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Found 14 papers, 7 papers with code
Backpropagation-free Network for 3D Test-time Adaptation
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
ChatGPT-guided Semantics for Zero-shot Learning
Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT.
LumiNet: The Bright Side of Perceptual Knowledge Distillation
In knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models.
Ranked #1 on
Classification
on CIFAR-100
Continual Test-time Domain Adaptation via Dynamic Sample Selection
DSS consists of dynamic thresholding, positive learning, and negative learning processes.
Prompt-guided Scene Generation for 3D Zero-Shot Learning
To address this problem, we propose a prompt-guided 3D scene generation and supervision method that augments 3D data to learn the network better, exploring the complex interplay of seen and unseen objects.
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training.
Learning without Forgetting for 3D Point Cloud Objects
Experimenting on three 3D point cloud recognition backbones (PointNet, DGCNN, and PointConv) and synthetic (ModelNet40, ModelNet10) and real scanned (ScanObjectNN) datasets, we establish new baseline results on learning without forgetting for 3D data.
Zero-Shot Learning on 3D Point Cloud Objects and Beyond
Zero-shot learning, the task of learning to recognize new classes not seen during training, has received considerable attention in the case of 2D image classification.
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner.
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces
In this paper, we propose addressing this problem using a mixture of subspaces.
Transductive Zero-Shot Learning for 3D Point Cloud Classification
This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification.
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects
In this paper, we therefore propose a loss to specifically address the hubness problem.
Zero-shot Learning of 3D Point Cloud Objects
A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes.
3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds
This paper introduces the 3DCapsule, which is a 3D extension of the recently introduced Capsule concept that makes it applicable to unordered point sets.
