Search Results for author:
Found 110 papers, 43 papers with code
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition
Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space.
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
There is a crucial limitation: these works assume the dataset used for training the target model to be known beforehand and leverage this dataset for model attribute attack.
Shared Growth of Graph Neural Networks via Free-direction Knowledge Distillation
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes.
Masked and Permuted Implicit Context Learning for Scene Text Recognition
Scene Text Recognition (STR) is a challenging task due to variations in text style, shape, and background.
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning
Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks.
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
We introduce a method for generating realistic pedestrian trajectories and full-body animations that can be controlled to meet user-defined goals.
EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
Predicting the binding sites of the target proteins plays a fundamental role in drug discovery.
Almost Sure Saddle Avoidance of Stochastic Gradient Methods without the Bounded Gradient Assumption
We prove that various stochastic gradient descent methods, including the stochastic gradient descent (SGD), stochastic heavy-ball (SHB), and stochastic Nesterov's accelerated gradient (SNAG) methods, almost surely avoid any strict saddle manifold.
Learning Human Dynamics in Autonomous Driving Scenarios
In this work, we propose a holistic framework for learning physically plausible human dynamics from real driving scenarios, narrowing the gap between real and simulated human behavior in safety-critical applications.
PhysDiff: Physics-Guided Human Motion Diffusion Model
Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically-plausible motion.
A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG.
Prototype as Query for Few Shot Semantic Segmentation
In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused.
Ranked #11 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
1st Place Solutions for UG2+ Challenge 2022 ATMOSPHERIC TURBULENCE MITIGATION
In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022.
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion
For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.
Ranked #5 on
Link Prediction
on UMLS
Beta R-CNN: Looking into Pedestrian Detection from Another Perspective
What's more, to fully exploit Beta Representation, a novel pipeline Beta R-CNN equipped with BetaHead and BetaMask is proposed, leading to high detection performance in occluded and crowded scenes.
Ranked #9 on
Object Detection
on CrowdHuman (full body)
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world.
Global Prototype Encoding for Incremental Video Highlights Detection
To the best of our knowledge, this is the first time the incremental learning settings are introduced to video highlights detection, which in turn relieves the burden of training video inputs and promotes the scalability of conventional neural networks in proportion to both the size of the dataset and the quantity of domains.
A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model
High-dimensional and incomplete (HDI) data holds tremendous interactive information in various industrial applications.
Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization
Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix.
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism.
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Recent years have witnessed the increasing attentions paid to dynamic graph neural networks for modelling such graph data, where almost all the existing approaches assume that when a new link is built, the embeddings of the neighbor nodes should be updated by learning the temporal dynamics to propagate new information.
Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration
To this end, we propose a Face Restoration Searching Network (FRSNet) to adaptively search the suitable feature extraction architecture within our specified search space, which can directly contribute to the restoration quality.
SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
Deformable image registration can obtain dynamic information about images, which is of great significance in medical image analysis.
From Universal Humanoid Control to Automatic Physically Valid Character Creation
Second, we use a design-and-control framework to optimize a humanoid's physical attributes to find body designs that can better imitate the pre-specified human motion sequence(s).
Embodied Scene-aware Human Pose Estimation
Since 2D third-person observations are coupled with the camera pose, we propose to disentangle the camera pose and use a multi-step projection gradient defined in the global coordinate frame as the movement cue for our embodied agent.
Ranked #300 on
3D Human Pose Estimation
on Human3.6M
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks
Knowledge distillation (KD) has demonstrated its effectiveness to boost the performance of graph neural networks (GNNs), where its goal is to distill knowledge from a deeper teacher GNN into a shallower student GNN.
Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation
We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.
Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression
Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data.
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis
A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes.
A Sampling Theorem for Exact Identification of Continuous-time Nonlinear Dynamical Systems
The necessary and sufficient condition is proposed -- which is built from Koopman operator -- to the exact identification of the CT system from sampled data.
Unified Simulation, Perception, and Generation of Human Behavior
Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans.
Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views.
Online No-regret Model-Based Meta RL for Personalized Navigation
Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting.
Adaptive Divergence-based Non-negative Latent Factor Analysis
High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions.
Syntax-Aware Network for Handwritten Mathematical Expression Recognition
In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network.
Molecule Generation for Drug Design: a Graph Learning Perspective
From the application perspective, one of the emerging and attractive areas is aiding the design and discovery of molecules, especially in drug industry.
On Almost Sure Convergence Rates of Stochastic Gradient Methods
We further provide last-iterate almost sure convergence rates analysis for stochastic gradient methods on weakly convex smooth functions, in contrast with most existing results in the literature that only provide convergence in expectation for a weighted average of the iterates.
Boosting Contrastive Learning with Relation Knowledge Distillation
We delve into this problem and find that the lightweight model is prone to collapse in semantic space when simply performing instance-wise contrast.
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras
Since the joint reconstruction of human motions and camera poses is underconstrained, we propose a global trajectory predictor that generates global human trajectories based on local body movements.
Ranked #1 on
Global 3D Human Pose Estimation
on EMDB
Deep Unsupervised Active Learning on Learnable Graphs
To make the learnt graph structure more stable and effective, we take into account $k$-nearest neighbor graph as a priori, and learn a relation propagation graph structure.
FBNet: Feature Balance Network for Urban-Scene Segmentation
In this paper, we present a novel add-on module, named Feature Balance Network (FBNet), to eliminate the feature camouflage in urban-scene segmentation.
Learning Deep Representation with Energy-Based Self-Expressiveness for Subspace Clustering
In this paper, we propose a new deep subspace clustering framework, motivated by the energy-based models.
Causal Effect Estimation using Variational Information Bottleneck
A typical approach is to formulate causal inference as a supervised learning problem and so counterfactual could be predicted.
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design
Specifically, we learn a conditional policy that, in an episode, first applies a sequence of transform actions to modify an agent's skeletal structure and joint attributes, and then applies control actions under the new design.
Backdoor Attacks on Federated Learning with Lottery Ticket Hypothesis
Edge devices in federated learning usually have much more limited computation and communication resources compared to servers in a data center.
POI Alias Discovery in Delivery Addresses using User Locations
People often refer to a place of interest (POI) by an alias.
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
The novel graph constructor maps a glyph's latent code to its graph representation that matches expert knowledge, which is trained to help the translation task.
Temporal Knowledge Consistency for Unsupervised Visual Representation Learning
The instance discrimination paradigm has become dominant in unsupervised learning.
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study
Existing diversity tests of samples from GANs are usually conducted qualitatively on a small scale, and/or depends on the access to original training data as well as the trained model parameters.
DeceFL: A Principled Decentralized Federated Learning Framework
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
Dynamics-Regulated Kinematic Policy for Egocentric Pose Estimation
By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model.
Egocentric Pose Estimation
Human-Object Interaction Detection
+1
LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images
Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images.
SimPoE: Simulated Character Control for 3D Human Pose Estimation
Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.
Ranked #225 on
3D Human Pose Estimation
on Human3.6M
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Instead, we would prefer a method that allows an agent's state at one time to directly affect another agent's state at a future time.
Ranked #9 on
Trajectory Prediction
on ETH/UCY
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
We present Few-Shot object detection via Contrastive proposals Encoding (FSCE), a simple yet effective approach to learning contrastive-aware object proposal encodings that facilitate the classification of detected objects.
Ranked #11 on
Few-Shot Object Detection
on MS-COCO (30-shot)
Causal inference using deep neural networks
Causal inference from observation data is a core problem in many scientific fields.
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation
We propose a method for incorporating object interaction and human body dynamics into the task of 3D ego-pose estimation using a head-mounted camera.
Automated data extraction of bar chart raster images
Objective: To develop software utilizing optical character recognition toward the automatic extraction of data from bar charts for meta-analysis.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
While machine learning is increasingly used in this field, the resulting large-scale collection of user private information has reinvigorated the privacy debate, considering dozens of data breach incidents every year caused by unauthorized hackers, and (potentially even more) information misuse/abuse by authorized parties.
End-to-End 3D Multi-Object Tracking and Trajectory Forecasting
To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units.
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Such simplification limits the fusion of information at different scales and fails to maintain high-resolution representations.
Efficient Non-Line-of-Sight Imaging from Transient Sinograms
Non-line-of-sight (NLOS) imaging techniques use light that diffusely reflects off of visible surfaces (e. g., walls) to see around corners.
On Deep Unsupervised Active Learning
Unsupervised active learning has attracted increasing attention in recent years, where its goal is to select representative samples in an unsupervised setting for human annotating.
AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
Based on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses.
Ranked #1 on
Facial Landmark Detection
on AFLW-Front
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i. e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples.
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3. 6M) and generates diverse long-term motions.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
Semi-Supervised Cervical Dysplasia Classification With Learnable Graph Convolutional Network
In this paper, we propose a novel and more flexible GCN model with a feature encoder that adaptively updates the adjacency matrix during learning and demonstrate that this model design leads to improved performance.
Optical Non-Line-of-Sight Physics-based 3D Human Pose Estimation
We bring together a diverse set of technologies from NLOS imaging, human pose estimation and deep reinforcement learning to construct an end-to-end data processing pipeline that converts a raw stream of photon measurements into a full 3D human pose sequence estimate.
BoostTree and BoostForest for Ensemble Learning
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.
DLow: Diversifying Latent Flows for Diverse Human Motion Prediction
To obtain samples from a pretrained generative model, most existing generative human motion prediction methods draw a set of independent Gaussian latent codes and convert them to motion samples.
Ranked #1 on
Human Pose Forecasting
on AMASS
(APD metric)
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling
We evaluate on KITTI and nuScenes datasets showing that our method with socially-aware feature learning and diversity sampling achieves new state-of-the-art performance on 3D MOT and trajectory prediction.
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
State-Aware Tracker for Real-Time Video Object Segmentation
For higher efficiency, SAT takes advantage of the inter-frame consistency and deals with each target object as a tracklet.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
This work addresses the above two shortcomings of triplet loss, extending its effectiveness to large-scale ReID datasets with potentially noisy labels.
A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images
In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed.
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification.
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).
Ranked #2 on
Visual Object Tracking
on VOT2017/18
(using extra training data)
PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.
Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration
Generative adversarial networks (GANs) nowadays are capable of producing im-ages of incredible realism.
Machine Discovery of Partial Differential Equations from Spatiotemporal Data
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.
ABD-Net: Attentive but Diverse Person Re-Identification
Attention mechanism has been shown to be effective for person re-identification (Re-ID).
Ranked #16 on
Person Re-Identification
on Market-1501-C
Diverse Trajectory Forecasting with Determinantal Point Processes
To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).
Ranked #5 on
Human Pose Forecasting
on HumanEva-I
Ego-Pose Estimation and Forecasting as Real-Time PD Control
We propose the use of a proportional-derivative (PD) control based policy learned via reinforcement learning (RL) to estimate and forecast 3D human pose from egocentric videos.
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.
UG$^{2+}$ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series
This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.
Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA)
Our final algorithm, mini-batch gradient descent with regularization, DropRule and AdaBound (MBGD-RDA), can achieve fast convergence in training TSK fuzzy systems, and also superior generalization performance in testing.
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis.
WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian.
Bridging the Gap Between Computational Photography and Visual Recognition
From the observed results, it is evident that we are in the early days of building a bridge between computational photography and visual recognition, leaving many opportunities for innovation in this area.
A General End-to-end Diagnosis Framework for Manufacturing Systems
A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
Data-driven Discovery of Cyber-Physical Systems
Cyber-physical systems (CPSs) embed software into the physical world.
3D Ego-Pose Estimation via Imitation Learning
Motivated by this, we propose a novel control-based approach to model human motion with physics simulation and use imitation learning to learn a video-conditioned control policy for ego-pose estimation.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
SFace: An Efficient Network for Face Detection in Large Scale Variations
A new dataset called 4K-Face is also introduced to evaluate the performance of face detection with extreme large scale variations.
Face Attention Network: An Effective Face Detector for the Occluded Faces
The performance of face detection has been largely improved with the development of convolutional neural network.
Ranked #1 on
Occluded Face Detection
on MAFA
On Identification of Distribution Grids
Large-scale integration of distributed energy resources into residential distribution feeders necessitates careful control of their operation through power flow analysis.
Video Representation Learning and Latent Concept Mining for Large-scale Multi-label Video Classification
We report on CMU Informedia Lab's system used in Google's YouTube 8 Million Video Understanding Challenge.
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
Joint Hand Detection and Rotation Estimation by Using CNN
Hand detection is essential for many hand related tasks, e. g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction.
Words or Characters? Fine-grained Gating for Reading Comprehension
Previous work combines word-level and character-level representations using concatenation or scalar weighting, which is suboptimal for high-level tasks like reading comprehension.
Ranked #50 on
Question Answering
on SQuAD1.1 dev
Inverse Power Flow Problem
We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes).
Review Networks for Caption Generation
We propose a novel extension of the encoder-decoder framework, called a review network.
On the Powerball Method for Optimization
We propose a new method to accelerate the convergence of optimization algorithms.
