Search Results for author:
Found 5 papers, 2 papers with code
AdaIR: Exploiting Underlying Similarities of Image Restoration Tasks with Adapters
Towards this goal, we propose AdaIR, a novel framework that enables low storage cost and efficient training without sacrificing performance.
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.
Ranked #3 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3)
Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions.
Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions.
Pixel-Wise Prediction based Visual Odometry via Uncertainty Estimation
This paper introduces pixel-wise prediction based visual odometry (PWVO), which is a dense prediction task that evaluates the values of translation and rotation for every pixel in its input observations.
