About XRCE

ECCV awards 2016"Unsupervised Domain Adaptation with Regularized Domain Instance Denoising", Gabriela Csurka, Boris Chidlovskii, Stephane Clinchant and Sofia Michel got one of the two best paper awards at the ECCV TASK-CV workshop. And Diane Larlus got a best reviewer award.

8th October, 2016:

New directions in saliency research: Developments in architecture, datasets and evaluation workshop; Naila Murray, invited talk: "Deep networks for eye fixation prediction"

11th-14th October, 2016:
Cesar De Souza and Adrien GaidonSympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition, co-authored with Eleonora Vig and Antonio Lopez.

Albert GordoDiane LarlusJon AlmazanJérôme RevaudDeep Image Retrieval: Learning Global Representations for Image Search 

8th October, 2016:  
Geometry Meets Deep Learning workshop: ”Learning the semantic structure of objects from Web supervision”, David Novotny, Diane Larlus and A. Vedaldi. This Paper will also be presented (poster session): October 10th, 2016, ECCV Workshop on Web-scale Vision and Social Media

9th October, 2016: 
TASK-CV workshop: “Unsupervised Domain Adaptation with Regularized Domain Instance Denoising”,  Gabriela CsurkaBoris ChidlovskiiStéphane Clinchant and Sofia Michel

15th October 2016:
ECCV invited tutorial: Florent Perronnin: "Output embedding for large-scale visual recognition"

Abstract: The focus of the computer vision community has long been on input embedding: how to transform an image or a video into a suitable descriptor? During this tutorial, we will consider the output embedding problem: how to embed classes in a Euclidean space? We will see that output embedding is a must for large-scale visual recognition as it enables parameter sharing: this yields classifiers which are more accurate when training data is scarce (including zero-shot recognition) and which are faster to train and evaluate. We will provide a taxonomy of output embeddings and focus on embeddings based on a priori information (eg class hierarchies, attributes or textual descriptions). We will also explain how to measure the compatibility between input and output embeddings, starting from simple least-square regression and going all the way to deep learning. Finally, we will show recent successful applications of output embedding to web-scale visual classification.