Search Results for author:
Found 137 papers, 70 papers with code
RobustFusion: Human Volumetric Capture with Data-driven Visual Cues using a RGBD Camera
To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios.
Effective Fine-Tuning Methods for Cross-lingual Adaptation
In this work, we propose a novel fine-tuning method based on co-training that aims to learn more generalized semantic equivalences as a complementary to multilingual language modeling using the unlabeled data in the target language.
An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity.
MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors
To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture.
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios.
YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5
Additionally, we have incorporated a global attention mechanism into the backbone network.
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning
Designing distributed filtering circuits (DFCs) is complex and time-consuming, with the circuit performance relying heavily on the expertise and experience of electronics engineers.
Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept
The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform.
ARKS: Active Retrieval in Knowledge Soup for Code Generation
Recently the retrieval-augmented generation (RAG) paradigm has raised much attention for its potential in incorporating external knowledge into large language models (LLMs) without further training.
Generative Representational Instruction Tuning
Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss.
Momentum Approximation in Asynchronous Private Federated Learning
Asynchronous protocols have been shown to improve the scalability of federated learning (FL) with a massive number of clients.
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement
Autonomous interaction with the computer has been a longstanding challenge with great potential, and the recent proliferation of large language models (LLMs) has markedly accelerated progress in building digital agents.
Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning
To fill these gaps, a novel contextual meta graph reinforcement learning (Meta-GRL) for a highly generalized multi-stage optimal dispatch policy is proposed.
Fluctuation-based Adaptive Structured Pruning for Large Language Models
Retraining-free is important for LLMs' pruning methods.
DisControlFace: Disentangled Control for Personalized Facial Image Editing
Our DisControlNet can perform robust editing on any facial image through training on large-scale 2D in-the-wild portraits and also supports low-cost fine-tuning with few additional images to further learn diverse personalized priors of a specific person.
Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0
In contrast, this paper envisions a novel concept of an Internet of Federated Digital Twins (IoFDT) that holistically integrates heterogeneous and physically separated DTs representing different Society 5. 0 services within a single framework and system.
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning
Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure.
OpenAgents: An Open Platform for Language Agents in the Wild
Language agents show potential in being capable of utilizing natural language for varied and intricate tasks in diverse environments, particularly when built upon large language models (LLMs).
Lemur: Harmonizing Natural Language and Code for Language Agents
We introduce Lemur and Lemur-Chat, openly accessible language models optimized for both natural language and coding capabilities to serve as the backbone of versatile language agents.
PARF: Primitive-Aware Radiance Fusion for Indoor Scene Novel View Synthesis
This paper proposes a method for fast scene radiance field reconstruction with strong novel view synthesis performance and convenient scene editing functionality.
Text2Reward: Automated Dense Reward Function Generation for Reinforcement Learning
Unlike inverse RL and recent work that uses LLMs to write sparse reward codes, Text2Reward produces interpretable, free-form dense reward codes that cover a wide range of tasks, utilize existing packages, and allow iterative refinement with human feedback.
Dynamic Spectrum Mixer for Visual Recognition
Recently, MLP-based vision backbones have achieved promising performance in several visual recognition tasks.
ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction
This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation.
AutoDroid: LLM-powered Task Automation in Android
Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones.
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons.
Fast Segment Anything
In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance.
Ranked #4 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset.
Coneheads: Hierarchy Aware Attention
These networks rely heavily on the dot product attention operator, which computes the similarity between two points by taking their inner product.
Shadow Cones: A Generalized Framework for Partial Order Embeddings
Specifically, we model partial orders as subset relations between shadows formed by a light source and opaque objects in hyperbolic space.
Generating Data for Symbolic Language with Large Language Models
However, such an approach has primarily been applied to natural language tasks and has not yet been explored for symbolic language tasks with complex structured outputs (e. g., semantic parsing and code generation).
Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression
During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN).
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods.
Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
Given multi-view low-resolution images, Super-NeRF constructs a consistency-controlling super-resolution module to generate view-consistent high-resolution details for NeRF.
Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting
Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications.
The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities
We then articulate how these experiences bring forth interactions between diverse metaverse constituents, namely, a) humans and avatars and b) connected intelligence systems and their digital twins (DTs).
Inpaint Anything: Segment Anything Meets Image Inpainting
We are also very willing to help everyone share and promote new projects based on our Inpaint Anything (IA).
Hi Sheldon! Creating Deep Personalized Characters from TV Shows
To support this novel task, we further collect a character centric multimodal dialogue dataset, named Deep Personalized Character Dataset (DPCD), from TV shows.
ZBS: Zero-shot Background Subtraction via Instance-level Background Modeling and Foreground Selection
However, previous unsupervised deep learning BGS algorithms perform poorly in sophisticated scenarios such as shadows or night lights, and they cannot detect objects outside the pre-defined categories.
TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge
This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge.
Automated Self-Supervised Learning for Recommendation
This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.
Compositional Exemplars for In-context Learning
The performance of ICL is highly dominated by the quality of the selected in-context examples.
Batch Prompting: Efficient Inference with Large Language Model APIs
We extensively validate the effectiveness of batch prompting on ten datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) reduces the LLM (Codex) inference token and time costs while achieving better or comparable performance.
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
Realization Scheme for Visual Cryptography with Computer-generated Holograms
This method can expand the application range of visual cryptography and further increase the security of visual cryptography.
Coder Reviewer Reranking for Code Generation
Sampling diverse programs from a code language model and reranking with model likelihood is a popular method for code generation but it is prone to preferring degenerate solutions.
Ranked #22 on
Code Generation
on MBPP
Task Residual for Tuning Vision-Language Models
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts.
DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation
We introduce DS-1000, a code generation benchmark with a thousand data science problems spanning seven Python libraries, such as NumPy and Pandas.
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
To improve the quality of dataset synthesis, we propose a progressive zero-shot dataset generation framework, ProGen, which leverages the feedback from the task-specific model to guide the generation of new training data via in-context examples.
Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play
We first design a SQL-to-text model conditioned on a sampled goal query, which represents a user's intent, that then converses with a text-to-SQL semantic parser to generate new interactions.
Binding Language Models in Symbolic Languages
We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.
Ranked #4 on
Table-based Fact Verification
on TabFact
NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries
We develop NL2INTERFACE to explore the potential of generating usable interactive multi-visualization interfaces from natural language queries.
Selective Annotation Makes Language Models Better Few-Shot Learners
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
FOLIO: Natural Language Reasoning with First-Order Logic
We present FOLIO, a human-annotated, open-domain, and logically complex and diverse dataset for reasoning in natural language (NL), equipped with first order logic (FOL) annotations.
MCTensor: A High-Precision Deep Learning Library with Multi-Component Floating-Point
In this paper, we introduce MCTensor, a library based on PyTorch for providing general-purpose and high-precision arithmetic for DL training.
Geometry-aware Single-image Full-body Human Relighting
For the relighting, we introduce a ray tracing-based per-pixel lighting representation that explicitly models high-frequency shadows and propose a learning-based shading refinement module to restore realistic shadows (including hard cast shadows) from the ray-traced shading maps.
Design and Analysis of Robust Resilient Diffusion over Multi-Task Networks Against Byzantine Attacks
This paper studies distributed diffusion adaptation over clustered multi-task networks in the presence of impulsive interferences and Byzantine attacks.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
GIMO: Gaze-Informed Human Motion Prediction in Context
We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures.
ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided Sampling
Existing state-of-the-art novel view synthesis methods rely on either fairly accurate 3D geometry estimation or sampling of the entire space for neural volumetric rendering, which limit the overall efficiency.
Structured Local Radiance Fields for Human Avatar Modeling
These local radiance fields not only leverage the flexibility of implicit representation in shape and appearance modeling, but also factorize cloth deformations into skeleton motions, node residual translations and the dynamic detail variations inside each individual radiance field.
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc.
Interacting Attention Graph for Single Image Two-Hand Reconstruction
To solve occlusion and interaction challenges of two-hand reconstruction, we introduce two novel attention based modules in each upsampling step of the original GCN.
Ranked #4 on
3D Interacting Hand Pose Estimation
on InterHand2.6M
3D Interacting Hand Pose Estimation
Vocal Bursts Valence Prediction
In-Context Learning for Few-Shot Dialogue State Tracking
In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates.
OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation
Light field disparity estimation is an essential task in computer vision with various applications.
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs).
Random Laplacian Features for Learning with Hyperbolic Space
Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree- and graph-structured data, upon which various hyperbolic networks have been developed.
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Using representation theory, we characterize which similarity matrices can be "expressed" by finite group VSA hypervectors, and we show how these VSAs can be constructed.
Mask-based Latent Reconstruction for Reinforcement Learning
For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance.
HDhuman: High-quality Human Novel-view Rendering from Sparse Views
To this end, we propose HDhuman, which uses a human reconstruction network with a pixel-aligned spatial transformer and a rendering network with geometry-guided pixel-wise feature integration to achieve high-quality human reconstruction and rendering.
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases.
Ranked #1 on
Task-Oriented Dialogue Systems
on KVRET
HVTR: Hybrid Volumetric-Textural Rendering for Human Avatars
To handle complicated motions (e. g., self-occlusions), we then leverage the encoded information on the UV manifold to construct a 3D volumetric representation based on a dynamic pose-conditioned neural radiance field.
Representing Hyperbolic Space Accurately using Multi-Component Floats
Hyperbolic space is particularly useful for embedding data with hierarchical structure; however, representing hyperbolic space with ordinary floating-point numbers greatly affects the performance due to its \emph{ineluctable} numerical errors.
DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization
Transformer-based models have achieved state-of-the-art performance on short-input summarization.
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions
Existing text-to-SQL research only considers complete questions as the input, but lay-users might strive to formulate a complete question.
An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.
SummerTime: Text Summarization Toolkit for Non-experts
We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs.
Lightweight Multi-person Total Motion Capture Using Sparse Multi-view Cameras
Overall, we propose the first light-weight total capture system and achieves fast, robust and accurate multi-person total motion capture performance.
Ranked #2 on
3D Multi-Person Pose Estimation
on Shelf
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL.
PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning
In this work, we propose a novel method, dubbed PlayVirtual, which augments cycle-consistent virtual trajectories to enhance the data efficiency for RL feature representation learning.
Continuous Control (100k environment steps)
Continuous Control (500k environment steps)
+3
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering
We introduce DoubleField, a novel framework combining the merits of both surface field and radiance field for high-fidelity human reconstruction and rendering.
Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors
Human volumetric capture is a long-standing topic in computer vision and computer graphics.
DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras
We propose DeepMultiCap, a novel method for multi-person performance capture using sparse multi-view cameras.
QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed.
INTERSPEECH 2021 ConferencingSpeech Challenge: Towards Far-field Multi-Channel Speech Enhancement for Video Conferencing
The ConferencingSpeech 2021 challenge is proposed to stimulate research on far-field multi-channel speech enhancement for video conferencing.
POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture
By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions.
Local Patch AutoAugment with Multi-Agent Collaboration
We formulate it as a multi-agent reinforcement learning (MARL) problem, where each agent learns an augmentation policy for each patch based on its content together with the semantics of the whole image.
Nematicity Arising from a Chiral Superconducting Ground State in Magic-Angle Twisted Bilayer Graphene under In-Plane Magnetic Fields
Recent measurements of the resistivity in magic-angle twisted bilayer graphene near the superconducting transition temperature show two-fold anisotropy, or nematicity, when changing the direction of an in-plane magnetic field [Cao \textit{et al.}, Science \textbf{372}, 264 (2021)].
Superconductivity
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing
Conversational Semantic Parsing (CSP) is the task of converting a sequence of natural language queries to formal language (e. g., SQL, SPARQL) that can be executed against a structured ontology (e. g. databases, knowledge bases).
Ranked #3 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i. e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-resolution (LR) images due to the complicated realistic degradations.
Spin-Wave Doppler Shift by Magnon Drag in Magnetic Insulators
The Doppler shift of the quasiparticle dispersion by charge currents is responsible for the critical supercurrents in superconductors and instabilities of the magnetic ground state of metallic ferromagnets.
Mesoscale and Nanoscale Physics
Deep Implicit Templates for 3D Shape Representation
Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power.
DeepCloth: Neural Garment Representation for Shape and Style Editing
In this work, we introduce, DeepCloth, a unified framework for garment representation, reconstruction, animation and editing.
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments.
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input.
Online Conversation Disentanglement with Pointer Networks
We also introduce a joint-learning objective to better capture contextual information.
Semantic Evaluation for Text-to-SQL with Distilled Test Suites
We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data.
Ranked #8 on
Semantic Parsing
on spider
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image.
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions.
PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction
To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function.
Ranked #2 on
3D Human Reconstruction
on CAPE
DART: Open-Domain Structured Data Record to Text Generation
Data-to-Text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures.
Semantic Evaluation for Text-to-SQL with Distilled Test Suite
We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models, where a predicted query is semantically correct if its denotation is the same as the gold for every possible database.
MulayCap: Multi-layer Human Performance Capture Using A Monocular Video Camera
We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning.
Robust 3D Self-portraits in Seconds
In this paper, we propose an efficient method for robust 3D self-portraits using a single RGBD camera.
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification
To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed.
Ranked #7 on
Hyperspectral Image Classification
on Indian Pines
Few-Shot Image Classification
Generative Adversarial Network
+2
4D Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras
Our method enables a realtime online motion capture system running at 30fps using 5 cameras on a 5-person scene.
Ranked #8 on
3D Multi-Person Pose Estimation
on Shelf
Salvaging Federated Learning by Local Adaptation
First, we show that on standard tasks such as next-word prediction, many participants gain no benefit from FL because the federated model is less accurate on their data than the models they can train locally on their own.
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
Hyperbolic embeddings achieve excellent performance when embedding hierarchical data structures like synonym or type hierarchies, but they can be limited by numerical error when ordinary floating-point numbers are used to represent points in hyperbolic space.
Region Normalization for Image Inpainting
In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Natural images are virtually surrounded by low-density misclassified regions that can be efficiently discovered by gradient-guided search --- enabling the generation of adversarial images.
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
Ranked #8 on
Dialogue State Tracking
on CoSQL
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
We focus on the cross-domain context-dependent text-to-SQL generation task.
Ranked #5 on
Text-To-SQL
on SParC
Progressive Image Inpainting with Full-Resolution Residual Network
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes.
SParC: Cross-Domain Semantic Parsing in Context
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection
Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper.
DeepHuman: 3D Human Reconstruction from a Single Image
We propose DeepHuman, an image-guided volume-to-volume translation CNN for 3D human reconstruction from a single RGB image.
SimulCap : Single-View Human Performance Capture with Cloth Simulation
This paper proposes a new method for live free-viewpoint human performance capture with dynamic details (e. g., cloth wrinkles) using a single RGBD camera.
Simplifying Graph Convolutional Networks
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #3 on
Text Classification
on Ohsumed
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Ranked #7 on
Text-To-SQL
on SParC
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
Ranked #10 on
Semantic Parsing
on spider
HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs
We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs).
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images
A variety of deep neural networks have been applied in medical image segmentation and achieve good performance.
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.
Ranked #2 on
Code Generation
on WikiSQL
DoubleFusion: Real-time Capture of Human Performances with Inner Body Shapes from a Single Depth Sensor
We further propose a joint motion tracking method based on the double layer representation to enable robust and fast motion tracking performance.
Curvature-based Comparison of Two Neural Networks
In this paper we show the similarities and differences of two deep neural networks by comparing the manifolds composed of activation vectors in each fully connected layer of them.
Preliminary theoretical troubleshooting in Variational Autoencoder
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
The Local Dimension of Deep Manifold
Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.
BodyFusion: Real-Time Capture of Human Motion and Surface Geometry Using a Single Depth Camera
To reduce the ambiguities of the non-rigid deformation parameterization on the surface graph nodes, we take advantage of the internal articulated motion prior for human performance and contribute a skeleton-embedded surface fusion (SSF) method.
Leveraging Sparse and Dense Feature Combinations for Sentiment Classification
This model outperforms many deep learning models and achieves comparable results to other deep learning models with complex architectures on sentiment analysis datasets.
