Search Results for author:
Found 17 papers, 8 papers with code
Latent Inversion with Timestep-aware Sampling for Training-free Non-rigid Editing
In this work, we propose a training-free approach for non-rigid editing with Stable Diffusion, aimed at improving the identity preservation quality without compromising editability.
CLIP Can Understand Depth
Recent studies on generalizing CLIP for monocular depth estimation reveal that CLIP pre-trained on web-crawled data is inefficient for deriving proper similarities between image patches and depth-related prompts.
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation
Then, we propose a semantic mining module that takes the object masks to refine the pseudo labels in the target domain.
ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Open compound domain adaptation (OCDA) considers the target domain as the compound of multiple unknown homogeneous subdomains.
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation
Experiments on KITTI, Cityscapes, and Waymo Open Dataset demonstrate the relevance of our approach and show that our method outperforms state-of-the-art algorithms for the tasks of self-supervised monocular depth estimation, object motion segmentation, monocular scene flow estimation, and visual odometry.
Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation
Volumetric deep learning approach towards stereo matching aggregates a cost volume computed from input left and right images using 3D convolutions.
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks.
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
Ranked #2 on
Monocular Depth Estimation
on Cityscapes
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
Ranked #2 on
Domain Adaptation
on Synscapes-to-Cityscapes
Instance-wise Depth and Motion Learning from Monocular Videos
We present an end-to-end joint training framework that explicitly models 6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular camera setup without supervision.
Learning Residual Flow as Dynamic Motion from Stereo Videos
Based on rigid projective geometry, the estimated stereo depth is used to guide the camera motion estimation, and the depth and camera motion are used to guide the residual flow estimation.
Visuomotor Understanding for Representation Learning of Driving Scenes
We postulate that success on this task requires the network to learn semantic and geometric knowledge in the ego-centric view.
Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
We take an approach to learn a generalizable embedding space for novel tasks.
Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Recent advances in visual recognition show overarching success by virtue of large amounts of supervised data.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on
Lane Detection
on Caltech Lanes Washington
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN).
Ranked #73 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
