19 citations · 22 across the 11 of their papers we have counts for
13 papers
MEBench: A Novel Benchmark for Understanding Mutual Exclusivity Bias in Vision-Language Models
Anh Thai, Stefan Stojanov, Zixuan Huang +2
This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during word learning. Unlike traditional M…
Leveraging Object Priors for Point Tracking
Bikram Boote, Anh Thai, Wenqi Jia +4
Point tracking is a fundamental problem in computer vision with numerous applications in AR and robotics. A common failure mode in long-term point tracking occurs when the predicte…
3x2: 3D Object Part Segmentation by 2D Semantic Correspondences
Anh Thai, Weiyao Wang, Hao Tang +3
3D object part segmentation is essential in computer vision applications. While substantial progress has been made in 2D object part segmentation, the 3D counterpart has received l…
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences
Bria Long, Robert Z. Sparks, Violet Xiang +9
Human children far exceed modern machine learning algorithms in their sample efficiency, achieving high performance in key domains with much less data than current models. This ''d…
ZeroShape: Regression-based Zero-shot Shape Reconstruction
Zixuan Huang, Stefan Stojanov, Anh Thai +2
We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets, but these models…
Low-shot Object Learning with Mutual Exclusivity Bias
Anh Thai, Ahmad Humayun, Stefan Stojanov +3
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 in…
ShapeClipper: Scalable 3D Shape Learning from Single-View Images via Geometric and CLIP-based Consistency
Zixuan Huang, Varun Jampani, Anh Thai +3
We present ShapeClipper, a novel method that reconstructs 3D object shapes from real-world single-view RGB images. Instead of relying on laborious 3D, multi-view or camera pose ann…
Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
Stefan Stojanov, Anh Thai, Zixuan Huang +1
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 recogni…
Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues
Zixuan Huang, Stefan Stojanov, Anh Thai +2
We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images…
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias
Stefan Stojanov, Anh Thai, James M. Rehg
It is widely accepted that reasoning about object shape is important for object recognition. However, the most powerful object recognition methods today do not explicitly make use…
The Surprising Positive Knowledge Transfer in Continual 3D Object Shape Reconstruction
Anh Thai, Stefan Stojanov, Zixuan Huang +2
Continual learning has been extensively studied for classification tasks with methods developed to primarily avoid catastrophic forgetting, a phenomenon where earlier learned conce…
3D Reconstruction of Novel Object Shapes from Single Images
Anh Thai, Stefan Stojanov, Vijay Upadhya +1
Accurately predicting the 3D shape of any arbitrary object in any pose from a single image is a key goal of computer vision research. This is challenging as it requires a model to…
Unsupervised 3D Pose Estimation with Geometric Self-Supervision
Ching-Hang Chen, Ambrish Tyagi, Amit Agrawal +4
We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data,…