Search Results for author:
Found 27 papers, 8 papers with code
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 #3 on
Vessel Detection
on Vessel detection Dateset
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 #9 on
Video Semantic Segmentation
on Cityscapes val
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 Indian Driving Dataset
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.
MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer
Developing generative models for interleaved image-text data has both research and practical value.
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In practice, it performs similarly to the Hungarian algorithm during inference.
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.
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.
Deep Rigid Instance Scene Flow
In this paper we tackle the problem of scene flow estimation in the context of self-driving.
Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors.
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.
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Particularly difficult is the prediction of human behavior.
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 #1000000000 on
Instance Segmentation
on Cityscapes test
(using extra training data)
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.
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.
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.
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.
Network Automatic Pruning: Start NAP and Take a Nap
This process is typically time-consuming and requires expert knowledge to achieve good results.
Cost-Efficient Online Hyperparameter Optimization
Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters.
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.
Learning Compact Representations for LiDAR Completion and Generation
We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost.
Rethinking Closed-loop Training for Autonomous Driving
Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v. s.
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Learning world models can teach an agent how the world works in an unsupervised manner.
UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost.
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers.
LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.
Towards Unsupervised Object Detection From LiDAR Point Clouds
In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes.
