Search Results for author:
Found 24 papers, 13 papers with code
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.
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.
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.
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.
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 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.
DeepScores and Deep Watershed Detection: current state and open issues
This paper gives an overview of our current Optical Music Recognition (OMR) research.
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 #20 on
Metric Learning
on CUB-200-2011
(using extra training data)
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.
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.
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.
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 #3 on
Metric Learning
on CARS196
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.
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
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 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.
Simple Cues Lead to a Strong Multi-Object Tracker
For association, most models resourced to motion and appearance cues, e. g., re-identification networks.
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
This allows us to improve the generalization of a CNN-based model without any increase in the number of weights at test time.
Learning to Discover and Detect Objects
We then train our network to learn to classify each RoI, either as one of the known classes, seen in the source dataset, or one of the novel classes, with a long-tail distribution constraint on the class assignments, reflecting the natural frequency of classes in the real world.
Ranked #2 on
Novel Object Detection
on LVIS v1.0 val
Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis
To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task.
Three Heads Are Better Than One: Complementary Experts for Long-Tailed Semi-supervised Learning
For instance, on CIFAR-10-LT, CPE improves test accuracy by over 2. 22% compared to baselines.
G3DR: Generative 3D Reconstruction in ImageNet
We introduce a novel 3D generative method, Generative 3D Reconstruction (G3DR) in ImageNet, capable of generating diverse and high-quality 3D objects from single images, addressing the limitations of existing methods.
$V_kD:$ Improving Knowledge Distillation using Orthogonal Projections
Knowledge distillation is an effective method for training small and efficient deep learning models.
Ranked #2 on
Knowledge Distillation
on ImageNet
Deep Active Learning: A Reality Check
By uncovering the limitations of current methods and understanding the impact of different experimental settings, we aim to inspire more efficient training of deep learning models in real-world scenarios with limited annotation budgets.
