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Found 11 papers, 6 papers with code
Object Remover Performance Evaluation Methods using Class-wise Object Removal Images
In this letter, to validate the current evaluation methods cannot properly evaluate the performance of an object remover, we create a dataset with object removal ground truth and compare the evaluations made by the current methods using original images to those utilizing object removal ground truth images.
AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal
We design the judge module to quantitatively estimate the quality of the object removal results.
Explainable Artificial Intelligence (XAI)
Image Inpainting
+2
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal performances.
SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Network
Monocular depth estimation plays a critical role in various computer vision and robotics applications such as localization, mapping, and 3D object detection.
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model
Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements.
Ranked #154 on
3D Human Pose Estimation
on Human3.6M
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training.
FS-NCSR: Increasing Diversity of the Super-Resolution Space via Frequency Separation and Noise-Conditioned Normalizing Flow
Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images.
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss
Deep neural networks have been widely studied in autonomous driving applications such as semantic segmentation or depth estimation.
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement Learning
Previous studies on image classification have mainly focused on the performance of the networks, not on real-time operation or model compression.
Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss
In this paper, we show that existing self-supervised methods do not perform well on depth estimation and propose a gradient-based self-supervised learning algorithm with momentum contrastive loss to help ConvNets extract the geometric information with unlabeled images.
Detection-Aware Trajectory Generation for a Drone Cinematographer
The proposed method actively guides the motion of a cinematographer drone so that the color of a target is well-distinguished against the colors of the background in the view of the drone.
