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Found 17 papers, 8 papers with code
Generic 3D Diffusion Adapter Using Controlled Multi-View Editing
Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity.
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion
Recent advancements in open-world 3D object generation have been remarkable, with image-to-3D methods offering superior fine-grained control over their text-to-3D counterparts.
RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches
Generalization remains one of the most important desiderata for robust robot learning systems.
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI.
Automatic Error Analysis for Document-level Information Extraction
Document-level information extraction (IE) tasks have recently begun to be revisited in earnest using the end-to-end neural network techniques that have been successful on their sentence-level IE counterparts.
Multi-skill Mobile Manipulation for Object Rearrangement
To tackle the entire task, prior work chains multiple stationary manipulation skills with a point-goal navigation skill, which are learned individually on subtasks.
Deep Feedback Inverse Problem Solver
Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation.
Compositionally Generalizable 3D Structure Prediction
However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals
We study how to learn a policy with compositional generalizability.
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems.
Weakly-supervised 3D Shape Completion in the Wild
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.
Multi-view PointNet for 3D Scene Understanding
Fusion of 2D images and 3D point clouds is important because information from dense images can enhance sparse point clouds.
Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively.
Learning Region Features for Object Detection
While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely hand-crafted, featured by RoI pooling methods.
Relation Networks for Object Detection
Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era.
