Search Results for author:
Found 16 papers, 7 papers with code
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
Numerical experiments show that our trained models clearly outperform existing analysis operator learning approaches and are on par with state-of-the-art image denoising algorithms.
A bi-level view of inpainting - based image compression
Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression.
Revisiting loss-specific training of filter-based MRFs for image restoration
It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision.
A higher-order MRF based variational model for multiplicative noise reduction
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.
Accurate Optical Flow via Direct Cost Volume Processing
We present an optical flow estimation approach that operates on the full four-dimensional cost volume.
Events-to-Video: Bringing Modern Computer Vision to Event Cameras
Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events.
What Do Single-view 3D Reconstruction Networks Learn?
Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research.
Ranked #1 on
3D Reconstruction
on 300W
High Speed and High Dynamic Range Video with an Event Camera
In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors.
Ranked #3 on
Event-based Object Segmentation
on MVSEC-SEG
Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks.
Ranked #2 on
Depth Estimation
on eBDtheque
Deep Drone Acrobatics
In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.
Robotics
Vision Transformers for Dense Prediction
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks.
Ranked #12 on
Semantic Segmentation
on PASCAL Context
An Analysis of Super-Net Heuristics in Weight-Sharing NAS
Weight sharing promises to make neural architecture search (NAS) tractable even on commodity hardware.
Learning High-Speed Flight in the Wild
Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.
Language-driven Semantic Segmentation
We present LSeg, a novel model for language-driven semantic image segmentation.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Semantic segmentation models struggle to generalize in the presence of domain shift.
Monocular Visual-Inertial Depth Estimation
We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in inverse RMSE with dense scale alignment relative to performing just global alignment alone.
