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Found 9 papers, 5 papers with code
ZeroShape: Regression-based Zero-shot Shape Reconstruction
In contrast, the traditional approach to this problem is regression-based, where deterministic models are trained to directly regress the object shape.
Low-shot Object Learning with Mutual Exclusivity Bias
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias (LSME), the first computational framing of mutual exclusivity bias, a phenomenon commonly observed in infants during word learning.
ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images.
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
A hallmark of the deep learning era for computer vision is the successful use of large-scale labeled datasets to train feature representations for tasks ranging from object recognition and semantic segmentation to optical flow estimation and novel view synthesis of 3D scenes.
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image.
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias
It is widely accepted that reasoning about object shape is important for object recognition.
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
Continual learning has been extensively studied for classification tasks with methods developed to primarily avoid catastrophic forgetting, a phenomenon where earlier learned concepts are forgotten at the expense of more recent samples.
3D Reconstruction of Novel Object Shapes from Single Images
This is challenging as it requires a model to learn a representation that can infer both the visible and occluded portions of any object using a limited training set.
Virtual organelle self-coding for fluorescence imaging via adversarial learning
A detailed comparative analysis is also conducted on the performance of the cGAN network between predicting fluorescence channels based on phase contrast or based on another fluorescence channel using human breast cancer MDA-MB-231 cell line as a test case.
