Search Results for author:
Found 61 papers, 26 papers with code
Multi-Agent Embodied Question Answering in Interactive Environments
We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task.
Timestamp-Supervised Action Segmentation in the Perspective of Clustering
We find the frames near the boundaries of action segments are in the transition region between two consecutive actions and have unclear semantics, which we call ambiguous intervals.
Reasoning over Different Types of Knowledge Graphs: Static, Temporal and Multi-Modal
The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task.
Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks
The first stage of ROCK corresponds to the process of decomposing objects into parts in human vision.
When to Update Your Model: Constrained Model-based Reinforcement Learning
Our follow-up derived bounds reveal the relationship between model shifts and performance improvement.
Model-based Reinforcement Learning
reinforcement-learning
+1
Pay Self-Attention to Audio-Visual Navigation
Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input.
Bridged Transformer for Vision and Point Cloud 3D Object Detection
Recently, there is a trend of leveraging multiple sources of input data, such as complementing the 3D point cloud with 2D images that often have richer color and fewer noises.
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective
To understand the reasons behind this phenomenon, we revisit the learning paradigm of knowledge distillation on the few-shot object detection task from the causal theoretic standpoint, and accordingly, develop a Structural Causal Model.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
To remedy this issue, we propose to select positive graph instances directly from existing graphs in the training set, which ultimately maintains the legality and similarity to the target graphs.
SNAKE: Shape-aware Neural 3D Keypoint Field
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection?
Multimodal Token Fusion for Vision Transformers
Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images.
Ranked #1 on
Semantic Segmentation
on SUN-RGBD
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.
Ranked #7 on
Domain Adaptation
on GTA5 to Cityscapes
Equivariant Graph Mechanics Networks with Constraints
The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.
Adversarial Texture for Fooling Person Detectors in the Physical World
Experiments showed that these clothes could fool person detectors in the physical world.
Sound Adversarial Audio-Visual Navigation
In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent.
Sim2Real Object-Centric Keypoint Detection and Description
Keypoint detection and description play a central role in computer vision.
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation
In the Vision-and-Language Navigation task, the embodied agent follows linguistic instructions and navigates to a specific goal.
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
Channel Exchanging Networks for Multimodal and Multitask Dense Image Prediction
For the application of dense image prediction, the validity of CEN is tested by four different scenarios: multimodal fusion, cycle multimodal fusion, multitask learning, and multimodal multitask learning.
Ranked #13 on
Semantic Segmentation
on NYU Depth v2
Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks
In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts.
Constrained Graph Mechanics Networks
In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge
In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network.
Ranked #20 on
Semantic Segmentation
on NYU Depth v2
Elastic Tactile Simulation Towards Tactile-Visual Perception
By contrast, EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact.
Adversarial Option-Aware Hierarchical Imitation Learning
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
Elastic Interaction of Particles for Robotic Tactile Simulation
At its core, EIP models the tactile sensor as a group of coordinated particles, and the elastic theory is applied to regulate the deformation of particles during the contact process.
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Robots have limited adaptation ability compared to humans and animals in the case of damage.
Deep Multimodal Fusion by Channel Exchanging
Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications.
Unsupervised Representation Learning by InvariancePropagation
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
Tackling Over-Smoothing for General Graph Convolutional Networks
Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification.
A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning
Using a gating mechanism that discriminates the unseen samples from the seen samples can decompose the GZSL problem to a conventional Zero-Shot Learning (ZSL) problem and a supervised classification problem.
Resolution Switchable Networks for Runtime Efficient Image Recognition
We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.
Towards Embodied Scene Description
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
In this paper, we present a multimodal mobile teleoperation system that consists of a novel vision-based hand pose regression network (Transteleop) and an IMU-based arm tracking method.
MQA: Answering the Question via Robotic Manipulation
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.
Robust Robotic Pouring using Audition and Haptics
Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment.
Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
The proposed architecture, termed as NICE-GAN, exhibits two advantageous patterns over previous approaches: First, it is more compact since no independent encoding component is required; Second, this plug-in encoder is directly trained by the adversary loss, making it more informative and trained more effectively if a multi-scale discriminator is applied.
FoveaBox: Beyound Anchor-based Object Detection
While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors.
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations.
Regularized Adversarial Sampling and Deep Time-aware Attention for Click-Through Rate Prediction
In deep CTR models, exploiting users' historical data is essential for learning users' behaviors and interests.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
This paper studies Learning from Observations (LfO) for imitation learning with access to state-only demonstrations.
Deep Point-wise Prediction for Action Temporal Proposal
Detecting actions in videos is an important yet challenging task.
Attention-based Transfer Learning for Brain-computer Interface
Different functional areas of the human brain play different roles in brain activity, which has not been paid sufficient research attention in the brain-computer interface (BCI) field.
FoveaBox: Beyond Anchor-based Object Detector
In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis.
Ranked #93 on
Object Detection
on COCO test-dev
(APM metric)
Consistent Optimization for Single-Shot Object Detection
We present consistent optimization for single stage object detection.
Vision-based Teleoperation of Shadow Dexterous Hand using End-to-End Deep Neural Network
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands.
Robotics
PointNetGPD: Detecting Grasp Configurations from Point Sets
In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
Robotics
Deep Feature Pyramid Reconfiguration for Object Detection
In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process.
Deep Transfer Learning for EEG-based Brain Computer Interface
First, we model cognitive events based on EEG data by characterizing the data using EEG optical flow, which is designed to preserve multimodal EEG information in a uniform representation.
A Survey on Deep Transfer Learning
As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains.
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography
Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG.
Learning and Inferring Movement with Deep Generative Model
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks.
Task Transfer by Preference-Based Cost Learning
The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task.
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding
Linear Dynamical Systems (LDSs) are fundamental tools for modeling spatio-temporal data in various disciplines.
RON: Reverse Connection with Objectness Prior Networks for Object Detection
To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs.
MAT: A Multimodal Attentive Translator for Image Captioning
In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model.
Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning
We then devise efficient algorithms to perform sparse coding and dictionary learning on the space of infinite-dimensional subspaces.
Sparse Coding and Dictionary Learning With Linear Dynamical Systems
To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs.
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances.
