Search Results for author:
Found 12 papers, 9 papers with code
A Simple and Generalist Approach for Panoptic Segmentation
These contributions are: (i) a positional-embedding (PE) based loss for improved centroid regressions; (ii) Edge Distance Sampling (EDS) for the better separation of instance boundaries.
Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models
With recent advances in image and video diffusion models for content creation, a plethora of techniques have been proposed for customizing their generated content.
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting
Panoptic and instance segmentation networks are often trained with specialized object detection modules, complex loss functions, and ad-hoc post-processing steps to manage the permutation-invariance of the instance masks.
Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation
This paper presents MaskDistill: a novel framework for unsupervised semantic segmentation based on three key ideas.
Ranked #4 on
Unsupervised Semantic Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings.
Ranked #3 on
Unsupervised Semantic Segmentation
on ImageNet-S-50
SCAN: Learning to Classify Images without Labels
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
Ranked #4 on
Image Clustering
on ImageNet-200
Multi-Task Learning for Dense Prediction Tasks: A Survey
In this survey, we provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision, explicitly emphasizing on dense prediction tasks.
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video
In particular, we put three different types of depth estimation (supervised depth prediction, self-supervised depth prediction, and self-supervised depth completion) into a common framework.
Sparse and noisy LiDAR completion with RGB guidance anduncertainty
For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions.
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input.
Ranked #5 on
Depth Completion
on KITTI Depth Completion
End-to-end Lane Detection through Differentiable Least-Squares Fitting
The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance.
