Search Results for author:
Found 19 papers, 5 papers with code
CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models
Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions.
Precipitation Nowcasting Using Diffusion Transformer with Causal Attention
Our model leverages Transformer and combines causal attention mechanisms to establish spatiotemporal queries between conditional information (causes) and forecast results (results).
Improving Agent Behaviors with RL Fine-tuning for Autonomous Driving
A major challenge in autonomous vehicle research is modeling agent behaviors, which has critical applications including constructing realistic and reliable simulations for off-board evaluation and forecasting traffic agents motion for onboard planning.
JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous Driving
We propose JFP, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories.
Indoor Smartphone SLAM with Learned Echoic Location Features
Indoor self-localization is a highly demanded system function for smartphones.
Sardino: Ultra-Fast Dynamic Ensemble for Secure Visual Sensing at Mobile Edge
This paper presents extensive evaluation of Sardino's performance in counteracting adversarial examples and applies it to build a real-time car-borne traffic sign recognition system.
Joint Forecasting of Panoptic Segmentations with Difference Attention
For this, panoptic segmentations have been studied as a compelling representation in recent work.
IntentNet: Learning to Predict Intention from Raw Sensor Data
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other traffic participants.
End-to-end Interpretable Neural Motion Planner
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users.
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
In this paper we propose 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.
PIXOR: Real-time 3D Object Detection from Point Clouds
Existing approaches are, however, expensive in computation due to high dimensionality of point clouds.
Ranked #8 on
Birds Eye View Object Detection
on KITTI Cars Hard
DeepRoadMapper: Extracting Road Topology From Aerial Images
In contrast, in this paper we propose an approach that directly estimates road topology from aerial images.
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
We study characteristics of receptive fields of units in deep convolutional networks.
TorontoCity: Seeing the World with a Million Eyes
In this paper we introduce the TorontoCity benchmark, which covers the full greater Toronto area (GTA) with 712. 5 $km^2$ of land, 8439 $km$ of road and around 400, 000 buildings.
Efficient Summarization with Read-Again and Copy Mechanism
Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word.
Efficient Deep Learning for Stereo Matching
In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation.
Exploiting Semantic Information and Deep Matching for Optical Flow
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving.
Latent Structured Active Learning
In this paper we present active learning algorithms in the context of structured prediction problems.
