Search Results for author:
Found 177 papers, 68 papers with code
Attacking Transformers with Feature Diversity Adversarial Perturbation
Understanding the mechanisms behind Vision Transformer (ViT), particularly its vulnerability to adversarial perturba tions, is crucial for addressing challenges in its real-world applications.
Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination
The common goal is an achievable state with high value, which is obtained by sampling from the distribution of future states.
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices
Human activity recognition (HAR) holds significant importance in smart homes, security, and healthcare.
GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians
Hairstyle reflects culture and ethnicity at first glance.
PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models
In recent times, a plethora of Large Code Generation Models (LCGMs) have been proposed, showcasing significant potential in assisting developers with complex programming tasks.
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e. g., winning games).
Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification
However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify.
Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study
We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision.
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion
Generating realistic human motion sequences from text descriptions is a challenging task that requires capturing the rich expressiveness of both natural language and human motion. Recent advances in diffusion models have enabled significant progress in human motion synthesis. However, existing methods struggle to handle text inputs that describe complex or long motions. In this paper, we propose the Adaptable Motion Diffusion (AMD) model, which leverages a Large Language Model (LLM) to parse the input text into a sequence of concise and interpretable anatomical scripts that correspond to the target motion. This process exploits the LLM's ability to provide anatomical guidance for complex motion synthesis. We then devise a two-branch fusion scheme that balances the influence of the input text and the anatomical scripts on the inverse diffusion process, which adaptively ensures the semantic fidelity and diversity of the synthesized motion. Our method can effectively handle texts with complex or long motion descriptions, where existing methods often fail.
Towards Detailed Text-to-Motion Synthesis via Basic-to-Advanced Hierarchical Diffusion Model
Besides, we introduce a multi-denoiser framework for the advanced diffusion model to ease the learning of high-dimensional model and fully explore the generative potential of the diffusion model.
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
We present FoundationPose, a unified foundation model for 6D object pose estimation and tracking, supporting both model-based and model-free setups.
Optimized View and Geometry Distillation from Multi-view Diffuser
We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images.
Fine-grained Appearance Transfer with Diffusion Models
A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes.
Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields
Different from existing methods that consider cross-view and along-epipolar information independently, EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process.
Ranked #1 on
Generalizable Novel View Synthesis
on Shiny dataset
Iterative missing value imputation based on feature importance
Existing imputation methods estimate the missing parts based on the observed values in the original feature space, and they treat all features as equally important during data completion, while in fact different features have different importance.
SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handovers
In this paper, we introduce a framework that can generate plausible human grasping motions suitable for training the robot.
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples.
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space.
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Due to the absence of autoregressive modeling and optical flow alignment, we can design an extremely minimalist framework that can greatly benefit computational efficiency.
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance.
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages.
Progressive Text-to-Image Diffusion with Soft Latent Direction
In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges.
RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models
Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses.
DiffusionTrack: Diffusion Model For Multi-Object Tracking
In inference, the model refines a set of paired randomly generated boxes to the detection and tracking results in a flexible one-step or multi-step denoising diffusion process.
Dynamic Neural Network is All You Need: Understanding the Robustness of Dynamic Mechanisms in Neural Networks
Then, through research studies, we provide insight into the design choices that can increase robustness of DyNNs against the attack generated using static model.
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
We thus introduce a dynamic gating network on top of the low-rank adaptation method, in order to decide which decoder layer should employ adaptation.
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model.
Ranked #3 on
Diffusion Personalization Tuning Free
on AgeDB
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation
In this paper, we propose a uniform network to fill both the gaps, termed FGNet.
HateModerate: Testing Hate Speech Detectors against Content Moderation Policies
We observe significant improvement in the models' conformity to content policies while having comparable scores on the original test data.
MIMONet: Multi-Input Multi-Output On-Device Deep Learning
In this paper, we propose MIMONet, a novel on-device multi-input multi-output (MIMO) DNN framework that achieves high accuracy and on-device efficiency in terms of critical performance metrics such as latency, energy, and memory usage.
DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization
\tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process.
RLTF: Reinforcement Learning from Unit Test Feedback
The goal of program synthesis, or code generation, is to generate executable code based on given descriptions.
AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot.
DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy
Treatment planning, which is a critical component of the radiotherapy workflow, is typically carried out by a medical physicist in a time-consuming trial-and-error manner.
C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction
There is an emerging effort to combine the two popular 3D frameworks using Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific focus on the few-shot / sparse view setting.
SlothSpeech: Denial-of-service Attack Against Speech Recognition Models
We show that popular ASR models like Speech2Text model and Whisper model have dynamic computation based on different inputs, causing dynamic efficiency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes
To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in recent years.
Future-conditioned Unsupervised Pretraining for Decision Transformer
While promising, return conditioning is limited to training data labeled with rewards and therefore faces challenges in learning from unsupervised data.
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization
In this paper, we conduct a thorough causal analysis to investigate the origins of biased activation.
Dynamic Transformers Provide a False Sense of Efficiency
Despite much success in natural language processing (NLP), pre-trained language models typically lead to a high computational cost during inference.
Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
MOBA games, e. g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far.
ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling
The research fields of parametric face model and 3D face reconstruction have been extensively studied.
Ranked #1 on
Face Alignment
on FaceScape
Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition
However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations.
NeMF: Inverse Volume Rendering with Neural Microflake Field
Recovering the physical attributes of an object's appearance from its images captured under an unknown illumination is challenging yet essential for photo-realistic rendering.
Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR
Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR.
Learning Human-to-Robot Handovers from Point Clouds
We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction.
Multi-RIS-aided Wireless Communications in Real-world: Prototyping and Field Trials
The performance of multiple reconfigurable intelligent surfaces (RISs) receives limited attention in previous studies.
Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detection
To address this issue, we propose an Uncertainty Regulated Dual Memory Units (UR-DMU) model to learn both the representations of normal data and discriminative features of abnormal data.
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for sample reuse.
Compact Transformer Tracker with Correlative Masked Modeling
Transformer framework has been showing superior performances in visual object tracking for its great strength in information aggregation across the template and search image with the well-known attention mechanism.
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning
We show that, despite such state distribution shift, the policy gradient estimation bias can be reduced in the following three ways: 1) a small learning rate; 2) an adaptive-learning-rate-based optimizer; and 3) KL regularization.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection
Recent advancements in deploying deep neural networks (DNNs) on resource-constrained devices have generated interest in input-adaptive dynamic neural networks (DyNNs).
Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo
To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds.
Ranked #6 on
Point Clouds
on Tanks and Temples
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification.
Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification
The framework mainly consists of a sparse encoder, a multi-view feature mathcing module, and a feature consolidation decoder.
Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System
To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm.
TestAug: A Framework for Augmenting Capability-based NLP Tests
Our experiments show that TestAug has three advantages over the existing work on behavioral testing: (1) TestAug can find more bugs than existing work; (2) The test cases in TestAug are more diverse; and (3) TestAug largely saves the manual efforts in creating the test suites.
DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks
To ensure an AdNN satisfies the performance requirements of resource-constrained applications, it is essential to conduct performance testing to detect IDPBs in the AdNN.
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation
Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth.
NMTSloth: Understanding and Testing Efficiency Degradation of Neural Machine Translation Systems
Neural Machine Translation (NMT) systems have received much recent attention due to their human-level accuracy.
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets
In order to comprehensively investigate the influence caused by the misalignment, we proposed a method for estimating the performance of a 4f-ONN in response to various misalignment in the context of the image classification task. The misalignment in numerical simulation is estimated by manipulating the optical intensity distributions in the fourth focus plane in the 4f system.
DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations
The policy learned from our dataset can generalize well on unseen object poses in both simulation and the real world
Revisiting Discrete Soft Actor-Critic
We study the adaption of soft actor-critic (SAC) from continuous action space to discrete action space.
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning.
DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field.
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information.
Fast localization and single-pixel imaging of the moving object using time-division multiplexing
When imaging moving objects, single-pixel imaging produces motion blur.
Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value Functions
The pipeline of current robotic pick-and-place methods typically consists of several stages: grasp pose detection, finding inverse kinematic solutions for the detected poses, planning a collision-free trajectory, and then executing the open-loop trajectory to the grasp pose with a low-level tracking controller.
VulCNN: An Image-inspired Scalable Vulnerability Detection System
Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability.
Learning to Reverse DNNs from AI Programs Automatically
With the privatization deployment of DNNs on edge devices, the security of on-device DNNs has raised significant concern.
HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers
We analyze the performance of a set of baselines and show a correlation with a real-world evaluation.
Transformer Tracking with Cyclic Shifting Window Attention
Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism.
Detecting Topology Attacks against Graph Neural Networks
In this work, we study the victim node detection problem under topology attacks against GNNs.
CorrGAN: Input Transformation Technique Against Natural Corruptions
These DNNs are vulnerable to adversarial perturbations and corruptions.
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0. 732 (0. 683-0. 761), and better performance when compared to other methods.
Model Predictive Control for Fluid Human-to-Robot Handovers
However, how to responsively generate smooth motions to take an object from a human is still an open question.
NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models
To further understand such efficiency-oriented threats, we propose a new attack approach, NICGSlowDown, to evaluate the efficiency robustness of NICG models.
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation
We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
EREBA: Black-box Energy Testing of Adaptive Neural Networks
The test inputs generated by EREBA can increase the energy consumption of AdNNs by 2, 000% compared to the original inputs.
NeuVV: Neural Volumetric Videos with Immersive Rendering and Editing
We further develop a hybrid neural-rasterization rendering framework to support consumer-level VR headsets so that the aforementioned volumetric video viewing and editing, for the first time, can be conducted immersively in virtual 3D space.
Video-driven Neural Physically-based Facial Asset for Production
In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets.
Artemis: Articulated Neural Pets with Appearance and Motion synthesis
Our ARTEMIS enables interactive motion control, real-time animation, and photo-realistic rendering of furry animals.
Learning Free Gait Transition for Quadruped Robots via Phase-Guided Controller
Reinforcement learning has become a powerful tool to formulate controllers for legged robots.
Node-Aligned Graph Convolutional Network for Whole-Slide Image Representation and Classification
In this paper, we propose a hierarchical global-to-local clustering strategy to build a Node-Aligned GCN (NAGCN) to represent WSI with rich local structural information as well as global distribution.
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning
To address this, we propose JueWu-MC, a sample-efficient hierarchical RL approach equipped with representation learning and imitation learning to deal with perception and exploration.
Efficient Exploration
Hierarchical Reinforcement Learning
+4
HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs
The raw HumanNeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings.
Hierarchical Neural Data Synthesis for Semantic Parsing
Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily available, especially in cross-domain semantic parsing.
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
Labels are costly and sometimes unreliable.
Ranked #5 on
Image Classification
on mini WebVision 1.0
Learning Perceptual Concepts by Bootstrapping from Human Queries
Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator.
Learning Diverse Policies in MOBA Games via Macro-Goals
Recently, many researchers have made successful progress in building the AI systems for MOBA-game-playing with deep reinforcement learning, such as on Dota 2 and Honor of Kings.
NODEAttack: Adversarial Attack on the Energy Consumption of Neural ODEs
Unlike the memory cost, the energy consumption of the Neural ODEs during inference can be adaptive because of the adaptive nature of the ODE solvers.
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems
To further the understanding of such efficiency-oriented threats and raise the community’s concern on the efficiency robustness of NMT systems, we propose a new attack approach, TranSlowDown, to test the efficiency robustness of NMT systems.
Estimating Predictive Uncertainty Under Program Data Distribution Shift
Experiments show that program distribution shift does degrade the DL model performance to varying degrees and that existing uncertainty methods all present certain limitations in quantifying uncertainty on program dataset.
GLIB: Towards Automated Test Oracle for Graphically-Rich Applications
We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99. 5\% recall in detecting non-crashing bugs such as game GUI glitches.
Boosting Offline Reinforcement Learning with Residual Generative Modeling
While most research focuses on the state-action function part through reducing the bootstrapping error in value function approximation induced by the distribution shift of training data, the effects of error propagation in generative modeling have been neglected.
SynthASR: Unlocking Synthetic Data for Speech Recognition
Our observations show that SynthASR holds great promise in training the state-of-the-art large-scale E2E ASR models for new applications while reducing the costs and dependency on production data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
A natural language database interface (NLDB) can democratize data-driven insights for non-technical users.
A Globally Normalized Neural Model for Semantic Parsing
In this paper, we propose a globally normalized model for context-free grammar (CFG)-based semantic parsing.
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos
In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D human motions and understanding fine-grained actions from monocular challenging sports video input.
Wide-Beam Array Antenna Power Gain Maximization via ADMM Framework
This paper proposes two algorithms to maximize the minimum array power gain in a wide-beam mainlobe by solving the power gain pattern synthesis (PGPS) problem with and without sidelobe constraints.
DexYCB: A Benchmark for Capturing Hand Grasping of Objects
We introduce DexYCB, a new dataset for capturing hand grasping of objects.
Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet
MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM).
F3SNet: A Four-Step Strategy for QIM Steganalysis of Compressed Speech Based on Hierarchical Attention Network
Traditional machine learning-based steganalysis methods on compressed speech have achieved great success in the field of communication security.
Cryptography and Security
Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation
Current one-step multi-object tracking and segmentation (MOTS) methods lag behind recent two-step methods.
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation
Second, we introduce an occlusion-aware distillation (OA Distillation) module, which leverages the predicted depths from StereoNet in non-occluded regions to train our monocular depth estimation network named SingleNet.
AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack
We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks.
Optimizing Deeper Transformers on Small Datasets
This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Text-to-SQL semantic parsing and logical reading comprehension.
Which Heroes to Pick? Learning to Draft in MOBA Games with Neural Networks and Tree Search
Hero drafting is essential in MOBA game playing as it builds the team of each side and directly affects the match outcome.
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Features for Indoor Localization
Experimental results show that the proposed RaP-Net trained with OpenLORIS-Location dataset achieves excellent performance in the feature matching task and significantly outperforms state-of-the-arts feature algorithms in indoor localization.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.
Reactive Human-to-Robot Handovers of Arbitrary Objects
We demonstrate the generalizability, usability, and robustness of our approach on a novel benchmark set of 26 diverse household objects, a user study with naive users (N=6) handing over a subset of 15 objects, and a systematic evaluation examining different ways of handing objects.
Emergence of Chern insulating states in non-Magic angle twisted bilayer graphene
For {\theta}=1. 25{\deg}, we observe an emergence of topological insulating states at hole side with a sequence of Chern number |C|=4-|v|, where v is the number of electrons (holes) in moir\'e unite cell.
Mesoscale and Nanoscale Physics Materials Science
Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
We demonstrate that our learned policy can be integrated into a tabletop 6D grasping system and a human-robot handover system to improve the grasping performance of unseen objects.
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement.
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.
Loop Closure Detection
Simultaneous Localization and Mapping
Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
The resulting online MOTS framework, named PointTrack, surpasses all the state-of-the-art methods including 3D tracking methods by large margins (5. 4% higher MOTSA and 18 times faster over MOTSFusion) with the near real-time speed (22 FPS).
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
Human Grasp Classification for Reactive Human-to-Robot Handovers
In this paper, we propose an approach for human-to-robot handovers in which the robot meets the human halfway, by classifying the human's grasp of the object and quickly planning a trajectory accordingly to take the object from the human's hand according to their intent.
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection
The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes.
Integrating Discrete and Neural Features via Mixed-feature Trans-dimensional Random Field Language Models
There has been a long recognition that discrete features (n-gram features) and neural network based features have complementary strengths for language models (LMs).
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents
Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient.
Mastering Complex Control in MOBA Games with Deep Reinforcement Learning
We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games.
Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
We also design benchmarking metrics for lifelong SLAM, with which the robustness and accuracy of pose estimation are evaluated separately.
Our Neural Machine Translation Systems for WAT 2019
We use Transformer model through our work in this paper to explore and experience the powerful of the Transformer architecture relying on self-attention mechanism.
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
This paper applies BERT to ad hoc document retrieval on news articles, which requires addressing two challenges: relevance judgments in existing test collections are typically provided only at the document level, and documents often exceed the length that BERT was designed to handle.
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling
Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering.
Applying BERT to Document Retrieval with Birch
We present Birch, a system that applies BERT to document retrieval via integration with the open-source Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections.
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
A core problem of information retrieval (IR) is relevance matching, which is to rank documents by relevance to a user{'}s query.
Multi-Stage Document Ranking with BERT
The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing.
DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System
Teleoperation offers the possibility of imparting robotic systems with sophisticated reasoning skills, intuition, and creativity to perform tasks.
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability
In this work, after theoretically quantifying the estimation error bound and the manipulating risk bound of the Laplace mechanism, we propose two mechanisms improving the usefulness and soundness simultaneously: the weighted sampling mechanism and the additive mechanism.
End-to-End Neural Context Reconstruction in Chinese Dialogue
We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context.
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.
Document Expansion by Query Prediction
One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content. From the perspective of a question answering system, this might comprise questions the document can potentially answer.
Ranked #1 on
Passage Re-Ranking
on TREC-PM
Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
Recently, a simple combination of passage retrieval using off-the-shelf IR techniques and a BERT reader was found to be very effective for question answering directly on Wikipedia, yielding a large improvement over the previous state of the art on a standard benchmark dataset.
Ranked #3 on
Open-Domain Question Answering
on SQuAD1.1 dev
Simple Applications of BERT for Ad Hoc Document Retrieval
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
(MAP metric)
End-to-End Open-Domain Question Answering with BERTserini
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit.
Ranked #4 on
Open-Domain Question Answering
on SQuAD1.1 dev
A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Learning word embeddings has received a significant amount of attention recently.
Hierarchical Macro Strategy Model for MOBA Game AI
In this paper, we propose a novel learning-based Hierarchical Macro Strategy model for mastering MOBA games, a sub-genre of RTS games.
Sequenced-Replacement Sampling for Deep Learning
We propose sequenced-replacement sampling (SRS) for training deep neural networks.
Visual Semantic Navigation using Scene Priors
Do we use the semantic/functional priors we have built over years to efficiently search and navigate?
SemRegex: A Semantics-Based Approach for Generating Regular Expressions from Natural Language Specifications
Recent research proposes syntax-based approaches to address the problem of generating programs from natural language specifications.
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets.
MULDEF: Multi-model-based Defense Against Adversarial Examples for Neural Networks
In particular, given a target model, our framework includes multiple models (constructed from the target model) to form a model family.
Testing Untestable Neural Machine Translation: An Industrial Case
To fill the gap of lacking test oracle for in-vivo testing of an NMT system, in this paper, we propose a new approach for automatically identifying translation failures, without requiring reference translations for a translation task; our approach can directly serve as a test oracle for in-vivo testing.
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings
Experiments on word similarity, syntactic analogy and text classification are conducted to validate the feasibility of our models.
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.
3D Human Pose Estimation in the Wild by Adversarial Learning
Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc.
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
Ray Space Features for Plenoptic Structure-From-Motion
In this paper, we explore using light fields captured by plenoptic cameras or camera arrays as inputs.
Recurrent Attention Network on Memory for Aspect Sentiment Analysis
We propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review.
Aspect-Based Sentiment Analysis (ABSA)
Feature Engineering
+1
Identity-Aware Textual-Visual Matching with Latent Co-attention
The stage-2 CNN-LSTM network refines the matching results with a latent co-attention mechanism.
Learning Feature Pyramids for Human Pose Estimation
We investigate our method on two standard benchmarks for human pose estimation.
Ranked #6 on
Pose Estimation
on Leeds Sports Poses
Multi-Context Attention for Human Pose Estimation
We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts.
Ranked #8 on
Pose Estimation
on Leeds Sports Poses
Progressively Diffused Networks for Semantic Image Segmentation
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning.
Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese--Japanese
To improve the translation accuracy, we adjust and balance the granularity of segmentation results around terms for Chinese{--}Japanese patent corpus for training translation model.
End-To-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation
In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts.
Multi-Task Recurrent Neural Network for Immediacy Prediction
In this paper, we propose to predict immediacy for interacting persons from still images.
Resolving Scale Ambiguity Via XSlit Aspect Ratio Analysis
We first conduct a comprehensive analysis to characterize DDAR, infer object depth from its AR, and model recoverable depth range, sensitivity, and error.
Ambient Occlusion via Compressive Visibility Estimation
This enables a sparsity-prior based solution for iteratively recovering the surface normal, the surface albedo, and the visibility function from a small number of images.
Learning Contour-Fragment-based Shape Model with And-Or Tree Representation
This paper proposes a simple yet effective method to learn the hierarchical object shape model consisting of local contour fragments, which represents a category of shapes in the form of an And-Or tree.
Data-Driven Scene Understanding with Adaptively Retrieved Exemplars
In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics (i. e. the assigned labels of the target and the references).
Clothing Co-Parsing by Joint Image Segmentation and Labeling
This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations.
Discriminatively Trained And-Or Graph Models for Object Shape Detection
In this paper, we investigate a novel reconfigurable part-based model, namely And-Or graph model, to recognize object shapes in images.
