Search Results for author:
Found 26 papers, 10 papers with code
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives
Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for the dataset with a small scale.
Primitive-based Shape Abstraction via Nonparametric Bayesian Inference
In this paper, we propose a novel non-parametric Bayesian statistical method to infer an abstraction, consisting of an unknown number of geometric primitives, from a point cloud.
Global-Aware Registration of Less-Overlap RGB-D Scans
We propose a novel method of registering less-overlap RGB-D scans.
Maintaining Reasoning Consistency in Compositional Visual Question Answering
Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions.
Robust and Accurate Superquadric Recovery: a Probabilistic Approach
Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters.
Personalized Response Generation via Generative Split Memory Network
Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated.
GroupLink: An End-to-end Multitask Method for Word Grouping and Relation Extraction in Form Understanding
Forms are a common type of document in real life and carry rich information through textual contents and the organizational structure.
A Decomposition Model for Stereo Matching
In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases.
A Hyperbolic-to-Hyperbolic Graph Convolutional Network
Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation.
Curvature Generation in Curved Spaces for Few-Shot Learning
Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples.
SG-Net: Syntax Guided Transformer for Language Representation
In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Based on it, we formulate a hierarchical learning problem for 3D point cloud segmentation and propose a measurement evaluating consistency across various hierarchies.
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching
In this paper, we present a content-aware inter-scale cost aggregation method that adaptively aggregates and upsamples the cost volume from coarse-scale to fine-scale by learning dynamic filter weights according to the content of the left and right views on the two scales.
Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial Attacks
Armed with the Thompson Sampling, we develop a black-box attack with success rate over 95% on ModelNet40 data set.
Semantics-aware BERT for Language Understanding
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks.
Ranked #4 on
Natural Language Inference
on SNLI
DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question.
SG-Net: Syntax-Guided Machine Reading Comprehension
In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Ranked #5 on
Question Answering
on SQuAD2.0 dev
Stitching Videos from a Fisheye Lens Camera and a Wide-Angle Lens Camera for Telepresence Robots
In this paper, we propose to stitch videos from the FF-camera with a wide-angle lens and the DF-camera with a fisheye lens for telepresence robots.
Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Ranked #3 on
Question Answering
on RACE
Auto-Retoucher(ART) - A framework for Background Replacement and Image Editing
Replacing the background and simultaneously adjusting foreground objects is a challenging task in image editing.
Explicit Contextual Semantics for Text Comprehension
Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task.
Ranked #7 on
Natural Language Inference
on SNLI
Machine Reading Comprehension
Natural Language Understanding
+2
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals.
Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds
To this end, several new layers are introduced in our network, including a nonlinear kernel aggregation layer, an SPD matrix transformation layer, and a vectorization layer.
HOPE: Hierarchical Object Prototype Encoding for Efficient Object Instance Search in Videos
This paper tackles the problem of efficient and effective object instance search in videos.
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking.
