NAVER LABS releases world's biggest visual localization dataset of indoor spaces with over 130K images. Dataset built with NAVER LABS mapping robots M1X & COMET and available in unified data format kapture.
14 May 2021
Jinyoung Choi, Christopher Dance, Jung-eun Kim, Kyung-sik Park, Jaehun Han, Joonho Seo, Minsu Kim - 14 May 2021
Using a novel algorithm we explore how effectively a single policy, learned by reinforcement learning, can modulate robot behaviour from risk-averse to risk-neutral, so that robots can safely navigate everyday environments like homes and shops.
A novel framework for controlled NLG called 'Generation with Distributional Control', achieves great generality on the types of constraints that can be imposed and has a large potential to remedy the problem of bias in language models.
Our Global BERT-based Transformer architecture fuses global and local information at every layer, resulting in a reading comprehension model that achieves a deeper understanding for long documents and enables flexibility for downstream tasks.
Visual localization is used by robots and self-driving cars to estimate their position and in AR apps to interact with the physical world both indoors and out in the open. This article gives an overview of current state-of-the-art methods and their advantages and drawbacks.
By dissecting the matching process of the recent ColBERT model, we make a step towards unveiling the ranking properties of BERT-based ranking models and show that ColBERT (implicitly) learns a notion of term importance that correlates with IDF.
Running into the unknown with RunAhead: finding pleasant running tours and intuitively navigating through them. The use of combinatorial optimization and UX design, together with head-tracking technology, provides a non-intrusive way of helping runners to discover new itineraries.
A novel framework which uses individual sample losses as error measures to determine the relative difficulty of samples in a dataset. Can be plugged on top of existing neural network models to implement curriculum learning for any task, even with noisy datasets.