Search Results for author:
Found 17 papers, 11 papers with code
Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking
To solve this problem, we propose to distill representations of the TIR modality from the RGB modality with Cross-Modal Distillation (CMD) on a large amount of unlabeled paired RGB-TIR data.
MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to Limited Data Domains
Therefore, we propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
Semi-supervised Learning for Few-shot Image-to-Image Translation
In this work, we go one step further and reduce the amount of required labeled data also from the source domain during training.
MineGAN: effective knowledge transfer from GANs to target domains with few images
We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain, either from a single or multiple pretrained GANs.
Orderless Recurrent Models for Multi-label Classification
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification.
Active Learning for Deep Detection Neural Networks
In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks.
Multi-Modal Fusion for End-to-End RGB-T Tracking
Our tracker is trained in an end-to-end manner, enabling the components to learn how to fuse the information from both modalities.
Ranked #7 on
Rgb-T Tracking
on RGBT210
Temporal Coherence for Active Learning in Videos
Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives.
SDIT: Scalable and Diverse Cross-domain Image Translation
Recently, image-to-image translation research has witnessed remarkable progress.
Learning the Model Update for Siamese Trackers
In general, this template is linearly combined with the accumulated template from the previous frame, resulting in an exponential decay of information over time.
Controlling biases and diversity in diverse image-to-image translation
The task of unpaired image-to-image translation is highly challenging due to the lack of explicit cross-domain pairs of instances.
Synthetic data generation for end-to-end thermal infrared tracking
These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking.
Image-to-image translation for cross-domain disentanglement
We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.
Transferring GANs: generating images from limited data
Transferring the knowledge of pretrained networks to new domains by means of finetuning is a widely used practice for applications based on discriminative models.
Ranked #7 on
10-shot image generation
on Babies
Objects as context for detecting their semantic parts
We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect.
Do semantic parts emerge in Convolutional Neural Networks?
We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network.
An active search strategy for efficient object class detection
First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set.
