Search Results for author:
Found 171 papers, 76 papers with code
Rethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model
In this work, we propose novel perspectives on the DBD problem and design convenient approach to build a real-time cost-effective DBD model.
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
This paper proposes new ways of sample mixing by thinking of the process as generation of barycenter in a metric space for data augmentation.
Discriminative Partial Domain Adversarial Network
Previous methods typically match the whole source domain to target domain, which causes negative transfer due to the source-negative classes in source domain that does not exist in target domain.
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information
In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss.
StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding
Charts are common in literature across different scientific fields, conveying rich information easily accessible to readers.
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Autonomous Driving
Our contributions are threefold: (1) Occupancy prediction is shown to be promising for learning general representations, which is demonstrated by extensive experiments on plenty of datasets and tasks.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous-domain knowledge can be hardly directly deployed to a new domain without additional costs.
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
We find that even equipped with a SOTA perception model, directly letting the student model learn the required inputs of the teacher model leads to poor driving performance, which comes from the large distribution gap between predicted privileged inputs and the ground-truth.
Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity
One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses.
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning
On classification tasks, for ViT-S, ADCLR achieves 77. 5% top-1 accuracy on ImageNet with linear probing, outperforming our baseline (DINO) without our devised techniques as plug-in, by 0. 5%.
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks
Graph structure learning is a well-established problem that aims at optimizing graph structures adaptive to specific graph datasets to help message passing neural networks (i. e., GNNs) to yield effective and robust node embeddings.
Simplifying and Empowering Transformers for Large-Graph Representations
Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \textsc{NodeFormer}.
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
End-to-end autonomous driving has made impressive progress in recent years.
HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction
Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction.
OpenLane-V2: A Topology Reasoning Benchmark for Scene Understanding in Autonomous Driving
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute accurate judgments.
Graph-based Topology Reasoning for Driving Scenes
Understanding the road genome is essential to realize autonomous driving.
Ranked #3 on
3D Lane Detection
on OpenLane-V2 test
H2RBox-v2: Incorporating Symmetry for Boosting Horizontal Box Supervised Oriented Object Detection
With the rapidly increasing demand for oriented object detection e. g. in autonomous driving and remote sensing, the recently proposed paradigm involving weakly-supervised detector H2RBox for learning rotated box (RBox) from the (currently) more readily-available horizontal box (HBox) has shown promise.
Geometric-aware Pretraining for Vision-centric 3D Object Detection
We also conduct experiments on various image backbones and view transformations to validate the efficacy of our approach.
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm
We propose a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space.
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility.
R-Tuning: Regularized Prompt Tuning in Open-Set Scenarios
In realistic open-set scenarios where labels of a part of testing data are totally unknown, current prompt methods on vision-language (VL) models always predict the unknown classes as the downstream training classes.
Rethinking Visual Prompt Learning as Masked Visual Token Modeling
To explore prompt learning on the generative pre-trained visual model as well as keeping the task consistency, we propose Visual Prompt learning as masked visual Token Modeling (VPTM) to transform the downstream visual classification into the pre-trained masked visual token prediction.
ARS-DETR: Aspect Ratio Sensitive Oriented Object Detection with Transformer
Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
Energy-based Out-of-Distribution Detection for Graph Neural Networks
This paves a way for a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
Industrial SAT formula generation is a critical yet challenging task for heuristic development and the surging learning-based methods in practical SAT applications.
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
Real-world data generation often involves complex inter-dependencies among instances, violating the IID-data hypothesis of standard learning paradigms and posing a challenge for uncovering the geometric structures for learning desired instance representations.
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling
Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving.
Distilling Focal Knowledge From Imperfect Expert for 3D Object Detection
Specifically, a set of queries are leveraged to locate the instance-level areas for masked feature generation, to intensify feature representation ability in these areas.
Deep Learning of Partial Graph Matching via Differentiable Top-K
Graph matching (GM) aims at discovering node matching between graphs, by maximizing the node- and edge-wise affinities between the matched elements.
Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture with additional message passing layers to allow features to flow across nodes.
Localized Contrastive Learning on Graphs
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach
Deep neural networks still struggle on long-tailed image datasets, and one of the reasons is that the imbalance of training data across categories leads to the imbalance of trained model parameters.
Ranked #19 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs.
Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks
We study a new paradigm of knowledge transfer that aims at encoding graph topological information into graph neural networks (GNNs) by distilling knowledge from a teacher GNN model trained on a complete graph to a student GNN model operating on a smaller or sparser graph.
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
End-to-End Context-Aided Unicity Matching for Person Re-identification
Based on a natural assumption that images belonging to the same person identity should not match with images belonging to multiple different person identities across views, called the unicity of person matching on the identity level, we propose an end-to-end person unicity matching architecture for learning and refining the person matching relations.
Learning Universe Model for Partial Matching Networks over Multiple Graphs
We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in another graph and vice versa.
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection
Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection.
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning
In particular, we propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously, which is called ST-P3.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step.
Ranked #2 on
Autonomous Driving
on CARLA Leaderboard
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot
Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design.
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source domain, leading to adapting unnecessary source specific knowledge to the target domain, i. e., biased domain adaptation.
CEP3: Community Event Prediction with Neural Point Process on Graph
Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs. However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next.
HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding
Based on these observations, we propose Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges.
DOTIN: Dropping Task-Irrelevant Nodes for GNNs
Scalability is an important consideration for deep graph neural networks.
MMRotate: A Rotated Object Detection Benchmark using PyTorch
We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning.
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.
Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time.
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data.
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations.
EAutoDet: Efficient Architecture Search for Object Detection
In contrast, this paper introduces an efficient framework, named EAutoDet, that can discover practical backbone and FPN architectures for object detection in 1. 4 GPU-days.
Ranked #26 on
Object Detection In Aerial Images
on DOTA
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
Ranked #5 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Object Localization under Single Coarse Point Supervision
In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.
A Survey for Solving Mixed Integer Programming via Machine Learning
This paper surveys the trend of leveraging machine learning to solve mixed integer programming (MIP) problems.
Machine Learning Methods in Solving the Boolean Satisfiability Problem
This paper reviews the recent literature on solving the Boolean satisfiability problem (SAT), an archetypal NP-complete problem, with the help of machine learning techniques.
Learning Neural Hamiltonian Dynamics: A Methodological Overview
The past few years have witnessed an increased interest in learning Hamiltonian dynamics in deep learning frameworks.
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows.
From Quantum Graph Computing to Quantum Graph Learning: A Survey
Quantum computing (QC) is a new computational paradigm whose foundations relate to quantum physics.
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.
Transformers in Time Series: A Survey
From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.
Handling Distribution Shifts on Graphs: An Invariance Perspective
There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
In this paper, we introduce neural networks into the ranking recovery problem by proposing the so-called GNNRank, a trainable GNN-based framework with digraph embedding.
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
Decompositional Quantum Graph Neural Network
The architecture is based on a novel mapping from real-world data to Hilbert space.
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond
We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models.
Ranked #3 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Optimal LED Spectral Multiplexing for NIR2RGB Translation
The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LED diodes, usually centered at 850nm or 940nm, for scene illumination.
Align Representations With Base: A New Approach to Self-Supervised Learning
Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives.
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems.
AlphaRotate: A Rotation Detection Benchmark using TensorFlow
AlphaRotate is an open-source Tensorflow benchmark for performing scalable rotation detection on various datasets.
On Joint Learning for Solving Placement and Routing in Chip Design
To achieve end-to-end placement learning, we first propose a joint learning method termed by DeepPlace for the placement of macros and standard cells, by the integration of reinforcement learning with a gradient based optimization scheme.
ZARTS: On Zero-order Optimization for Neural Architecture Search
Also, we search on the search space of DARTS to compare with peer methods, and our discovered architecture achieves 97. 54% accuracy on CIFAR-10 and 75. 7% top-1 accuracy on ImageNet, which are state-of-the-art performance.
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach
We target open-world feature extrapolation problem where the feature space of input data goes through expansion and a model trained on partially observed features needs to handle new features in test data without further retraining.
DAAS: Differentiable Architecture and Augmentation Policy Search
Neural architecture search (NAS) has been an active direction of automatic machine learning (Auto-ML), aiming to explore efficient network structures.
$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK
Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network.
Explaining Point Processes by Learning Interpretable Temporal Logic Rules
We propose a principled method to learn a set of human-readable logic rules to explain temporal point processes.
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
Zero-CL: Instance and Feature decorrelation for negative-free symmetric contrastive learning
The proposed two methods (FCL, ICL) can be combined synthetically, called Zero-CL, where ``Zero'' means negative samples are \textbf{zero} relevant, which allows Zero-CL to completely discard negative pairs i. e., with \textbf{zero} negative samples.
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.
Nonlinear ICA Using Volume-Preserving Transformations
Nonlinear ICA is a fundamental problem in machine learning, aiming to identify the underlying independent components (sources) from data which is assumed to be a nonlinear function (mixing function) of these sources.
On the Expressiveness, Predictability and Interpretability of Neural Temporal Point Processes
Despite the fast advance in neural temporal point processes (NTPP) which enjoys high model capacity, there are still some standing gaps to fill including model expressiveness, predictability, and interpretability, especially with the wide application of event sequence modeling.
Learning mixture of neural temporal point processes for event sequence clustering
To fill the gap, we propose Mixture of Neural Temporal Point Processes (NTPP-MIX), a general framework that can utilize many existing NTPPs for event sequence clustering.
Adversarial Robustness via Adaptive Label Smoothing
We first show both theoretically and empirically that strong smoothing in AT increases local smoothness of the loss surface which is beneficial for robustness but sacrifices the training loss which influences the accuracy of samples near the decision boundary.
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items.
RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection
We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration.
Dynamic Modeling of Hand-Object Interactions via Tactile Sensing
This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.
Monocular Visual Analysis for Electronic Line Calling of Tennis Games
We propose a monocular vision technology based ELC method.
UniRE: A Unified Label Space for Entity Relation Extraction
Entities and relations are represented by squares and rectangles in the table.
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
SelfSAGCN: Self-Supervised Semantic Alignment for Graph Convolution Network
Specifically, the Identity Aggregation is applied to extract semantic features from labeled nodes, the Semantic Alignment is utilized to align node features obtained from different aspects using the class central similarity.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
Ranked #10 on
Object Detection In Aerial Images
on DOTA
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Based on this observation, considering a necessary condition of IID generation that the inverse samples from target data should also be IID in the source distribution, we propose a new loss to encourage the closeness between inverse samples of real data and the Gaussian source in latent space to regularize the generation to be IID from the target distribution.
Scalable and Explainable 1-Bit Matrix Completion via Graph Signal Learning
One-bit matrix completion is an important class of positiveunlabeled (PU) learning problems where the observations consist of only positive examples, eg, in top-N recommender systems.
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework.
Tuning IR-cut Filter for Illumination-aware Spectral Reconstruction from RGB
To reconstruct spectral signals from multi-channel observations, in particular trichromatic RGBs, has recently emerged as a promising alternative to traditional scanning-based spectral imager.
Generalizing Face Forgery Detection with High-frequency Features
The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective.
Learning Comprehensive Motion Representation for Action Recognition
For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame.
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder
Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model.
Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting
Time-series is ubiquitous across applications, such as transportation, finance and healthcare.
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Ranked #12 on
Object Detection In Aerial Images
on DOTA
Cognitive Visual Inspection Service for LCD Manufacturing Industry
This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry.
GSdyn: Learning training dynamics via online Gaussian optimization with gradient states
Bayesian optimization, whose efficiency for automatic hyperparameter tuning has been verified over the decade, still faces a standing dilemma between massive consumption of time and suboptimal search results.
Learning Latent Topology for Graph Matching
Graph matching (GM) has been traditionally modeled as a deterministic optimization problem characterized by an affinity matrix under pre-defined graph topology.
Self-supervised Disentangled Representation Learning
In our experiments, we find that even the data is only augmented along a few latent variables, more latent variables can be identified, and adding a small noise in data space can stabilize this outcome.
InvertGAN: Reducing mode collapse with multi-dimensional Gaussian Inversion
Generative adversarial networks have shown their ability in capturing high-dimensional complex distributions and generating realistic data samples e. g. images.
Self-supervised representation learning via adaptive hard-positive mining
Despite their success in perception over the last decade, deep neural networks are also known ravenous to labeled data for training, which limits their applicability to real-world problems.
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
Adversarial Learning for Robust Deep Clustering
Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
This paper considers the setting of jointly matching and clustering multiple graphs belonging to different groups, which naturally rises in many realistic problems.
Ranked #2 on
Graph Matching
on Willow Object Class
The Diversified Ensemble Neural Network
Ensemble is a general way of improving the accuracy and stability of learning models, especially for the generalization ability on small datasets.
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.
ROME: Robustifying Memory-Efficient NAS via Topology Disentanglement and Gradient Accumulation
Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory.
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.
Ranked #25 on
Object Detection In Aerial Images
on DOTA
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR.
Distributionally Robust Learning for Uncertainty Calibration under Domain Shift
We detect such domain shifts through the use of a binary domain classifier and integrate it with the task network and train them jointly end-to-end.
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators
We call this approach DARTS-.
Ranked #19 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Fusing Motion Patterns and Key Visual Information for Semantic Event Recognition in Basketball Videos
Based on the observations, we propose a scheme to fuse global and local motion patterns (MPs) and key visual information (KVI) for semantic event recognition in basketball videos.
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
Various point neural networks have been developed with isotropic filters or using weighting matrices to overcome the structure inconsistency on point clouds.
SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects.
Ranked #29 on
Object Detection In Aerial Images
on DOTA
Learning Local Neighboring Structure for Robust 3D Shape Representation
Mesh is a powerful data structure for 3D shapes.
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
GFTE: Graph-based Financial Table Extraction
Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison.
On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited
For the resulting circularly distributed angle classification problem, we first devise a Circular Smooth Label technique to handle the periodicity of angle and increase the error tolerance to adjacent angles.
Ranked #33 on
Object Detection In Aerial Images
on DOTA
Learning Structured Communication for Multi-agent Reinforcement Learning
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem
In spite of the success of existing meta reinforcement learning methods, they still have difficulty in learning a meta policy effectively for RL problems with sparse reward.
Learning deep graph matching with channel-independent embedding and Hungarian attention
Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete.
Ranked #12 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
We also show how to extend our network to hypergraph matching, and matching of multiple graphs.
Ranked #3 on
Graph Matching
on SPair-71k
Towards Better Understanding of Disentangled Representations via Mutual Information
Most existing works on disentangled representation learning are solely built upon an marginal independence assumption: all factors in disentangled representations should be statistically independent.
Decoding Spiking Mechanism with Dynamic Learning on Neuron Population
Our model is aimed to reconstruct neuron information while inferring representations of neuron spiking states.
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning
To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network.
Learning Modulated Loss for Rotated Object Detection
Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss as the loss function.
Ranked #39 on
Object Detection In Aerial Images
on DOTA
Pingan Smart Health and SJTU at COIN - Shared Task: utilizing Pre-trained Language Models and Common-sense Knowledge in Machine Reading Tasks
We have experimented both (a) improving the fine-tuning of pre-trained language models on a task with a small dataset size, by leveraging datasets of similar tasks; and (b) incorporating the distributional representations of a KG onto the representations of pre-trained language models, via simply concatenation or multi-head attention.
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations.
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features.
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Temporal point process is an expressive tool for modeling event sequences over time.
Deep Spectral Clustering using Dual Autoencoder Network
In this paper, we propose a joint learning framework for discriminative embedding and spectral clustering.
Learning Combinatorial Embedding Networks for Deep Graph Matching
In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises.
Generalizing Graph Matching beyond Quadratic Assignment Model
Graph matching has received persistent attention over decades, which can be formulated as a quadratic assignment problem (QAP).
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
Specifically, a sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects.
Ranked #43 on
Object Detection In Aerial Images
on DOTA
Collaborative Filtering with Stability
Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks.
Incremental Multi-graph Matching via Diversity and Randomness based Graph Clustering
In this paper, we present an incremental multi-graph matching approach, which deals with the arriving graph utilizing the previous matching results under the global consistency constraint.
Decoupled Learning for Factorial Marked Temporal Point Processes
In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i. e. a marker.
tau-FPL: Tolerance-Constrained Learning in Linear Time
Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.
Attention based convolutional neural network for predicting RNA-protein binding sites
In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences.
Joint Cuts and Matching of Partitions in One Graph
As two fundamental problems, graph cuts and graph matching have been investigated over decades, resulting in vast literature in these two topics respectively.
Transductive Non-linear Learning for Chinese Hypernym Prediction
Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc.
Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks
In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena.
Deep Extreme Multi-label Learning
Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data.
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied.
Learning Correspondence Structures for Person Re-identification
We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair.
Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data
In order to efficiently solve the proposed framework, we propose a parameter-free and scalable optimization algorithm by effectively exploring the sparse and low-rank structure of the tensor.
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories
Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.
