← Search

Daniel Fu

4 accepted papers

2025

WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction

ICCV 2025poster

In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features (e.g. texture) of the shape from arbitrary view…

Cited by 0SourcePDFScholar
2022

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

ACL 2022findings

Entity retrieval—retrieving information about entity mentions in a query—is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions due to biases towards popular entities. Inco…

2020

Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods

ICML 2020poster

Weak supervision is a popular method for building machine learning models without relying on ground truth annotations. Instead, it generates probabilistic training labels by estimating the accuracies of multiple noisy labeling sources (e.g., heuristics, crowd workers). Existing approaches use latent…

2019

Multi-Resolution Weak Supervision for Sequential Data

NeurIPS 2019poster

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can…

Cited by 42SourcePDFScholar