Search Results for author:
Found 26 papers, 7 papers with code
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
A dynamic treatment regime (DTR) consists of a sequence of decision rules, one per stage of intervention, that dictates how to determine the treatment assignment to patients based on evolving treatments and covariates' history.
Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models
Large-scale vision-language models have demonstrated impressive skill in handling tasks that involve both areas.
EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
News image captioning requires model to generate an informative caption rich in entities, with the news image and the associated news article.
DeformToon3D: Deformable 3D Toonification from Neural Radiance Fields
In this paper, we address the challenging problem of 3D toonification, which involves transferring the style of an artistic domain onto a target 3D face with stylized geometry and texture.
Variational Relational Point Completion Network for Robust 3D Classification
Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details.
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Besides, we devise hierarchical guidance and patch-based methods, enabling the GDP to generate images of arbitrary resolutions.
DeformToon3D: Deformable Neural Radiance Fields for 3D Toonification
In this paper, we address the challenging problem of 3D toonification, which involves transferring the style of an artistic domain onto a target 3D face with stylized geometry and texture.
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
This paper studies the problem of learning the shape given in the form of point clouds by inverse sketch-and-extrude.
CARNet:Compression Artifact Reduction for Point Cloud Attribute
A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts.
Sequential Causal Imitation Learning with Unobserved Confounders
"Monkey see monkey do" is an age-old adage, referring to na\"ive imitation without a deep understanding of a system's underlying mechanics.
Causal Imitation Learning with Unobserved Confounders
One of the common ways children learn is by mimicking adults.
Monocular 3D Object Reconstruction with GAN Inversion
Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation.
Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completion
We adopt DCD to evaluate the point cloud completion task, where experimental results show that DCD pays attention to both the overall structure and local geometric details and provides a more reliable evaluation even when CD and EMD contradict each other.
Partial Counterfactual Identification from Observational and Experimental Data
This paper investigates the problem of bounding counterfactual queries from an arbitrary collection of observational and experimental distributions and qualitative knowledge about the underlying data-generating model represented in the form of a causal diagram.
MeshInversion: 3D textured mesh reconstruction with generative prior
Reconstruction is achieved by searching for a latent space in the 3D GAN that best resembles the target mesh in accordance with the single view observation.
CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing
Generating an interpretable and compact representation of 3D shapes from point clouds is an important and challenging problem.
Crafting Adversarial Examples for Neural Machine Translation
We first show the current NMT adversarial attacks may be improperly estimated by the commonly used mono-directional translation, and we propose to leverage the round-trip translation technique to build valid metrics for evaluating NMT adversarial attacks.
Partial Identification of Counterfactual Distributions
We show that all counterfactual distributions (over finite observed variables) in an arbitrary causal diagram could be generated by a special family of structural causal models (SCMs), compatible with the same causal diagram, where unobserved (exogenous) variables are discrete, taking values in a finite domain.
Unsupervised 3D Shape Completion through GAN Inversion
In contrast to previous fully supervised approaches, in this paper we present ShapeInversion, which introduces Generative Adversarial Network (GAN) inversion to shape completion for the first time.
Variational Relational Point Completion Network
In particular, we propose a dual-path architecture to enable principled probabilistic modeling across partial and complete clouds.
Ranked #2 on
Point Cloud Completion
on Completion3D
Leveraging Localization for Multi-camera Association
We present McAssoc, a deep learning approach to the as-sociation of detection bounding boxes in different views ofa multi-camera system.
MessyTable: Instance Association in Multiple Camera Views
We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views.
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
A dynamic treatment regime (DTR) consists of a sequence of decision rules, one per stage of intervention, that dictates how to determine the treatment assignment to patients based on evolving treatments and covariates' history.
Efficient Memory Management for GPU-based Deep Learning Systems
They are achieved by exploiting the iterative nature of the training algorithm of deep learning to derive the lifetime and read/write order of all variables.
Equality of Opportunity in Classification: A Causal Approach
The goal of this paper is to develop a principled approach to connect the statistical disparities characterized by the EO and the underlying, elusive, and frequently unobserved, causal mechanisms that generated such inequality.
