Search Results for author:
Found 11 papers, 4 papers with code
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning
For this reason, this paper explores a Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the use of existing pretrained models instead of training a model with source data, avoiding accessing source data.
PlaneRecTR: Unified Query Learning for 3D Plane Recovery from a Single View
3D plane recovery from a single image can usually be divided into several subtasks of plane detection, segmentation, parameter estimation and possibly depth estimation.
ROFusion: Efficient Object Detection using Hybrid Point-wise Radar-Optical Fusion
In this paper, we propose a hybrid point-wise Radar-Optical fusion approach for object detection in autonomous driving scenarios.
Unbiased Scene Graph Generation via Two-stage Causal Modeling
To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages.
Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey
Deep learning has been highly successful in computer vision with large amounts of labeled data, but struggles with limited labeled training data.
SSR-2D: Semantic 3D Scene Reconstruction from 2D Images
In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations.
Cross-Domain Few-Shot Classification via Inter-Source Stylization
The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary dataset, despite the differences between the domains of the two datasets.
ILabel: Interactive Neural Scene Labelling
ILabel's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation.
Bootstrapping Semantic Segmentation with Regional Contrast
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
In-Place Scene Labelling and Understanding with Implicit Scene Representation
Semantic labelling is highly correlated with geometry and radiance reconstruction, as scene entities with similar shape and appearance are more likely to come from similar classes.
SceneCode: Monocular Dense Semantic Reconstruction using Learned Encoded Scene Representations
Systems which incrementally create 3D semantic maps from image sequences must store and update representations of both geometry and semantic entities.
