activity
20162021
most citedADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

1.4k citations · 1.4k across the 5 of their papers we have counts for

collaborators

9 papers

cs.CV2021

CSG0: Continual Urban Scene Generation with Zero Forgetting

Himalaya Jain, Tuan-Hung Vu, Patrick Pérez +1

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to…

cs.CV2021★ 1 cited

Semantic Palette: Guiding Scene Generation with Class Proportions

Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain +2

Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous…

cs.CV2019

QUEST: Quantized embedding space for transferring knowledge

Himalaya Jain, Spyros Gidaris, Nikos Komodakis +2

Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network. Most of the existing…

cs.CV2019

This dataset does not exist: training models from generated images

Victor Besnier, Himalaya Jain, Andrei Bursuc +2

Current generative networks are increasingly proficient in generating high-resolution realistic images. These generative networks, especially the conditional ones, can potentially…

cs.CV2019

DADA: Depth-aware Domain Adaptation in Semantic Segmentation

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2

Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it h…

cs.CV2018★ 1.4k cited

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Tuan-Hung Vu, Himalaya Jain, Maxime Bucher +2

Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks,…

cs.CV2017

Learning a Complete Image Indexing Pipeline

Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1

To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the i…

cs.CV2017

SUBIC: A supervised, structured binary code for image search

Himalaya Jain, Joaquin Zepeda, Patrick Pérez +1

For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants…

cs.CV2016★ 4 cited

Approximate search with quantized sparse representations

Himalaya Jain, Patrick Pérez, Rémi Gribonval +2

This paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it. To this end, we propose to approximate database vectors by…