.
Multimodal and multiview distillation for real-time player detection on a football field
As an alternative, we developed a system that detects players from a unique cheap and wide-angle fisheye camera assisted by a single narrow-angle thermal camera.
Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage
We show that accuracy improvements can be made with more complex meta-architectures and speed can be optimised by decreasing the image size with only slight losses in accuracy.
A Context-Aware Loss Function for Action Spotting in Soccer Videos
We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12. 8% over the baseline.
Ranked #3 on
Action Spotting
on SoccerNet
Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras
In this paper, we design a system for the detection of rainfall by the use of surveillance cameras.
Rain Removal in Traffic Surveillance: Does it Matter?
We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow.
The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos
This tech report gives an introduction to two annotation toolboxes that enable the creation of pixel and polygon-based masks as well as bounding boxes around objects of interest.
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks
In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e. g., spiky peaks for popular events, and periodicities for repetitive events.
A comprehensive study of sparse codes on abnormality detection
Sparse representation has been applied successfully in abnormal event detection, in which the baseline is to learn a dictionary accompanied by sparse codes.
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery
This paper presents a robust occupancy analysis system for thermal imaging.
