Search Results for author:
Found 16 papers, 8 papers with code
The Group Loss++: A deeper look into group loss for deep metric learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection
Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data.
The 2021 Image Similarity Dataset and Challenge
This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021).
Ranked #1 on
Image Similarity Detection
on DISC21 dev
Active Learning for Deep Object Detection via Probabilistic Modeling
Most of these methods are based on multiple models or are straightforward extensions of classification methods, hence estimate an image's informativeness using only the classification head.
Learning Intra-Batch Connections for Deep Metric Learning
To this end, we propose an approach based on message passing networks that takes all the relations in a mini-batch into account.
Ranked #2 on
Metric Learning
on CARS196
Deep Active Learning for Object Detection with Mixture Density Networks
For active learning, we propose a scoring function that aggregates uncertainties from both the classification and the localization outputs of the network.
Exploiting Contextual Information with Deep Neural Networks
In this thesis, we show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly.
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
The Group Loss for Deep Metric Learning
Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.
Ranked #16 on
Metric Learning
on CUB-200-2011
(using extra training data)
DeepScores and Deep Watershed Detection: current state and open issues
This paper gives an overview of our current Optical Music Recognition (OMR) research.
Deep Learning in the Wild
Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks.
Learning Neural Models for End-to-End Clustering
We propose a novel end-to-end neural network architecture that, once trained, directly outputs a probabilistic clustering of a batch of input examples in one pass.
Deep Watershed Detector for Music Object Recognition
Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline.
Transductive Label Augmentation for Improved Deep Network Learning
Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach.
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition
Introduced in the mid-1970's as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science.
