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Found 11 papers, 8 papers with code
View Selection for 3D Captioning via Diffusion Ranking
Scalable annotation approaches are crucial for constructing extensive 3D-text datasets, facilitating a broader range of applications.
Scalable 3D Captioning with Pretrained Models
We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects.
Fine-grained Text Style Transfer with Diffusion-Based Language Models
Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain.
Multimodal Subtask Graph Generation from Instructional Videos
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program
On Text2Shape, ShapeGlot, ABO, Genre, and Program Synthetic datasets, Neural Shape Compiler shows strengths in $\textit{Text}$ $\Longrightarrow$ $\textit{Point Cloud}$, $\textit{Point Cloud}$ $\Longrightarrow$ $\textit{Text}$, $\textit{Point Cloud}$ $\Longrightarrow$ $\textit{Program}$, and Point Cloud Completion tasks.
RANDOM MASK: Towards Robust Convolutional Neural Networks
We next investigate the adversarial examples which 'fool' a CNN with Random Mask.
Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories
We address the problem of discovering 3D parts for objects in unseen categories.
Defective Convolutional Networks
Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i. e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly.
Defective Convolutional Layers Learn Robust CNNs
We first show that predictions made by the defective CNN are less dependent on textural information, but more on shape information, and further find that adversarial examples generated by the defective CNN appear to have semantic shapes.
Few-Shot Learning with Global Class Representations
In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples.
Learning to Navigate for Fine-grained Classification
In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher.
Ranked #42 on
Fine-Grained Image Classification
on FGVC Aircraft
