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Found 22 papers, 9 papers with code
DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining
Few-shot semantic segmentation has gained increasing interest due to its generalization capability, i. e., segmenting pixels of novel classes requiring only a few annotated images.
EchoONE: Segmenting Multiple echocardiography Planes in One Model
Effective solution for such a multi-plane segmentation (MPS) problem is highly demanded for medical images, yet has not been well investigated.
Partitioning Message Passing for Graph Fraud Detection
Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks.
Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
Moreover, OPAL employs a two-stage training strategy, in which the pre-train is to generate soft interests from historical interactions under the guidance of orthogonal hyper-categories of micro-videos and the fine-tune is to reinforce the degree of disentanglement among the interests and learn the temporal evolution of each interest of each user.
Commute Graph Neural Networks
However, their application to directed graphs (digraphs) presents unique challenges, primarily due to the inherent asymmetry in node relationships.
Efficient Graph Similarity Computation with Alignment Regularization
In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference.
APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation
Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples.
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels.
Ranked #7 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Activating the Discriminability of Novel Classes for Few-shot Segmentation
To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.
Improving Micro-video Recommendation via Contrastive Multiple Interests
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.
Fully Self-Supervised Learning for Semantic Segmentation
To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.
Ranked #21 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
Graph Neural Networks with Feature and Structure Aware Random Walk
Graph Neural Networks (GNNs) have received increasing attention for representation learning in various machine learning tasks.
Learning Inner-Group Relations on Point Clouds
We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation.
Ranked #21 on
3D Point Cloud Classification
on ModelNet40
A Behavior-aware Graph Convolution Network Model for Video Recommendation
Interactions between users and videos are the major data source of performing video recommendation.
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences
Based on the continuity between slices/frames and the common spatial layout of organs across volumes/sequences, we introduced a novel bootstrap self-supervised representation learning method by leveraging the predictable possibility of neighboring slices.
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast
Not restricted by connectivity in the original graph, the generated views allow the model to enhance its expressive power with new and complementary perspectives from which to look at the relationship between nodes.
Mining Latent Classes for Few-shot Segmentation
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #47 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Ranked #23 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference
In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships.
Indoor Scene Structure Analysis for Single Image Depth Estimation
We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities.
