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Found 114 papers, 40 papers with code
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
RelationRS: Relationship Representation Network for Object Detection in Aerial Images
To address these issues, we propose a relationship representation network for object detection in aerial images (RelationRS): 1) Firstly, multi-scale features are fused and enhanced by a dual relationship module (DRM) with conditional convolution.
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks.
Combing Policy Evaluation and Policy Improvement in a Unified f-Divergence Framework
In this paper, we start from studying the f-divergence between learning policy and sampling policy and derive a novel DRL framework, termed f-Divergence Reinforcement Learning (FRL).
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning
Ensemble reinforcement learning (RL) aims to mitigate instability in Q-learning and to learn a robust policy, which introduces multiple value and policy functions.
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Variational autoencoders (VAEs), as an important aspect of generative models, have received a lot of research interests and reached many successful applications.
Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19
Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours.
Iterative Self-consistent Parallel Magnetic Resonance Imaging Reconstruction based on Nonlocal Low-Rank Regularization
Recent methods have exploited the nonlocal self-similarity (NSS) of images by imposing nonlocal low-rankness of similar patches to achieve a superior performance.
Unified Regularity Measures for Sample-wise Learning and Generalization
Fundamental machine learning theory shows that different samples contribute unequally both in learning and testing processes.
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle
UGV-KPNet is computationally efficient with a small number of parameters and provides pixel-level accurate keypoints detection results in real-time.
Curvature Graph Neural Network
And we propose Curvature Graph Neural Network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural property of graph curvature.
Communication Efficient SGD via Gradient Sampling With Bayes Prior
Different from all of them, we regard large and small gradients selection as the exploitation and exploration of gradient information, respectively.
FNAS: Uncertainty-Aware Fast Neural Architecture Search
Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times.
A Novel Unified Stereo Stimuli based Binocular Eye-Tracking System for Accurate 3D Gaze Estimation
In this paper, we present a novel noncontact technique for the PoG estimation in a stereoscopic environment, which integrates a 3D stereoscopic display system and an eye-tracking system.
Role-Aware Modeling for N-ary Relational Knowledge Bases
N-ary relational knowledge bases (KBs) represent knowledge with binary and beyond-binary relational facts.
Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval
Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy.
Self-supervised Video Representation Learning by Context and Motion Decoupling
A key challenge in self-supervised video representation learning is how to effectively capture motion information besides context bias.
High-efficiency Euclidean-based Models for Low-dimensional Knowledge Graph Embeddings
Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space.
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation
In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains.
ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation
Interpreting human actions requires understanding the spatial and temporal context of the scenes.
Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning
However, scaling up the classification task from thousands of semantic labels to millions of instance labels brings specific challenges including 1) the large-scale softmax computation; 2) the slow convergence due to the infrequent visiting of instance samples; and 3) the massive number of negative classes that can be noisy.
Deep Image Retrieval: A Survey
More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies.
Self-supervised Temporal Learning
Inspired by spatial-based contrastive SSL, we show that significant improvement can be achieved by a proposed temporal-based contrastive learning approach, which includes three novel and efficient modules: temporal augmentations, temporal memory bank and SSTL loss.
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning
Further, a life-long knowledge pool together with a block similarity function is proposed to utilize the lifelong parameter knowledge and reduces the searching time by 2 times.
Switchable K-Class Hyperplanes for Noise-Robust Representation Learning
When collaborated with the popular ArcFace on million-level data representation learning, we found that the switchable manner in SKH can effectively eliminate the gradient conflict generated by real-world label noise on a single K-class hyperplane.
Precision test of statistical dynamics with state-to-state ultracold chemistry
Chemical reactions represent a class of quantum problems that challenge both the current theoretical understanding and computational capabilities.
Chemical Physics Atomic Physics Quantum Physics
High Fermi velocities and small cyclotron masses in LaAlGe
We report quantum oscillation measurements of LaAlGe, a Lorentz-violating type-II Weyl semimetal with tilted Weyl cones.
Mesoscale and Nanoscale Physics Materials Science
Electronic properties of InAs/EuS/Al hybrid nanowires
We study the electronic properties of InAs/EuS/Al heterostructures as explored in a recent experiment [S. Vaitiekenas \emph{et al.}, Nat.
Mesoscale and Nanoscale Physics
On the Exploration of Incremental Learning for Fine-grained Image Retrieval
On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting.
MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings
Link prediction based on knowledge graph embeddings (KGE) aims to predict new triples to automatically construct knowledge graphs (KGs).
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary
In this work, we deviate from recent, popular task settings and consider the problem under an autonomous vehicle scenario.
Ranked #2 on
Referring Expression Comprehension
on Talk2Car
Image Retrieval for Structure-from-Motion via Graph Convolutional Network
Conventional image retrieval techniques for Structure-from-Motion (SfM) suffer from the limit of effectively recognizing repetitive patterns and cannot guarantee to create just enough match pairs with high precision and high recall.
Discriminability Distillation in Group Representation Learning
We show the discrimiability knowledge has good properties that can be distilled by a light-weight distillation network and can be generalized on the unseen target set.
Single Cell Transcriptome Research in Human Placenta
Human placenta is a complex and heterogeneous organ interfacing between the mother and the fetus that supports fetal development.
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively.
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks
Then, the vacant channels in the selected segment will be aggregated for satisfying the user requirement.
Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020
Specifically, in order to generate high-quality proposals, we consider several factors including the video feature encoder, the proposal generator, the proposal-proposal relations, the scale imbalance, and ensemble strategy.
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases
With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a. k. a knowledge graph) with powerful tensor decomposition related methods.
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
Spatio-Temporal Action Localization
Temporal Action Localization
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.
Ranked #2 on
Spatio-Temporal Action Localization
on AVA-Kinetics
(using extra training data)
A novel multimodal approach for hybrid brain-computer interface
It has been already shown in literature that the hybrid of EEG and NIRS has better results than their respective individual signals.
Human-Computer Interaction Signal Processing
Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusion
To solve this problem, this paper presents a new homogeneous transformation model termed deep homogeneous feature fusion (DHFF) based on image style transfer (IST).
Anisotropic Convolutional Networks for 3D Semantic Scene Completion
In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely.
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #1 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods.
1st Place Solutions for OpenImage2019 -- Object Detection and Instance Segmentation
Given such good instance bounding box, we further design a simple instance-level semantic segmentation pipeline and achieve the 1st place on the segmentation challenge.
KPNet: Towards Minimal Face Detector
The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors.
Revisiting the Sibling Head in Object Detector
The ``shared head for classification and localization'' (sibling head), firstly denominated in Fast RCNN~\cite{girshick2015fast}, has been leading the fashion of the object detection community in the past five years.
Ranked #43 on
Object Detection
on COCO test-dev
(using extra training data)
Top-1 Solution of Multi-Moments in Time Challenge 2019
In this technical report, we briefly introduce the solutions of our team 'Efficient' for the Multi-Moments in Time challenge in ICCV 2019.
DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images
However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information.
3D Gated Recurrent Fusion for Semantic Scene Completion
This paper tackles the problem of data fusion in the semantic scene completion (SSC) task, which can simultaneously deal with semantic labeling and scene completion.
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years.
Depth Based Semantic Scene Completion with Position Importance Aware Loss
We propose PALNet, a novel hybrid network for SSC based on single depth.
Temporal Interlacing Network
In this way, a heavy temporal model is replaced by a simple interlacing operator.
Understanding the mesoscopic scaling patterns within cities
Understanding quantitative relationships between urban elements is crucial for a wide range of applications.
Physics and Society
Search to Distill: Pearls are Everywhere but not the Eyes
Standard Knowledge Distillation (KD) approaches distill the knowledge of a cumbersome teacher model into the parameters of a student model with a pre-defined architecture.
Learning Where to Focus for Efficient Video Object Detection
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur.
A game method for improving the interpretability of convolution neural network
The reason is, we believe that: the network is essentially a perceptual model.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Differentiable Kernel Evolution
This paper proposes a differentiable kernel evolution (DKE) algorithm to find a better layer-operator for the convolutional neural network.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation.
Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
Using spiking speech and image recognition datasets, we demonstrate the feasibility of supporting large time compression ratios of up to 16x, delivering up to 15. 93x, 13. 88x, and 86. 21x improvements in throughput, energy dissipation, the tradeoffs between hardware area, runtime, energy, and classification accuracy, respectively based on different spike codes on a Xilinx Zynq-7000 FPGA.
Towards Flops-constrained Face Recognition
Large scale face recognition is challenging especially when the computational budget is limited.
In defense of OSVOS
Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i. e. online fine-tuning on the target instance.
Exploiting temporal consistency for real-time video depth estimation
The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion.
Ranked #4 on
Monocular Depth Estimation
on Mid-Air Dataset
Modeling Event Propagation via Graph Biased Temporal Point Process
Temporal point process is widely used for sequential data modeling.
Scalable Place Recognition Under Appearance Change for Autonomous Driving
Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.
Use of OWL and Semantic Web Technologies at Pinterest
In this paper, we present the engineering of an OWL ontology---the Pinterest Taxonomy---that forms the core of Pinterest's knowledge graph, the Pinterest Taste Graph.
From Caesar Cipher to Unsupervised Learning: A New Method for Classifier Parameter Estimation
To remove the need for the parallel training corpora has practical significance for real-world applications, and it is one of the main goals of unsupervised learning.
Extracting human emotions at different places based on facial expressions and spatial clustering analysis
In this study, a novel framework for extracting human emotions from large-scale georeferenced photos at different places is proposed.
Knowledge Distillation via Route Constrained Optimization
However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss.
Correlation Congruence for Knowledge Distillation
Most teacher-student frameworks based on knowledge distillation (KD) depend on a strong congruent constraint on instance level.
Conditional Adversarial Generative Flow for Controllable Image Synthesis
Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions.
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene completion (SSC).
Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes
As the post-processing step for object detection, non-maximum suppression (GreedyNMS) is widely used in most of the detectors for many years.
Derivative Estimation in Random Design
We propose a nonparametric derivative estimation method for random design without having to estimate the regression function.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition.
How to improve the interpretability of kernel learning
In this paper, a quantitative index of the interpretability is proposed and its rationality is proved, and equilibrium problem between the interpretability and the generalization performance is analyzed.
Visual Localization Under Appearance Change: Filtering Approaches
Our approaches rely on local features with an encoding technique to represent an image as a single vector.
How far from automatically interpreting deep learning
In other words, there is a gap between the deep learning model and the cognitive mode.
Gradient Harmonized Single-stage Detector
Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i. e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples.
Ranked #130 on
Object Detection
on COCO test-dev
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
Transductive Centroid Projection for Semi-supervised Large-scale Recognition
It is inspired by the observation of the weights in classification layer (called extit{anchors}) converge to the central direction of each class in hyperspace.
Knowledge Graph Embedding with Entity Neighbors and Deep Memory Network
Knowledge Graph Embedding (KGE) aims to represent entities and relations of knowledge graph in a low-dimensional continuous vector space.
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Talking face generation aims to synthesize a sequence of face images that correspond to a clip of speech.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning
Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process.
MoNet: Deep Motion Exploitation for Video Object Segmentation
In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i. e., frame representation learning and segmentation refinement.
PAD-Net: A Perception-Aided Single Image Dehazing Network
In this work, we investigate the possibility of replacing the $\ell_2$ loss with perceptually derived loss functions (SSIM, MS-SSIM, etc.)
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone.
Exploring Disentangled Feature Representation Beyond Face Identification
This paper proposes learning disentangled but complementary face features with minimal supervision by face identification.
The artificial ecosystem: number soup (part II)
This paper is a follow-up work about the artificial ecosystem model: number soup (Liu and Sumpter, J. Royal Soc.
Region-based Quality Estimation Network for Large-scale Person Re-identification
One of the major restrictions on the performance of video-based person re-id is partial noise caused by occlusion, blur and illumination.
Transferable Semi-supervised Semantic Segmentation
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations.
Learning a Recurrent Residual Fusion Network for Multimodal Matching
A major challenge in matching between vision and language is that they typically have completely different features and representations.
Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection
A key observation is that it is difficult to classify anchors of different sizes with the same set of features.
Ranked #2 on
Region Proposal
on COCO test-dev
Recurrent Scale Approximation for Object Detection in CNN
To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid.
Ranked #5 on
Face Detection
on Annotated Faces in the Wild
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
We then compare the performance of the top winning code available from Kaggle with that of running machine learning clouds from both Azure and Amazon on mlbench.
Quality Aware Network for Set to Set Recognition
In this paper, the quality aware network (QAN) is proposed to confront this problem, where the quality of each sample can be automatically learned although such information is not explicitly provided in the training stage.
Ranked #6 on
Face Verification
on YouTube Faces DB
Unsupervised Sequence Classification using Sequential Output Statistics
Although it is harder to optimize in its functional form, a stochastic primal-dual gradient method is developed to effectively solve the problem.
Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance.
Zoom Out-and-In Network with Recursive Training for Object Proposal
In this paper, we propose a zoom-out-and-in network for generating object proposals.
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Despite recent advances in multi-scale deep representations, their limitations are attributed to expensive parameters and weak fusion modules.
Combinatorial Multi-Armed Bandit with General Reward Functions
Our framework enables a much larger class of reward functions such as the $\max()$ function and nonlinear utility functions.
POI: Multiple Object Tracking with High Performance Detection and Appearance Feature
In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting.
Crafting GBD-Net for Object Detection
The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network
Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline.
Learning Relaxed Deep Supervision for Better Edge Detection
We consider these false positives in the supervision, and are able to achieve high performance for better edge detection.
Modulation Classification via Gibbs Sampling Based on a Latent Dirichlet Bayesian Network
A novel Bayesian modulation classification scheme is proposed for a single-antenna system over frequency-selective fading channels.
Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China
Travel activities have been widely applied to quantify spatial interactions between places, regions and nations.
Physics and Society Social and Information Networks