Search Results for author:
Found 10 papers, 5 papers with code
A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention
This module uses the cross-slice attention mechanism to effectively capture 3D spatial information by learning long-range dependencies between the center slice (for segmentation) and its neighboring slices.
Contrastive Mean-Shift Learning for Generalized Category Discovery
We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown.
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation
For this mixed setup, we propose to improve the pseudo-labels using a pseudo-label enhancer that was trained using the available ground-truth pixel-level labels.
Few-shot Metric Learning: Online Adaptation of Embedding for Retrieval
Metric learning aims to build a distance metric typically by learning an effective embedding function that maps similar objects into nearby points in its embedding space.
Integrative Few-Shot Learning for Classification and Segmentation
We introduce the integrative task of few-shot classification and segmentation (FS-CS) that aims to both classify and segment target objects in a query image when the target classes are given with a few examples.
Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain.
Relational Embedding for Few-Shot Classification
We propose to address the problem of few-shot classification by meta-learning "what to observe" and "where to attend" in a relational perspective.
Ranked #15 on
Few-Shot Image Classification
on CUB 200 5-way 5-shot
Hypercorrelation Squeeze for Few-Shot Segmentation
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
Ranked #13 on
Few-Shot Semantic Segmentation
on FSS-1000 (5-shot)
Pair-based Self-Distillation for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain.
Hypercorrelation Squeeze for Few-Shot Segmenation
Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class.
