Search Results for author:
Found 10 papers, 4 papers with code
PosSAM: Panoptic Open-vocabulary Segment Anything
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework.
Ranked #1 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+2
Trends in Workplace Wearable Technologies and Connected-Worker Solutions for Next-Generation Occupational Safety, Health, and Productivity
Wearable devices enable constant monitoring of individual workers and the environment, whereas connected worker solutions provide contextual information and decision support.
ART-SS: An Adaptive Rejection Technique for Semi-Supervised restoration for adverse weather-affected images
Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images.
3SD: Self-Supervised Saliency Detection With No Labels
Our method generates and uses pseudo-ground truth labels for training.
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection
Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation.
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition
First, we learn generative features using the one-class data with a generative framework.
Deep Subspace Clustering with Data Augmentation
In particular, we introduce a temporal ensembling component to the objective function of DSC algorithms to enable the DSC networks to maintain consistent subspaces for random transformations in the input data.
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification
Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process.
Face Alignment by Local Deep Descriptor Regression
We also present a face alignment algorithm based on regression using these local descriptors.
