14 citations · 60 across the 25 of their papers we have counts for
25 papers
PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation
Yifan Yin, Zhengtao Han, Shivam Aarya +6
Fine-grained robot manipulation, such as lifting and rotating a bottle to display the label on the cap, requires robust reasoning about object parts and their relationships with in…
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency
Feng Wang, Timing Yang, Yaodong Yu +7
In this work, we introduce the Adventurer series models where we treat images as sequences of patch tokens and employ uni-directional language models to learn visual representation…
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
Tom Fischer, Yaoyao Liu, Artur Jesslen +6
Different from human nature, it is still common practice today for vision tasks to train deep learning models only initially and on fixed datasets. A variety of approaches have rec…
Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering
Xingrui Wang, Wufei Ma, Angtian Wang +3
For vision-language models (VLMs), understanding the dynamic properties of objects and their interactions in 3D scenes from videos is crucial for effective reasoning about high-lev…
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski +4
3D object pose estimation is a challenging task. Previous works always require thousands of object images with annotated poses for learning the 3D pose correspondence, which is lab…
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
Fengrui Tian, Yueqi Duan, Angtian Wang +2
In this work, we pioneer Semantic Flow, a neural semantic representation of dynamic scenes from monocular videos. In contrast to previous NeRF methods that reconstruct dynamic scen…
From Pixel to Cancer: Cellular Automata in Computed Tomography
Yuxiang Lai, Xiaoxi Chen, Angtian Wang +2
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in…
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
Yuanhao Cai, Yixun Liang, Jiahao Wang +5
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on N…
HISR: Hybrid Implicit Surface Representation for Photorealistic 3D Human Reconstruction
Angtian Wang, Yuanlu Xu, Nikolaos Sarafianos +4
Neural reconstruction and rendering strategies have demonstrated state-of-the-art performances due, in part, to their ability to preserve high level shape details. Existing approac…
Structure-Aware Sparse-View X-ray 3D Reconstruction
Yuanhao Cai, Jiahao Wang, Alan Yuille +2
X-ray, known for its ability to reveal internal structures of objects, is expected to provide richer information for 3D reconstruction than visible light. Yet, existing neural radi…
3D-Aware Neural Body Fitting for Occlusion Robust 3D Human Pose Estimation
Yi Zhang, Pengliang Ji, Angtian Wang +3
Regression-based methods for 3D human pose estimation directly predict the 3D pose parameters from a 2D image using deep networks. While achieving state-of-the-art performance on s…
Generating Images with 3D Annotations Using Diffusion Models
Wufei Ma, Qihao Liu, Jiahao Wang +11
Diffusion models have emerged as a powerful generative method, capable of producing stunning photo-realistic images from natural language descriptions. However, these models lack e…
Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis
Angtian Wang, Wufei Ma, Alan Yuille +1
Human vision demonstrates higher robustness than current AI algorithms under out-of-distribution scenarios. It has been conjectured such robustness benefits from performing analysi…
Robust Category-Level 3D Pose Estimation from Synthetic Data
Jiahao Yang, Wufei Ma, Angtian Wang +3
Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is…
NOVUM: Neural Object Volumes for Robust Object Classification
Artur Jesslen, Guofeng Zhang, Angtian Wang +3
Discriminative models for object classification typically learn image-based representations that do not capture the compositional and 3D nature of objects. In this work, we show th…
Benchmarking Robustness in Neural Radiance Fields
Chen Wang, Angtian Wang, Junbo Li +2
Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, curr…
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
Wufei Ma, Angtian Wang, Alan Yuille +1
We consider the problem of category-level 6D pose estimation from a single RGB image. Our approach represents an object category as a cuboid mesh and learns a generative model of t…
VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis
Angtian Wang, Peng Wang, Jian Sun +2
The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation…
OOD-CV: A Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images
Bingchen Zhao, Shaozuo Yu, Wufei Ma +6
Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on syntheti…
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose
Angtian Wang, Shenxiao Mei, Alan Yuille +1
We study the problem of learning to estimate the 3D object pose from a few labelled examples and a collection of unlabelled data. Our main contribution is a learning framework, neu…
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation
Angtian Wang, Adam Kortylewski, Alan Yuille
3D pose estimation is a challenging but important task in computer vision. In this work, we show that standard deep learning approaches to 3D pose estimation are not robust when ob…
CoKe: Localized Contrastive Learning for Robust Keypoint Detection
Yutong Bai, Angtian Wang, Adam Kortylewski +1
In this paper, we introduce a contrastive learning framework for keypoint detection (CoKe). Keypoint detection differs from other visual tasks where contrastive learning has been a…
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
Adam Kortylewski, Qing Liu, Angtian Wang +2
Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neur…
Robust Object Detection under Occlusion with Context-Aware CompositionalNets
Angtian Wang, Yihong Sun, Adam Kortylewski +1
Detecting partially occluded objects is a difficult task. Our experimental results show that deep learning approaches, such as Faster R-CNN, are not robust at object detection unde…