Search Results for author:
Found 16 papers, 7 papers with code
Online Overexposed Pixels Hallucination in Videos with Adaptive Reference Frame Selection
We present a learning-based system to reduce these artifacts without resorting to complex acquisition mechanisms like alternating exposures or costly processing that are typical of high dynamic range (HDR) imaging.
The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks
Many defenses against adversarial attacks (\eg robust classifiers, randomization, or image purification) use countermeasures put to work only after the attack has been crafted.
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
We first discover and analyze a counter-intuitive phenomenon: action selection through TS and a pre-trained value function often leads to lower performance compared to the original pre-trained agent, even when having access to the exact state and reward in future steps.
Noise-Aware Video Saliency Prediction
We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene.
Robust Vision-Based Cheat Detection in Competitive Gaming
Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems.
How to Close Sim-Real Gap? Transfer with Segmentation!
Combining the control policy learned from simulation with the perception model, we achieve an impressive $\bf{88\%}$ success rate in grasping a tiny sphere with a real robot.
Robotics
Accelerating Reinforcement Learning through GPU Atari Emulation
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms.
Importance Estimation for Neural Network Pruning
On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0. 02% in the top-1 accuracy on ImageNet.
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Training intelligent agents through reinforcement learning is a notoriously unstable procedure.
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by time-of-flight cameras.
On Nearest Neighbors in Non Local Means Denoising
To denoise a reference patch, the Non-Local-Means denoising filter processes a set of neighbor patches.
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks.
Learning Adaptive Parameter Tuning for Image Processing
The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content.
Robust Model-Based 3D Head Pose Estimation
We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera.
Loss Functions for Neural Networks for Image Processing
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems.
