Collaborating Authors

Ordering Matters: Word Ordering Aware Unsupervised NMT Machine Learning

Specifically, given an input sentence of length n, the model applies n/2 random swaps between consecutive words and trains the denoising-based U-NMT model (Artetxe, Labaka, and Agirre 2018). Though effective, applying denoising strategy on every sentence in the training data leads to uncertainty in the model thereby, limiting the benefits from the denoising-based U-NMT model. In this paper, we propose a simple fine-tuning strategy where we fine-tune the trained denoising-based U-NMT system without the de-noising strategy. The input sentences are presented as is i.e., without any shuffling noise added. We observe significant improvements in translation performance on many language pairs from our fine-tuning strategy. Our analysis reveals that our proposed models lead to increase in higher n-gram BLEU score compared to the denoising U-NMT models. 1 Introduction Unsupervised Neural Machine Translation (U-NMT) systems (Lample et al. 2018; Artetxe, Labaka, and Agirre 2018; 2019; Wu, Wang, and Wang 2019) typically train an encoder-decoder model for machine translation task using the monolingual data available in the two languages (l 1, l 2). The model proposed by Artetxe, Labaka, and Agirre 2018 consists of a shared encoder and language specific decoders.

Multi-path Learning for Object Pose Estimation Across Domains Machine Learning

We introduce a scalable approach for object pose estimation trained on simulated RGB views of multiple 3D models together. We learn an encoding of object views that does not only describe the orientation of all objects seen during training, but can also relate views of untrained objects. Our single-encoder-multi-decoder network is trained using a technique we denote "multi-path learning": While the encoder is shared by all objects, each decoder only reconstructs views of a single object. Consequently, views of different instances do not need to be separated in the latent space and can share common features. The resulting encoder generalizes well from synthetic to real data and across various instances, categories, model types and datasets. We systematically investigate the learned encodings, their generalization capabilities and iterative refinement strategies on the ModelNet40 and T-LESS dataset. On T-LESS, we achieve state-of-the-art results with our 6D Object Detection pipeline, both in the RGB and depth domain, outperforming learning-free pipelines at much lower runtimes.

Emphasizing 3D Properties in Recurrent Multi-View Aggregation for 3D Shape Retrieval

AAAI Conferences

Multi-view based shape descriptors have achieved impressive performance for 3D shape retrieval. The core of view-based methods is to interpret 3D structures through 2D observations. However, most existing methods pay more attention to discriminative models and none of them necessarily incorporate the 3D properties of the objects. To resolve this problem, we propose an encoder-decoder recurrent feature aggregation network (ERFA-Net) to emphasize the 3D properties of 3D shapes in multi-view features aggregation. In our network, a view sequence of the shape is trained to encode a discriminative shape embedding and estimate unseen rendered views of any viewpoints. This generation task gives an effective supervision which makes the network exploit 3D properties of shapes through various 2D images. During feature aggregation, a discriminative feature representation across multiple views is effectively exploited based on LSTM network. The proposed 3D representation has following advantages against other state-of-the-art: 1) it performs robust discrimination under the existence of noise such as view missing and occlusion, because of the improvement brought by 3D properties. 2) it has strong generative capabilities, which is useful for various 3D shape tasks. We evaluate ERFA-Net on two popular 3D shape datasets, ModelNet and ShapeNetCore55, and ERFA-Net outperforms the state-of-the-art methods significantly. Extensive experiments show the effectiveness and robustness of the proposed 3D representation.

YUVMultiNet: Real-time YUV multi-task CNN for autonomous driving Artificial Intelligence

In this paper, we propose a multi-task convolutional neural network (CNN) architecture optimized for a low power automotive grade SoC. We introduce a network based on a unified architecture where the encoder is shared among the two tasks namely detection and segmentation. The pro-posed network runs at 25FPS for 1280x800 resolution. We briefly discuss the methods used to optimize the network architecture such as using native YUV image directly, optimization of layers & feature maps and applying quantization. We also focus on memory bandwidth in our design as convolutions are data intensives and most SOCs are bandwidth bottlenecked. We then demonstrate the efficiency of our proposed network for a dedicated CNN accelerators presenting the key performance indicators (KPI) for the detection and segmentation tasks obtained from the hardware execution and the corresponding run-time.

Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System Artificial Intelligence

Modern driver assistance systems rely on a wide range of sensors (RADAR, LIDAR, ultrasound and cameras) for scene understanding and prediction. These sensors are typically used for detecting traffic participants and scene elements required for navigation. In this paper we argue that relying on camera based systems, specifically Around View Monitoring (AVM) system has great potential to achieve these goals in both parking and driving modes with decreased costs. The contributions of this paper are as follows: we present a new end-to-end solution for delimiting the safe drivable area for each frame by means of identifying the closest obstacle in each direction from the driving vehicle, we use this approach to calculate the distance to the nearest obstacles and we incorporate it into a unified end-to-end architecture capable of joint object detection, curb detection and safe drivable area detection. Furthermore, we describe the family of networks for both a high accuracy solution and a low complexity solution. We also introduce further augmentation of the base architecture with 3D object detection.