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Found 16 papers, 13 papers with code
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects.
Safe Real-World Autonomous Driving by Learning to Predict and Plan with a Mixture of Experts
The code for training and testing our model on a public prediction dataset and the video of the road test are available at https://woven. mobi/safepathnet
CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization
The imitation learning of self-driving vehicle policies through behavioral cloning is often carried out in an open-loop fashion, ignoring the effect of actions to future states.
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem.
Ranked #21 on
Panoptic Segmentation
on COCO minival
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning
We address the problem of visually guided rearrangement planning with many movable objects, i. e., finding a sequence of actions to move a set of objects from an initial arrangement to a desired one, while relying on visual inputs coming from an RGB camera.
Exploring Weight Symmetry in Deep Neural Networks
We propose several ways to impose local symmetry in recurrent and convolutional neural networks, and show that our symmetry parameterizations satisfy universal approximation property for single hidden layer networks.
Compressing the Input for CNNs with the First-Order Scattering Transform
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN).
Scattering Networks for Hybrid Representation Learning
In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making progress towards constructing more interpretable CNNs.
DiracNets: Training Very Deep Neural Networks Without Skip-Connections
Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks.
Scaling the Scattering Transform: Deep Hybrid Networks
Combining scattering networks with a modern ResNet, we achieve a single-crop top 5 error of 11. 4% on imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing only 10 layers.
Ranked #73 on
Image Classification
on STL-10
Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
Attention plays a critical role in human visual experience.
Ranked #39 on
Knowledge Distillation
on ImageNet
Wide Residual Networks
Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance.
Ranked #12 on
Image Classification
on SVHN
A MultiPath Network for Object Detection
To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.
Ranked #104 on
Instance Segmentation
on COCO test-dev
A MRF Shape Prior for Facade Parsing With Occlusions
We present a new shape prior formalism for segmentation of rectified facade images.
Learning to Compare Image Patches via Convolutional Neural Networks
In this paper we show how to learn directly from image data (i. e., without resorting to manually-designed features) a general similarity function for comparing image patches, which is a task of fundamental importance for many computer vision problems.
