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Found 18 papers, 6 papers with code
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters.
Network Automatic Pruning: Start NAP and Take a Nap
This process is typically time-consuming and requires expert knowledge to achieve good results.
Self-Supervised Representation Learning from Flow Equivariance
Motivated by this ability, we present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes with many moving objects.
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.
Weakly-supervised 3D Shape Completion in the Wild
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.
LoCo: Local Contrastive Representation Learning
Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and is not biologically plausible.
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving.
PolyTransform: Deep Polygon Transformer for Instance Segmentation
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods.
Ranked #1 on
Instance Segmentation
on Cityscapes test
(using extra training data)
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Particularly difficult is the prediction of human behavior.
Learning to Remember from a Multi-Task Teacher
Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task.
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In practice, it performs similarly to the Hungarian algorithm during inference.
Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors.
Deep Rigid Instance Scene Flow
In this paper we tackle the problem of scene flow estimation in the context of self-driving.
UPSNet: A Unified Panoptic Segmentation Network
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
Ranked #3 on
Panoptic Segmentation
on KITTI Panoptic Segmentation
Inference in Probabilistic Graphical Models by Graph Neural Networks
Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.
Reviving and Improving Recurrent Back-Propagation
We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks.
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.
Ranked #182 on
Object Detection
on COCO test-dev
Deep Feature Flow for Video Recognition
Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable.
Ranked #8 on
Video Semantic Segmentation
on Cityscapes val
