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Found 179 papers, 48 papers with code
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images.
Metric Learning based Interactive Modulation for Real-World Super-Resolution
Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner.
Deep Generalized Unfolding Networks for Image Restoration
Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation.
Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?"
Learning Weighting Map for Bit-Depth Expansion within a Rational Range
Existing BDE methods have no unified solution for various BDE situations, and directly learn a mapping for each pixel from LBD image to the desired value in HBD image, which may change the given high-order bits and lead to a huge deviation from the ground truth.
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i. e., where to sample and how to reconstruct.
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning
After RRL, the classification head is fine-tuned with global class-balanced classification loss to address the data imbalance issue as well as learn the decision boundary between new and previous classes.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
Series Photo Selection via Multi-view Graph Learning
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos.
Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration
Emerging high-quality face restoration (FR) methods often utilize pre-trained GAN models (\textit{i. e.}, StyleGAN2) as GAN Prior.
NeRFocus: Neural Radiance Field for 3D Synthetic Defocus
Unlike the pinhole, the thin lens refracts rays of a scene point, so its imaging on the sensor plane is scattered as a circle of confusion (CoC).
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
Quantifying Robustness to Adversarial Word Substitutions
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations.
CSformer: Bridging Convolution and Transformer for Compressive Sensing
The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery.
COTReg:Coupled Optimal Transport based Point Cloud Registration
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration.
More is Better: A Novel Multi-view Framework for Domain Generalization
In test stage, to alleviate unstable prediction, we utilize multiple augmented images to yield multi-view prediction, which significantly promotes model reliability via fusing the results of different views of a test image.
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging
Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine.
GenReg: Deep Generative Method for Fast Point Cloud Registration
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
ε-weakened Robustness of Deep Neural Networks
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).
Research on the inverse kinematics prediction of a soft biomimetic actuator via BP neural network
On the other hand, the material model is a complicated system with significant nonlinearity, non-stationarity, and uncertainty, making it challenging to develop an appropriate system model.
Memory-Augmented Deep Unfolding Network for Compressive Sensing
By understanding DUNs from the perspective of the human brain's memory processing, we find there exists two issues in existing DUNs.
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty.
Semantic Segmentation
Semi-supervised Medical Image Segmentation
Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction
Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i. e., generating a subgraph sequence as the trigger and embedding it to the training data.
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging
To better exploit the spatial-temporal correlation among frames and address the problem of information loss between adjacent phases in existing DUNs, we propose to adopt the 3D-CNN prior in our proximal mapping module and develop a novel dense feature map (DFM) strategy, respectively.
Dynamic Attentive Graph Learning for Image Restoration
Specifically, we propose an improved graph model to perform patch-wise graph convolution with a dynamic and adaptive number of neighbors for each node.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Structure Destruction and Content Combination for Face Anti-Spoofing
In pursuit of consolidating the face verification systems, prior face anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types with the assistance of auxiliary supervision.
Multi-Level Contrastive Learning for Few-Shot Problems
Most current applications of contrastive learning benefit only a single representation from the last layer of an encoder. In this paper, we propose a multi-level contrasitive learning approach which applies contrastive losses at different layers of an encoder to learn multiple representations from the encoder.
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing
In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model.
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features.
Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream
Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.
TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.
A Multi-Branch Hybrid Transformer Networkfor Corneal Endothelial Cell Segmentation
Corneal endothelial cell segmentation plays a vital role inquantifying clinical indicators such as cell density, coefficient of variation, and hexagonality.
Weakly Supervised Dense Video Captioning via Jointly Usage of Knowledge Distillation and Cross-modal Matching
This paper proposes an approach to Dense Video Captioning (DVC) without pairwise event-sentence annotation.
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain.
Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach
To overcome this issue and to further improve the recognition performance, we adopt a deep learning approach for underwater target recognition and propose a LOFAR spectrum enhancement (LSE)-based underwater target recognition scheme, which consists of preprocessing, offline training, and online testing.
Super-Resolving Compressed Video in Coding Chain
Scaling and lossy coding are widely used in video transmission and storage.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
ISTA-Net++: Flexible Deep Unfolding Network for Compressive Sensing
While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications.
Thousand to One: Semantic Prior Modeling for Conceptual Coding
To this end, we propose a novel end-to-end semantic prior modeling-based conceptual coding scheme towards extremely low bitrate image compression, which leverages semantic-wise deep representations as a unified prior for entropy estimation and texture synthesis.
COLA-Net: Collaborative Attention Network for Image Restoration
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
A comprehensive survey on point cloud registration
This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight.
Temperature dependent coherence properties of NV ensemble in diamond up to 600K
Nitrogen-vacancy (NV) center in diamond is an ideal candidate for quantum sensors because of its excellent optical and coherence property.
Quantum Physics Mesoscale and Nanoscale Physics
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Then we utilize the fused prototype to guide the final segmentation of the query image.
Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking
With the wide application of blockchain in the financial field, the rise of various types of cybercrimes has brought great challenges to the security of blockchain.
Aurora Guard: Reliable Face Anti-Spoofing via Mobile Lighting System
Face authentication on mobile end has been widely applied in various scenarios.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
Super Resolve Dynamic Scene From Continuous Spike Streams
In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution.
Uncertainty Weighted Offline Reinforcement Learning
Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration.
Revisiting BFfloat16 Training
We ask can we do pure 16-bit training which requires only 16-bit compute units, while still matching the model accuracy attained by 32-bit training.
Multi-Representation Ensemble in Few-Shot Learning
Deep neural networks (DNNs) compute representations in a layer by layer fashion, producing a final representation at the top layer of the pipeline, and classification or regression is made using the final representation.
Counting the Number of Solutions to Constraints
Compared with constraint satisfaction problems, counting problems have received less attention.
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNet
In this paper, a network called Brachial Plexus Multi-instance Segmentation Network (BPMSegNet) is proposed to identify different tissues (nerves, arteries, veins, muscles) in ultrasound images.
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning
Second, the extra unlabeled samples are employed to transfer the knowledge from base classes to novel classes through contrastive learning.
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
Rigid and Articulated Point Registration with Expectation Conditional Maximization
This paper addresses the issue of matching rigid and articulated shapes through probabilistic point registration.
Field-wise Learning for Multi-field Categorical Data
We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.
Time-Series Snapshot Network for Partner Recommendation: A Case Study on OSS
In this paper, we introduce time-series snapshot network (TSSN) which is a mixture network to model the interactions among users and developers.
Social and Information Networks
Conceptual Compression via Deep Structure and Texture Synthesis
To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks.
Multi-layer Feature Aggregation for Deep Scene Parsing Models
The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information.
Ranked #22 on
Semantic Segmentation
on NYU Depth v2
Parameter Efficient Deep Neural Networks with Bilinear Projections
Recent research on deep neural networks (DNNs) has primarily focused on improving the model accuracy.
Distribution-aware Margin Calibration for Medical Image Segmentation
In this paper, we present a novel data-distribution-aware margin calibration method for a better generalization of the mIoU over the whole data-distribution, underpinned by a rigid lower bound.
Dual Attention on Pyramid Feature Maps for Image Captioning
In this paper, we propose to apply dual attention on pyramid image feature maps to fully explore the visual-semantic correlations and improve the quality of generated sentences.
Identification of deep breath while moving forward based on multiple body regions and graph signal analysis
This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera.
AutoBSS: An Efficient Algorithm for Block Stacking Style Search
Neural network architecture design mostly focuses on the new convolutional operator or special topological structure of network block, little attention is drawn to the configuration of stacking each block, called Block Stacking Style (BSS).
Revisiting BFloat16 Training
State-of-the-art generic low-precision training algorithms use a mix of 16-bit and 32-bit precision, creating the folklore that 16-bit hardware compute units alone are not enough to maximize model accuracy.
Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone
Aimed at solving the problem that we don't know which service stage of the mask belongs to, we propose a detection system based on the mobile phone.
ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution
For SR of small-scales (between 1 and 2), images are constructed by interpolation from a sparse set of precalculated Laplacian pyramid levels.
Scalar Coupling Constant Prediction Using Graph Embedding Local Attention Encoder
The experimental results demonstrate that, compared to the traditional chemical bond structure representations, the rotation and translation invariant structure representations proposed in this work can improve the SCC prediction accuracy; with the graph embedded local self-attention, the mean absolute error (MAE) of the prediction model in the validation set decreases from 0. 1603 Hz to 0. 1067 Hz; using the classification based loss function instead of the scaled regression loss, the MAE of the predicted SCC can be decreased to 0. 0963 HZ, which is close to the quantum chemistry standard on CHAMPS dataset.
Implicit Multidimensional Projection of Local Subspaces
We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation.
Computational prediction of RNA tertiary structures using machine learning methods
RNAs play crucial and versatile roles in biological processes.
Face Anti-Spoofing Via Disentangled Representation Learning
Face anti-spoofing is crucial to security of face recognition systems.
Salvage Reusable Samples from Noisy Data for Robust Learning
To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images.
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks
Recent studies identify that Deep learning Neural Networks (DNNs) are vulnerable to subtle perturbations, which are not perceptible to human visual system but can fool the DNN models and lead to wrong outputs.
MACD R-CNN: An Abnormal Cell Nucleus Detection Method
We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification.
A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification
RGB-Infrared (IR) cross-modality person re-identification (re-ID), which aims to search an IR image in RGB gallery or vice versa, is a challenging task due to the large discrepancy between IR and RGB modalities.
Cross-Modality Person Re-identification
Person Re-Identification
Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer
SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.
Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
The model provides automated recognition of given scans and generation of reports.
Understanding Graph Neural Networks from Graph Signal Denoising Perspectives
This paper aims to provide a theoretical framework to understand GNNs, specifically, spectral graph convolutional networks and graph attention networks, from graph signal denoising perspectives.
TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
The challenges of high intra-class variance yet low inter-class fluctuations in fine-grained visual categorization are more severe with few labeled samples, \textit{i. e.,} Fine-Grained categorization problems under the Few-Shot setting (FGFS).
Iterative Network for Image Super-Resolution
A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
Contextual Embeddings: When Are They Worth It?
We study the settings for which deep contextual embeddings (e. g., BERT) give large improvements in performance relative to classic pretrained embeddings (e. g., GloVe), and an even simpler baseline---random word embeddings---focusing on the impact of the training set size and the linguistic properties of the task.
Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
Inspired by the fact that spreading and collecting information through the Internet becomes the norm, more and more people choose to post for-profit contents (images and texts) in social networks.
Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences.
Measuring and Improving the Use of Graph Information in Graph Neural Networks
Graph neural networks (GNNs) have been widely used for representation learning on graph data.
DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks
On one hand, we can reduce the computation cost remarkably while maintaining the performance.
Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
Semantic segmentation in a supervised learning manner has achieved significant progress in recent years.
GID-Net: Detecting Human-Object Interaction with Global and Instance Dependency
GID block breaks through the local neighborhoods and captures long-range dependency of pixels both in global-level and instance-level from the scene to help detecting interactions between instances.
Understanding the Downstream Instability of Word Embeddings
To theoretically explain this tradeoff, we introduce a new measure of embedding instability---the eigenspace instability measure---which we prove bounds the disagreement in downstream predictions introduced by the change in word embeddings.
Multi-factorial Optimization for Large-scale Virtual Machine Placement in Cloud Computing
However, as growing demand for cloud services, the existing EAs fail to implement in large-scale virtual machine placement (LVMP) problem due to the high time complexity and poor scalability.
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs
Recently, generative adversarial networks (GANs) have shown great advantages in synthesizing images, leading to a boost of explorations of using faked images to augment data.
Adversarial AutoAugment
The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization.
Ranked #335 on
Image Classification
on ImageNet
Relational Mimic for Visual Adversarial Imitation Learning
In this work, we introduce a new method for imitation learning from video demonstrations.
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems.
Adversarial Domain Adaptation with Domain Mixup
In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains.
Time-aware Gradient Attack on Dynamic Network Link Prediction
In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP).
Worst Cases Policy Gradients
Recent advances in deep reinforcement learning have demonstrated the capability of learning complex control policies from many types of environments.
Max-min Fairness of K-user Cooperative Rate-Splitting in MISO Broadcast Channel with User Relaying
Cooperative Rate-Splitting (CRS) strategy, relying on linearly precoded rate-splitting at the transmitter and opportunistic transmission of the common message by the relaying user, has recently been shown to outperform typical Non-cooperative Rate-Splitting (NRS), Cooperative Non-Orthogonal Multiple Access (C-NOMA) and Space Division Multiple Access (SDMA) in a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC) with user relaying.
PipeMare: Asynchronous Pipeline Parallel DNN Training
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization.
Variational Few-Shot Learning
We propose a variational Bayesian framework for enhancing few-shot learning performance.
DyNet: Dynamic Convolution for Accelerating Convolution Neural Networks
To verify the scalability, we also apply DyNet on segmentation task, the results show that DyNet can reduces 69. 3% FLOPs while maintaining the Mean IoU on segmentation task.
On the Downstream Performance of Compressed Word Embeddings
Finally, we show that by using the eigenspace overlap score as a selection criterion between embeddings drawn from a representative set we compressed, we can efficiently identify the better performing embedding with up to $2\times$ lower selection error rates than the next best measure of compression quality, and avoid the cost of training a model for each task of interest.
C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks
Although two-stage detectors like Faster R-CNN achieved big successes in object detection due to the strategy of extracting region proposals by region proposal network, they show their poor adaption in real-world object detection as a result of without considering mining hard samples during extracting region proposals.
Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric.
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.
Extracting Visual Knowledge from the Internet: Making Sense of Image Data
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
With this insight, we design a novel Dispersion-based Clustering (DBC) approach which can discover the underlying patterns in data.
Ranked #10 on
Unsupervised Person Re-Identification
on Market-1501
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics
Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making.
Structured Discriminative Tensor Dictionary Learning for Unsupervised Domain Adaptation
However, the number of labeled source samples are always limited due to expensive annotation cost in practice, making sub-optimal performance been observed.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping.
Low-Memory Neural Network Training: A Technical Report
In this paper we study a fundamental question: How much memory is actually needed to train a neural network?
Local Deep-Feature Alignment for Unsupervised Dimension Reduction
Next, we exploit an affine transformation to align the local deep features of each neighbourhood with the global features.
Single Pixel Reconstruction for One-stage Instance Segmentation
In this paper, we propose a one-stage framework, SPRNet, which performs efficient instance segmentation by introducing a single pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors.
Compare More Nuanced:Pairwise Alignment Bilinear Network For Few-shot Fine-grained Learning
Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric.
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks
Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects.
Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement
Accurate and fast registration of cross-source 3D point clouds from different sensors is an emerged research problem in computer vision.
Unsupervised Part Mining for Fine-grained Image Classification
To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance.
Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs).
Learning Extreme Hummingbird Maneuvers on Flapping Wing Robots
Inspired by the hummingbirds' near-maximal performance during such extreme maneuvers, we developed a flight control strategy and experimentally demonstrated that such maneuverability can be achieved by an at-scale 12-gram hummingbird robot equipped with just two actuators.
Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals
Here, we present an open source high fidelity dynamic simulation for FWMAVs to serve as a testbed for the design, optimization and flight control of FWMAVs.
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science.
Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
We investigate how to train kernel approximation methods that generalize well under a memory budget.
Performance assessment of the deep learning technologies in grading glaucoma severity
Among these CNNs, the DenseNet had the highest classification accuracy (i. e., 75. 50%) based on pre-trained weights when using global ROIs, as compared to 65. 50% when using local ROIs.
Deep Bi-Dense Networks for Image Super-Resolution
In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor.
Scene Text Recognition from Two-Dimensional Perspective
Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem.
Ranked #15 on
Scene Text Recognition
on ICDAR2013
Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
In this work, we analyze the entries from DAWNBench, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries.
Fine-Grained Video Captioning for Sports Narrative
First, to facilitate this novel research of fine-grained video caption, we collected a novel dataset called Fine-grained Sports Narrative dataset (FSN) that contains 2K sports videos with ground-truth narratives from YouTube. com.
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
High-Accuracy Low-Precision Training
Low-precision computation is often used to lower the time and energy cost of machine learning, and recently hardware accelerators have been developed to support it.
Structured Control Nets for Deep Reinforcement Learning
The proposed Structured Control Net (SCN) splits the generic MLP into two separate sub-modules: a nonlinear control module and a linear control module.
Global Pose Estimation with an Attention-based Recurrent Network
The ability for an agent to localize itself within an environment is crucial for many real-world applications.
Deep Reinforcement Learning for Image Hashing
Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
(2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations.
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.
Unsupervised Generative Adversarial Cross-modal Hashing
To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data.
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge.
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags.
Natural Language Inference over Interaction Space
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
Ranked #12 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
Towards Automatic Construction of Diverse, High-quality Image Dataset
To eliminate manual annotation, in this work, we propose a novel image dataset construction framework by employing multiple textual queries.
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures.
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones.
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision and robotics communities, where the task is to accurately and efficiently recognize the location of a given query image.
A New Probabilistic Algorithm for Approximate Model Counting
Constrained counting is important in domains ranging from artificial intelligence to software analysis.
YellowFin and the Art of Momentum Tuning
We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.
Refining Image Categorization by Exploiting Web Images and General Corpus
To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.
Query-adaptive Image Retrieval by Deep Weighted Hashing
On the other hand, different hash bits actually contribute to the image retrieval differently, and treating them equally greatly affects the retrieval accuracy of image.
Multi-Label Image Classification with Regional Latent Semantic Dependencies
Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study
We show that the neural LM perplexity can be reduced by 7. 395 and 12. 011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively.
Topic-Informed Neural Machine Translation
In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance.
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.
A coarse-to-fine algorithm for registration in 3D street-view cross-source point clouds
In this paper, different from previous ICP-based methods, and from a statistic view, we propose a effective coarse-to-fine algorithm to detect and register a small scale SFM point cloud in a large scale Lidar point cloud.
A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
We propose a systematic approach for registering cross-source point clouds.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
(2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors.
Parallel SGD: When does averaging help?
Consider a number of workers running SGD independently on the same pool of data and averaging the models every once in a while -- a common but not well understood practice.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100, 000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.
Higher-Order Inference for Multi-Class Log-Supermodular Models
Higher-order models have been shown to be very useful for a plethora of computer vision tasks.
A Tool for Computing and Estimating the Volume of the Solution Space of SMT(LA)
There are already quite a few tools for solving the Satisfiability Modulo Theories (SMT) problems.
Image Denoising via Adaptive Soft-Thresholding Based on Non-Local Samples
To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution.
Message Passing Inference for Large Scale Graphical Models with High Order Potentials
To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields.
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling
Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years.
Group-based Sparse Representation for Image Restoration
In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR).
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner.
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain.
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
At the encoder, for each block of compressive sensing (CS) measurements, the optimal pre-diction is selected from a set of prediction candidates that are generated by four designed directional predictive modes.
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain.
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
These sequence-level latent parameters for each sequence are modeled as latent Dirichlet random variables and parameterized by a set of deterministic database-level hyper-parameters.
Multiple Instance Learning on Structured Data
Ignoring this structure information limits the performance of existing MIL algorithms.
Face Detection with Effective Feature Extraction
There is an abundant literature on face detection due to its important role in many vision applications.
