Search Results for author:
Found 32 papers, 15 papers with code
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
We present a foundation model for zero-shot metric monocular depth estimation.
Enhancing Incremental Summarization with Structured Representations
Large language models (LLMs) often struggle with processing extensive input contexts, which can lead to redundant, inaccurate, or incoherent summaries.
SUMIE: A Synthetic Benchmark for Incremental Entity Summarization
No existing dataset adequately tests how well language models can incrementally update entity summaries - a crucial ability as these models rapidly advance.
STRUM-LLM: Attributed and Structured Contrastive Summarization
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages.
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks.
FaceStudio: Put Your Face Everywhere in Seconds
This study investigates identity-preserving image synthesis, an intriguing task in image generation that seeks to maintain a subject's identity while adding a personalized, stylistic touch.
An Augmentation Strategy for Visually Rich Documents
Many business workflows require extracting important fields from form-like documents (e. g. bank statements, bills of lading, purchase orders, etc.).
VRDU: A Benchmark for Visually-rich Document Understanding
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry.
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models
A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy.
#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs
COVID-19 has caused lasting damage to almost every domain in public health, society, and economy.
Clinical Named Entity Recognition using Contextualized Token Representations
To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.
NeRD: Neural 3D Reflection Symmetry Detector
Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks.
CREATe: Clinical Report Extraction and Annotation Technology
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
Simplified DOM Trees for Transferable Attribute Extraction from the Web
There has been a steady need to precisely extract structured knowledge from the web (i. e. HTML documents).
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers
Being able to infer 3D structures from 2D images with geometric principles, vanishing points have been a well-recognized concept in 3D vision research.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities
The optimization problems associated with training generative adversarial neural networks can be largely reduced to certain {\em non-monotone} variational inequality problems (VIPs), whereas existing convergence results are mostly based on monotone or strongly monotone assumptions.
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding.
Learning to Create Better Ads: Generation and Ranking Approaches for Ad Creative Refinement
Since major ad platforms typically run A/B tests for multiple advertisers in parallel, we explore the possibility of collaboratively learning ad creative refinement via A/B tests of multiple advertisers.
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
We present HoliCity, a city-scale 3D dataset with rich structural information.
``The Boating Store Had Its Best Sail Ever'': Pronunciation-attentive Contextualized Pun Recognition
In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.
Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image pixel-wise correspondence of the symmetry.
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition
In this paper, we propose Pronunciation-attentive Contextualized Pun Recognition (PCPR) to perceive human humor, detect if a sentence contains puns and locate them in the sentence.
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
There is a perennial need in the online advertising industry to refresh ad creatives, i. e., images and text used for enticing online users towards a brand.
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images.
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification
To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations.
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
In this paper, we propose a method to obtain a compact and accurate 3D wireframe representation from a single image by effectively exploiting global structural regularities.
End-to-End Wireframe Parsing
We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms.
Ranked #5 on
Line Segment Detection
on wireframe dataset
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image.
BandNet: A Neural Network-based, Multi-Instrument Beatles-Style MIDI Music Composition Machine
In the learning stage, a sequence of stylistically uniform, multiple-channel music samples was modeled by a RNN.
Learning Gender-Neutral Word Embeddings
Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications.
Computational Protein Design Using AND/OR Branch-and-Bound Search
The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design.
