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Learning to Reweight Examples for Robust Deep Learning
TLDR
This work proposes a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions that can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.
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PIXOR: Real-time 3D Object Detection from Point Clouds
TLDR
PIXOR is proposed, a proposal-free, single-stage detector that outputs oriented 3D object estimates decoded from pixel-wise neural network predictions that surpasses other state-of-the-art methods notably in terms of Average Precision (AP), while still runs at 10 FPS.
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Deep Continuous Fusion for Multi-sensor 3D Object Detection
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable…
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Learning Lane Graph Representations for Motion Forecasting
TLDR
A motion forecasting model that exploits a novel structured map representation as well as actor-map interactions to predict accurate and realistic multi-modal trajectories and significantly outperforms the state-of-the-art on the large scale Argoverse motion forecasting benchmark.
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Multi-Task Multi-Sensor Fusion for 3D Object Detection
TLDR
An end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion is presented that leads the KITTI benchmark on 2D, 3D and bird's eye view object detection, while being real-time.
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Aggregate channel features for multi-view face detection
TLDR
Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB test-sets, while runs at 42 FPS on VGA images.
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Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
TLDR
A novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor is proposed, which is very efficient in terms of both memory and computation.
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Convolutional Channel Features
TLDR
Convolutional Channel Features (CCF) serves as a good way of tailoring pre-trained CNN models to diverse tasks without fine-tuning the whole network to each task by achieving state-of-the-art performances in pedestrian detection, face detection, edge detection and object proposal generation.
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T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos
TLDR
A deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos is proposed, called T-CNN.
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HDNET: Exploiting HD Maps for 3D Object Detection
TLDR
It is shown that High-Definition maps provide strong priors that can boost the performance and robustness of modern 3D object detectors and a single stage detector is designed that extracts geometric and semantic features from the HD maps.
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