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Found 115 papers, 44 papers with code
Group R-CNN for Weakly Semi-supervised Object Detection with Points
The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation.
TransRank: Self-supervised Video Representation Learning via Ranking-based Transformation Recognition
To mitigate this problem, we developed TransRank, a unified framework for recognizing Transformations in a Ranking formulation.
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.
ROMA: Cross-Domain Region Similarity Matching for Unpaired Nighttime Infrared to Daytime Visible Video Translation
To be specific, ROMA could efficiently translate the unpaired nighttime infrared videos into fine-grained daytime visible ones, meanwhile maintain the spatiotemporal consistency via matching the cross-domain region similarity.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking
To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
In particular on KITTI-STEP, the simple method can boost almost 12\% relative improvements over previous methods.
StructToken : Rethinking Semantic Segmentation with Structural Prior
From a perspective on semantic segmentation as per-pixel classification, the previous deep learning-based methods learn the per-pixel representation first through an encoder and a decoder head and then classify each pixel representation to a specific category to obtain the semantic masks.
Dense Siamese Network
It also extracts a batch of region embeddings that correspond to some sub-regions in the overlapped area to be contrasted for region consistency.
APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic Text Image Generation
Style-guided text image generation tries to synthesize text image by imitating reference image's appearance while keeping text content unaltered.
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention.
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor.
No Free Lunch Theorem for Security and Utility in Federated Learning
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms.
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances.
PPA: Preference Profiling Attack Against Federated Learning
In this work, we reveal a new type of privacy inference attack, coined Preference Profiling Attack (PPA), that accurately profiles private preferences of a local user.
Efficient Data-Plane Memory Scheduling for In-Network Aggregation
However, switch memory is scarce compared to the volume of gradients transmitted in distributed training.
Efficient DNN Training with Knowledge-Guided Layer Freezing
To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers.
OCSampler: Compressing Videos to One Clip with Single-step Sampling
Recent works prefer to formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance, while we present a new paradigm of learning instance-specific video condensation policies to select informative frames for representing the entire video only in a single step.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the target domain.
Automatic Configuration for Optimal Communication Scheduling in DNN Training
ByteScheduler partitions and rearranges tensor transmissions to improve the communication efficiency of distributed Deep Neural Network (DNN) training.
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.
Ranked #1 on
Referring Expression Segmentation
on RefCOCO+ testA
Few-Shot Object Detection via Association and DIscrimination
1) In the association step, in contrast to implicitly leveraging multiple base classes, we construct a compact novel class feature space via explicitly imitating a specific base class feature space.
DBIA: Data-free Backdoor Injection Attack against Transformer Networks
In this paper, we propose DBIA, a novel data-free backdoor attack against the CV-oriented transformer networks, leveraging the inherent attention mechanism of transformers to generate triggers and injecting the backdoor using the poisoned surrogate dataset.
Attacking Video Recognition Models with Bullet-Screen Comments
On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90\% fooling rate when attacking three mainstream video recognition models, while only occluding \textless 8\% areas in the video.
The Nuts and Bolts of Adopting Transformer in GANs
Transformer becomes prevalent in computer vision, especially for high-level vision tasks.
Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction
However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity.
Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI
Inspired by the mechanism of human visual attention, in this paper, we propose a novel method of reconstructing visual stimulus images, which first decodes the distribution of visual attention from fMRI, and then reconstructs the visual images guided by visual attention.
Temporal RoI Align for Video Object Recognition
In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal RoI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity.
Ranked #1 on
Video Instance Segmentation
on YouTube-VIS
MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving
By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.
Towards Balanced Learning for Instance Recognition
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
Category-Level 6D Object Pose Estimation via Cascaded Relation and Recurrent Reconstruction Networks
Furthermore, we design a recurrent reconstruction network for iterative residual refinement to progressively improve the reconstruction and correspondence estimations from coarse to fine.
Practical and Secure Federated Recommendation with Personalized Masks
Federated masked matrix factorization could protect the data privacy in federated recommender systems without sacrificing efficiency or efficacy.
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning
In this paper, we present Aegis, a trusted, automatic, and accurate verification framework to verify the security of VFL jobs.
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction.
I2V-GAN: Unpaired Infrared-to-Visible Video Translation
Human vision is often adversely affected by complex environmental factors, especially in night vision scenarios.
HAFLO: GPU-Based Acceleration for Federated Logistic Regression
In recent years, federated learning (FL) has been widely applied for supporting decentralized collaborative learning scenarios.
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
Data-driven approaches have been applied to many problems in urban computing.
K-Net: Towards Unified Image Segmentation
The framework, named K-Net, segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class.
Ranked #4 on
Panoptic Segmentation
on COCO test-dev
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.
Learning To Identify Correct 2D-2D Line Correspondences on Sphere
In contrast, we propose the first approach suitable for both structured and unstructured scenes.
WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection
The performance of object detection, to a great extent, depends on the availability of large annotated datasets.
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks
Compared to the method of retraining from scratch, our approach can achieve 99. 0%, 95. 0%, 91. 9%, 96. 7%, 74. 1% accuracy rates and 66. 7$\times$, 75. 0$\times$, 33. 3$\times$, 29. 4$\times$, 13. 7$\times$ speedups on the MNIST, SVHN, CIFAR-10, Purchase, and ImageNet datasets, respectively.
Revisiting Skeleton-based Action Recognition
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks
Most existing solutions embed backdoors in DNN model training such that DNN ownership can be verified by triggering distinguishable model behaviors with a set of secret inputs.
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems
In this paper, we present our systematization of knowledge for ASR security and provide a comprehensive taxonomy for existing work based on a modularized workflow.
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
In this competition, we set up three tasks, namely, Scanned Receipt Text Localisation (Task 1), Scanned Receipt OCR (Task 2) and Key Information Extraction from Scanned Receipts (Task 3).
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting
Although Transformer has made breakthrough success in widespread domains especially in Natural Language Processing (NLP), applying it to time series forecasting is still a great challenge.
Fast and Reliable Probabilistic Face Embeddings in the Wild
In addition, an identification preserving loss is proposed to improve the discriminative of the MLS metric, and a multi-layer feature fusion module is proposed to improve the neural network's uncertainty estimation ability.
Learning Icosahedral Spherical Probability Map Based on Bingham Mixture Model for Vanishing Point Estimation
Based on this map, we can detect all the VPs.
SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation
The prior adaptation intrinsically associates the adopted prior with different objects, from which we can accurately reconstruct the 3D canonical model of the specific object for pose estimation.
UMLE: Unsupervised Multi-discriminator Network for Low Light Enhancement
To address this problem, we propose a real-time unsupervised generative adversarial network (GAN) containing multiple discriminators, i. e. a multi-scale discriminator, a texture discriminator, and a color discriminator.
A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews
In this work, we propose a Hierarchical Reasoning Graph Neural Network (HRGNN) for the automatic assessment of question-answer pairs.
Positional Encoding as Spatial Inductive Bias in GANs
In this work, taking SinGAN and StyleGAN2 as examples, we show that such capability, to a large extent, is brought by the implicit positional encoding when using zero padding in the generators.
CARAFE++: Unified Content-Aware ReAssembly of FEatures
Feature reassembly, i. e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e. g., residual networks and feature pyramids.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning
As an innovative solution for privacy-preserving machine learning (ML), federated learning (FL) is attracting much attention from research and industry areas.
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting
The heavy tail and skewness characteristics of such distributions in the spectral domain allow to capture long-range covariance of the signal in the time domain.
Possible multi-orbital ground state in CeCu$_2$Si$_2$
The crystal-field ground state wave function of CeCu$_2$Si$_2$ has been investigated with linear polarized $M$-edge x-ray absorption spectroscopy from 250mK to 250K, thus covering the superconducting ($T_{\text{c}}$=0. 6K), the Kondo ($T_{\text{K}}$$\approx$20K) as well as the Curie-Weiss regime.
Strongly Correlated Electrons
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.
Seesaw Loss for Long-Tailed Instance Segmentation
Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories.
Domain-specific Communication Optimization for Distributed DNN Training
Communication overhead poses an important obstacle to distributed DNN training and draws increasing attention in recent years.
FPGA-Based Hardware Accelerator of Homomorphic Encryption for Efficient Federated Learning
Our framework implements the representative Paillier homomorphic cryptosystem with high level synthesis for flexibility and portability, with careful optimization on the modular multiplication operation in terms of processing clock cycle, resource usage and clock frequency.
DS-Sync: Addressing Network Bottlenecks with Divide-and-Shuffle Synchronization for Distributed DNN Training
However, due to the global synchronization nature, its performance can be significantly influenced by network bottlenecks caused by either static topology heterogeneity or dynamic bandwidth contentions.
Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation
Extracting the desired speech from a mixture is a meaningful and challenging task.
Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet
Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and far-end signal.
Neural Data-to-Text Generation with Dynamic Content Planning
We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.
Feature Pyramid Grids
Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale.
Vanishing Point Guided Natural Image Stitching
Recently, works on improving the naturalness of stitching images gain more and more extensive attention.
Quantifying the Performance of Federated Transfer Learning
The scarcity of data and isolated data islands encourage different organizations to share data with each other to train machine learning models.
Unified Approach to Witness Nonentanglement-Breaking Quantum Channels
The ability of quantum devices to preserve or distribute entanglement is essential in employing quantum technologies.
Quantum Physics
Side-Aware Boundary Localization for More Precise Object Detection
To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket.
Towards Security Threats of Deep Learning Systems: A Survey
In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude some findings in multiple views.
An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
This paper introduces an end-to-end audio classification system based on raw waveforms and mix-training strategy.
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.
Ranked #24 on
Object Detection In Aerial Images
on DOTA
Real-time Scene Text Detection with Differentiable Binarization
Recently, segmentation-based methods are quite popular in scene text detection, as the segmentation results can more accurately describe scene text of various shapes such as curve text.
Ranked #5 on
Scene Text Detection
on SCUT-CTW1500
(F-Measure metric)
Interpretable Encrypted Searchable Neural Networks
In cloud security, traditional searchable encryption (SE) requires high computation and communication overhead for dynamic search and update.
Multi-owner Secure Encrypted Search Using Searching Adversarial Networks
To address this, this paper proposes secure and efficient multi-keyword ranked search over encrypted cloud data for multi-owner model based on searching adversarial networks.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
Secure Federated Matrix Factorization
The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.
Extracting Symptoms and their Status from Clinical Conversations
This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status.
CARAFE: Content-Aware ReAssembly of FEatures
CARAFE introduces little computational overhead and can be readily integrated into modern network architectures.
Prime Sample Attention in Object Detection
Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector.
Libra R-CNN: Towards Balanced Learning for Object Detection
In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.
Ranked #130 on
Object Detection
on COCO test-dev
Hybrid Task Cascade for Instance Segmentation
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
Ranked #27 on
Instance Segmentation
on COCO test-dev
Region Proposal by Guided Anchoring
State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.
Ranked #1 on
Region Proposal
on COCO test-dev
Seeing isn't Believing: Practical Adversarial Attack Against Object Detectors
In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors.
Generalized Content-Preserving Warps for Image Stitching
Content-Preserving Warping (CPW) is a typical method to deal with this issue, in which geometric and photometric constraints are imposed to guide the warping process.
Multiple Combined Constraints for Image Stitching
In this paper, geometric and photometric constraints are combined to improve the alignment quality, which is based on the observation that these two kinds of constraints are complementary.
Multi-Output Convolution Spectral Mixture for Gaussian Processes
The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel.
Multitask Gaussian Process with Hierarchical Latent Interactions
The function interaction is modeled by using cross convolution of latent functions.
Double Supervised Network with Attention Mechanism for Scene Text Recognition
In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition.
Novel Compressible Adaptive Spectral Mixture Kernels for Gaussian Processes with Sparse Time and Phase Delay Structures
By verifying the presence of dependencies between function components using Gaussian conditionals and posterior covariance, we first propose a new SM kernel variant with a time and phase delay dependency structure (SMD) and then provide a structure adaptation (SA) algorithm for the SMD.
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models.
Ranked #28 on
Question Answering
on SQuAD1.1 dev
Optimizing Video Object Detection via a Scale-Time Lattice
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
Understanding Membership Inferences on Well-Generalized Learning Models
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition
For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Cryptography and Security
Fused Text Segmentation Networks for Multi-oriented Scene Text Detection
In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective.
Ranked #10 on
Scene Text Detection
on MSRA-TD500
Video Object Segmentation with Re-identification
Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module.
Discover and Learn New Objects from Documentaries
Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task.
Convolutional Neural Networks for Page Segmentation of Historical Document Images
This paper presents a Convolutional Neural Network (CNN) based page segmentation method for handwritten historical document images.
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
Convolutional Regression for Visual Tracking
In this paper, we propose a Convolutional Regression framework for visual tracking (CRT).
Context-aware System Service Call-oriented Symbolic Execution of Android Framework with Application to Exploit Generation
We present novel ideas and techniques to resolve the challenges, and have built the first system for symbolic execution of Android Framework.
Cryptography and Security Software Engineering
Once for All: a Two-flow Convolutional Neural Network for Visual Tracking
As a result, the model need to be initialized and retrained for different objects.
A Novel Scene Text Detection Algorithm Based On Convolutional Neural Network
In this paper, we propose a CNN based scene text detection algorithm with a new text region extractor.
A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine
The proposed Chinese text recognition algorithm is evaluated with two Chinese text datasets.
Distributed Representations of Words and Phrases and their Compositionality
Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.
Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector representations of words from very large data sets.
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance.
Improved Successive Cancellation Decoding of Polar Codes
As improved versions of successive cancellation (SC) decoding algorithm, successive cancellation list (SCL) decoding and successive cancellation stack (SCS) decoding are used to improve the finite-length performance of polar codes.
Information Theory Information Theory
Building high-level features using large scale unsupervised learning
For example, is it possible to learn a face detector using only unlabeled images?
