Search Results for author:
Found 125 papers, 48 papers with code
AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks.
StyleAvatar3D: Leveraging Image-Text Diffusion Models for High-Fidelity 3D Avatar Generation
The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models.
Boost Vision Transformer with GPU-Friendly Sparsity and Quantization
Moreover, GPUSQ-ViT can boost actual deployment performance by 1. 39-1. 79 times and 3. 22-3. 43 times of latency and throughput on A100 GPU, and 1. 57-1. 69 times and 2. 11-2. 51 times improvement of latency and throughput on AGX Orin.
Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend
Adversarial attack is commonly regarded as a huge threat to neural networks because of misleading behavior.
Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS) models relying on class activation maps (CAMs) have achieved desirable performance comparing to the non-CAMs-based counterparts.
Stimulative Training++: Go Beyond The Performance Limits of Residual Networks
In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond their performance limits.
Revolutionizing Agrifood Systems with Artificial Intelligence: A Survey
With the world population rapidly increasing, transforming our agrifood systems to be more productive, efficient, safe, and sustainable is crucial to mitigate potential food shortages.
Semi-Supervised Semantic Segmentation With Region Relevance
The most common approach is to generate pseudo-labels for unlabeled images to augment the training data.
Multi-view Vision-Prompt Fusion Network: Can 2D Pre-trained Model Boost 3D Point Cloud Data-scarce Learning?
Then, a novel multi-view prompt fusion module is developed to effectively fuse information from different views to bridge the gap between 3D point cloud data and 2D pre-trained models.
BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation
Second, by leveraging the stochastic nature of the diffusion model, our BerDiff randomly samples the initial Bernoulli noise and intermediate latent variables multiple times to produce a range of diverse segmentation masks, which can highlight salient regions of interest that can serve as valuable references for radiologists.
What Makes for Effective Few-shot Point Cloud Classification?
Due to the emergence of powerful computing resources and large-scale annotated datasets, deep learning has seen wide applications in our daily life.
A Closer Look at Few-Shot 3D Point Cloud Classification
In recent years, research on few-shot learning (FSL) has been fast-growing in the 2D image domain due to the less requirement for labeled training data and greater generalization for novel classes.
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects.
TactoFind: A Tactile Only System for Object Retrieval
We study the problem of object retrieval in scenarios where visual sensing is absent, object shapes are unknown beforehand and objects can move freely, like grabbing objects out of a drawer.
Performance-aware Approximation of Global Channel Pruning for Multitask CNNs
Global channel pruning (GCP) aims to remove a subset of channels (filters) across different layers from a deep model without hurting the performance.
Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection
In this paper, we study the task of training a unified 3D detector from multiple datasets.
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
Unsupervised Domain Adaptation (UDA) technique has been explored in 3D cross-domain tasks recently.
Tucker Bilinear Attention Network for Multi-scale Remote Sensing Object Detection
However, for small targets, there still be seriously missed detections due to the loss of key detail features.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network
Few-shot semantic segmentation is the task of learning to locate each pixel of the novel class in the query image with only a few annotated support images.
Ranked #2 on
Few-Shot Semantic Segmentation
on COCO-20i (10-shot)
A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction
Meanwhile, a new benchmark for several outdoor NeRF-based tasks is established, such as novel view synthesis, surface reconstruction, and multi-modal NeRF.
$β$-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Search
To address the robustness problem, we first benchmark different NAS methods under a wide range of proxy data, proxy channels, proxy layers and proxy epochs, since the robustness of NAS under different kinds of proxies has not been explored before.
Do Performance Aspirations Matter for Guiding Software Configuration Tuning?
Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process.
End-to-End 3D Dense Captioning with Vote2Cap-DETR
Compared with prior arts, our framework has several appealing advantages: 1) Without resorting to numerous hand-crafted components, our method is based on a full transformer encoder-decoder architecture with a learnable vote query driven object decoder, and a caption decoder that produces the dense captions in a set-prediction manner.
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering
Moreover, for grasping the unified semantic representation, we extend the adversarial masking data augmentation to the contrastive representation learning of vision and text in a unified manner.
Executing your Commands via Motion Diffusion in Latent Space
We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors.
Ranked #1 on
Motion Synthesis
on KIT Motion-Language
Towards Accurate Ground Plane Normal Estimation from Ego-Motion
In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles.
Visual Dexterity: In-hand Dexterous Manipulation from Depth
In-hand object reorientation is necessary for performing many dexterous manipulation tasks, such as tool use in unstructured environments that remain beyond the reach of current robots.
Instance-aware Model Ensemble With Distillation For Unsupervised Domain Adaptation
To enhance the model, adaptability between domains and reduce the computational cost when deploying the ensemble model, we propose a novel framework, namely Instance aware Model Ensemble With Distillation, IMED, which fuses multiple UDA component models adaptively according to different instances and distills these components into a small model.
Coordinates Are NOT Lonely -- Codebook Prior Helps Implicit Neural 3D Representations
Implicit neural 3D representation has achieved impressive results in surface or scene reconstruction and novel view synthesis, which typically uses the coordinate-based multi-layer perceptrons (MLPs) to learn a continuous scene representation.
Retrieval Augmentation for T5 Re-ranker using External Sources
Retrieval augmentation has shown promising improvements in different tasks.
Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing
In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training strategy to strengthen the performance of residual networks.
Distilling Task-specific Logical Rules from Large Pre-trained Models
Specifically, we borrow recent prompt-based language models as the knowledge expert to yield initial seed rules, and based on the formed high-quality instance pool that acts as an intermediary role, we keep teaching the expert to fit our task and learning task-specific logical rules.
Multifaceted Hierarchical Report Identification for Non-Functional Bugs in Deep Learning Frameworks
Yet, given the growing number of new GitHub reports for DL frameworks, it is intrinsically difficult for developers to distinguish those that reveal non-functional bugs among the others, and assign them to the right contributor for investigation in a timely manner.
Efficient Joint-Dimensional Search with Solution Space Regularization for Real-Time Semantic Segmentation
In this paper, we intend to search an optimal network structure that can run in real-time for this problem.
Multi-Forgery Detection Challenge 2022: Push the Frontier of Unconstrained and Diverse Forgery Detection
In this paper, we present the solutions from the Top 3 teams, in order to boost the research work in the field of image forgery detection.
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.
Learning Cross-Image Object Semantic Relation in Transformer for Few-Shot Fine-Grained Image Classification
Few-shot fine-grained learning aims to classify a query image into one of a set of support categories with fine-grained differences.
Saliency Guided Inter- and Intra-Class Relation Constraints for Weakly Supervised Semantic Segmentation
Specifically, we propose a saliency guided class-agnostic distance module to pull the intra-category features closer by aligning features to their class prototypes.
Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model
The results of the experiments support the advantages of our method.
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.
Rapid Locomotion via Reinforcement Learning
Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots.
ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference
This results in significant inference time speedups since the decoder-only architecture only needs to learn to interpret static encoder embeddings during inference.
On Parametric Optimal Execution and Machine Learning Surrogates
Precise calibration of price impact, resilience, etc., is known to be extremely challenging and hence it is critical to understand sensitivity of the execution policy to these parameters.
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation
Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semantic segmentation on mobile devices.
Does Configuration Encoding Matter in Learning Software Performance? An Empirical Study on Encoding Schemes
Learning and predicting the performance of a configurable software system helps to provide better quality assurance.
Topological Experience Replay
State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer.
Planning Landscape Analysis for Self-Adaptive Systems
To assure performance on the fly, planning is arguably one of the most important steps for self-adaptive systems (SASs), especially when they are highly configurable with a daunting number of adaptation options.
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation
Although domain adaptation has been extensively studied in natural image-based segmentation task, the research on cross-domain segmentation for very high resolution (VHR) remote sensing images (RSIs) still remains underexplored.
$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Neural Architecture Search~(NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural networks automatically.
The Weights can be Harmful: Pareto Search versus Weighted Search in Multi-Objective Search-Based Software Engineering
However, when clear preferences of the stakeholders (e. g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in.
Pre-Trained Language Models for Interactive Decision-Making
Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans; these representations can aid learning and generalization even outside of language processing.
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search
The NAR explores the quality tiers of the search space globally and classifies each individual to the tier they belong to according to its global ranking.
Out-of-Domain Semantics to the Rescue! Zero-Shot Hybrid Retrieval Models
In this work, we carefully select five datasets, including two in-domain datasets and three out-of-domain datasets with different levels of domain shift, and study the generalization of a deep model in a zero-shot setting.
Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?
When faced with changing environment, highly configurable software systems need to dynamically search for promising adaptation plan that keeps the best possible performance, e. g., higher throughput or smaller latency -- a typical planning problem for self-adaptive systems (SASs).
Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction
While CNN excels at extracting local detail features, the Transformer naturally perceives global contextual information.
LONViZ: Unboxing the black-box of Configurable Software Systems from a Complex Networks Perspective
Due to the black-box nature, it is difficult, if not impossible, to analyze and understand its behavior, such as the interaction between combinations of configuration options with regard to the performance, in particular, which is of great importance to advance the controllability of the underlying software system.
b-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
Neural Architecture Search (NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural network automatically.
Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.
Feature-context driven Federated Meta-Learning for Rare Disease Prediction
Additionally, a dynamic-weight based fusion strategy is proposed to further improve the accuracy of federated learning, which dynamically selects clients based on the accuracy of each local model.
MMO: Meta Multi-Objectivization for Software Configuration Tuning
We also demonstrate that the MMO model with the new normalization can consolidate Flash, a recent model-based tuning tool, on 68% of the cases with 1. 22x speedup in general.
CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization
We propose CO2Sum (Contrastive for Consistency), a contrastive learning scheme that can be easily applied on sequence-to-sequence models for factual-consistent abstractive summarization, proving that the model can be fact-aware without modifying the architecture.
Point Cloud Instance Segmentation with Semi-supervised Bounding-Box Mining
For stage two, the bounding box proposals with SPCR are grouped into some subsets, and the instance masks are mined inside each subset with a novel semantic propagation module and a property consistency graph module.
A System for General In-Hand Object Re-Orientation
The videos of the learned policies are available at: https://taochenshh. github. io/projects/in-hand-reorientation.
Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment
Recent face reenactment works are limited by the coarse reference landmarks, leading to unsatisfactory identity preserving performance due to the distribution gap between the manipulated landmarks and those sampled from a real person.
Sketch Me A Video
Video creation has been an attractive yet challenging task for artists to explore.
Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
Densely Semantic Enhancement for Domain Adaptive Region-free Detectors
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
Object-aware Long-short-range Spatial Alignment for Few-Shot Fine-Grained Image Classification
The goal of few-shot fine-grained image classification is to recognize rarely seen fine-grained objects in the query set, given only a few samples of this class in the support set.
Fine-Grained Image Classification
Semantic correspondence
+2
TexSmart: A System for Enhanced Natural Language Understanding
This paper introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification
In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification.
An End-to-End Differentiable Framework for Contact-Aware Robot Design
Existing methods for co-optimization are limited and fail to explore a rich space of designs.
Empower Distantly Supervised Relation Extraction with Collaborative Adversarial Training
In this paper, we propose collaborative adversarial training to improve the data utilization, which coordinates virtual adversarial training (VAT) and adversarial training (AT) at different levels.
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction
The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task.
Dynamic Language Models for Continuously Evolving Content
To this end, we both explore two different vocabulary composition methods, as well as propose three sampling methods which help in efficient incremental training for BERT-like models.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction
Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document.
Multi-Objectivizing Software Configuration Tuning (for a single performance concern)
Automatically tuning software configuration for optimizing a single performance attribute (e. g., minimizing latency) is not trivial, due to the nature of the configuration systems (e. g., complex landscape and expensive measurement).
Gradient Descent in Materio
Here, we demonstrate an efficient and accurate homodyne gradient extraction method for performing gradient descent on the loss function directly in the material system.
LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
text, table, image) and propose a novel layout-aware multimodal hierarchical framework, LAMPreT, to model the blocks and the whole document.
Residual Model Learning for Microrobot Control
RML provides a general framework for learning from extremely small amounts of interaction data, and our experiments with HAMR clearly demonstrate that RML substantially outperforms existing techniques.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
EADNet: Efficient Asymmetric Dilated Network for Semantic Segmentation
Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network, to simultaneously reduce computational cost and increase inference speed.
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Then we utilize the fused prototype to guide the final segmentation of the query image.
Noncyclic Geometric Quantum Gates with Smooth Paths via Invariant-based Shortcuts
Here, we propose a scheme to realize geometric quantum gates with noncyclic and nonadiabatic evolution via invariant-based shortcuts, where universal quantum gates can be induced in one step without path mutation and the gate time is also effectively shortened.
Quantum Physics
Learning to Recover from Failures using Memory
Learning from past mistakes is a quintessential aspect of intelligence.
Deep Symmetric Network for Underexposed Image Enhancement With Recurrent Attentional Learning
Underexposed image enhancement is of importance in many research domains.
TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities.
In silico study of the lipophilicity of the follicular route during topical drug delivery
The results obtained with the follicular gap filled with water give satisfactory quantitative comparison with in vitro experimental data.
Numerical analysis of the strain distribution in skin domes formed upon the application of hypobaric pressure
Suction cups are widely used in applications such as in measurement of mechanical properties of skin in vivo, in drug delivery devices or in acupuncture treatment.
M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic.
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection
Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance.
A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation
Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles and others.
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems
Using the proposed deep RL scheme, each MU in the system is able to make decisions without a priori statistical knowledge of dynamics.
Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression
Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks can be recovered or not depends, to a large extent, on how early they will be treated appropriately before irreversible consequences are caused to the patients by the virus.
Context-Based Quotation Recommendation
Our experiments confirm the strong performance of BERT-based methods on this task, which outperform bag-of-words and neural ranking baselines by more than 30% relative across all ranking metrics.
Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition
The introduced deep feature mining approach can precisely recognize human motion intents from raw EEG signals, which paves the road to translate the EEG based MI recognition to practical BCI systems.
JMNet: A joint matting network for automatic human matting
We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images.
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance
In this paper, we propose to alleviate these problems by means of a novel gaze redirection framework which exploits both a numerical and a pictorial direction guidance, jointly with a coarse-to-fine learning strategy.
Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses
Recent advances in Generative Adversarial Nets (GANs) have shown remarkable improvements for facial expression editing.
How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance
We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.
Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
Search-Based Software Engineering for Self-Adaptive Systems: Survey, Disappointments, Suggestions and Opportunities
In this paper, we provide the first systematic and comprehensive survey exclusively on SBSE for SASs, covering papers in 27 venues from 7 repositories, which eventually leads to several key statistics from the most notable 74 primary studies in this particular field of research.
Synergizing Domain Expertise with Self-Awareness in Software Systems: A Patternized Architecture Guideline
To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process.
Learning to Augment Expressions for Few-shot Fine-grained Facial Expression Recognition
Considering the phenomenon of uneven data distribution and lack of samples is common in real-world scenarios, we further evaluate several tasks of few-shot expression learning by virtue of our F2ED, which are to recognize the facial expressions given only few training instances.
A Gegenbauer Neural Network with Regularized Weights Direct Determination for Classification
Single-hidden layer feed forward neural networks (SLFNs) are widely used in pattern classification problems, but a huge bottleneck encountered is the slow speed and poor performance of the traditional iterative gradient-based learning algorithms.
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
Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.
A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition
First, we create a new facial expression dataset of more than 200k images with 119 persons, 4 poses and 54 expressions.
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
This paper introduces PyRobot, an open-source robotics framework for research and benchmarking.
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.
On Using Retrained and Incremental Machine Learning for Modeling Performance of Adaptable Software: An Empirical Comparison
Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e. g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating, understanding and predicting software performance, which facilitates better informed self-adaptations.
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Robotics
Learning Exploration Policies for Navigation
Numerous past works have tackled the problem of task-driven navigation.
Hardware Conditioned Policies for Multi-Robot Transfer Learning
In tasks where knowing the agent dynamics is important for success, we learn an embedding for robot hardware and show that policies conditioned on the encoding of hardware tend to generalize and transfer well.
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one.
A Comparison of Word Embeddings for English and Cross-Lingual Chinese Word Sense Disambiguation
Thus we have also applied word embeddings to the novel task of cross-lingual WSD for Chinese and provide a public dataset for further benchmarking.
Multilingual Visual Sentiment Concept Matching
The impact of culture in visual emotion perception has recently captured the attention of multimedia research.
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia.
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural Networks
Nearly one million Flickr images tagged with these ANPs are downloaded to train the classifiers of the concepts.
Latent Tree Models and Approximate Inference in Bayesian Networks
We propose a novel method for approximate inference in Bayesian networks (BNs).
Sketch2Photo: Internet Image Montage
The composed picture is generated by seamlessly stitching several photographs in agreement with the sketch and text labels; these are found by searching the Internet.
