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Found 290 papers, 110 papers with code
Robust Face Recognition via Adaptive Sparse Representation
Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years.
Label Consistent Fisher Vectors for Supervised Feature Aggregation
In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years.
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i. e. the network configuration is allowed to be partially activated during inference.
Crowded Scene Analysis: A Survey
Then, existing models, popular algorithms, evaluation protocols, as well as system performance are provided corresponding to different aspects of crowded scene analysis.
Sketching for Sequential Change-Point Detection
We study sequential change-point detection procedures based on linear sketches of high-dimensional signal vectors using generalized likelihood ratio (GLR) statistics.
3D Deep Shape Descriptor
Shape descriptor is a concise yet informative representation that provides a 3D object with an identification as a member of some category.
Interaction Part Mining: A Mid-Level Approach for Fine-Grained Action Recognition
Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them.
Fine-grained Action Recognition
Human-Object Interaction Detection
+2
Learning A Task-Specific Deep Architecture For Clustering
In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both.
Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition
This scheme leverages motion cues such as motion boundary and motion magnitude (in contrast, camera motion is usually considered as "noise" for most previous methods) to generate a more compact and discriminative set of object proposals, which are more closely related to the objects which are being manipulated.
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
Understanding human activity is very challenging even with the recently developed 3D/depth sensors.
DisturbLabel: Regularizing CNN on the Loss Layer
During a long period of time we are combating over-fitting in the CNN training process with model regularization, including weight decay, model averaging, data augmentation, etc.
Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence
Different from these real-valued local shape descriptors, in this paper, we propose to learn a novel binary spectral shape descriptor with the deep neural network for 3D shape correspondence.
Visual Processing by a Unified Schatten-$p$ Norm and $\ell_q$ Norm Regularized Principal Component Pursuit
Typically, the specific structure is assumed to be low rank, which holds for a wide range of data, such as images and videos.
Online Feature Selection with Group Structure Analysis
The problem assumes that features are generated individually but there are group structure in the feature stream.
Constrained Low-Rank Learning Using Least Squares-Based Regularization
To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization.
Enhancing Person Re-identification in a Self-trained Subspace
To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched.
Generative Adversarial Network based on Resnet for Conditional Image Restoration
The GANs promote an adversarive game to approximate complex and jointed example probability.
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge.
Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment
We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.
High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology
Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
Rather than relying on elaborative annotations (e. g., manually labeled semantic maps and relations), we train our deep model in a weakly-supervised learning manner by leveraging the descriptive sentences of the training images.
SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation
To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world.
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data.
Ranked #1 on
Face Sketch Synthesis
on CUFS
(FID metric)
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding
Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task.
Self-Supervised Video Hashing with Hierarchical Binary Auto-encoder
Existing video hash functions are built on three isolated stages: frame pooling, relaxed learning, and binarization, which have not adequately explored the temporal order of video frames in a joint binary optimization model, resulting in severe information loss.
USAR: an Interactive User-specific Aesthetic Ranking Framework for Images
When assessing whether an image is of high or low quality, it is indispensable to take personal preference into account.
A Parameter Estimation of Fractional Order Grey Model Based on Adaptive Dynamic Cat Swarm Algorithm
The parameters of Fractional Order Grey Model affect the prediction accuracy of the model.
Multi-Cue Correlation Filters for Robust Visual Tracking
By combining different types of features, our approach constructs multiple experts through Discriminative Correlation Filter (DCF) and each of them tracks the target independently.
A Hierarchical Attention Model for Social Contextual Image Recommendation
After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.
ThingPot: an interactive Internet-of-Things honeypot
For that purpose, in this paper, a novel IoT honeypot called ThingPot is proposed and deployed.
Networking and Internet Architecture Cryptography and Security
Generative Adversarial Active Learning for Unsupervised Outlier Detection
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging.
SocialGCN: An Efficient Graph Convolutional Network based Model for Social Recommendation
Based on a classical CF model, the key idea of our proposed model is that we borrow the strengths of GCNs to capture how users' preferences are influenced by the social diffusion process in social networks.
Learning Symmetry Consistent Deep CNNs for Face Completion
As for missing pixels on both of half-faces, we present a generative reconstruction subnet together with a perceptual symmetry loss to enforce symmetry consistency of recovered structures.
Ranked #1 on
Facial Inpainting
on VggFace2
3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-identification
In this paper, we introduce a global video representation to video-based person re-identification (re-ID) that aggregates local 3D features across the entire video extent.
Quality-aware Unpaired Image-to-Image Translation
To tackle the above two challenges, in this paper, we propose a unified quality-aware GAN-based framework for unpaired image-to-image translation, where a quality-aware loss is explicitly incorporated by comparing each source image and the reconstructed image at the domain level.
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection
Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification
Video-based person re-identification (re-ID) refers to matching people across camera views from arbitrary unaligned video footages.
Cross-Entropy Adversarial View Adaptation for Person Re-identification
The main contribution is to learn coupled asymmetric mappings regarding view characteristics which are adversarially trained to address the view discrepancy by optimising the cross-entropy view confusion objective.
Graphonomy: Universal Human Parsing via Graph Transfer Learning
By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity.
Person Re-identification with Metric Learning using Privileged Information
We jointly learn two distance metrics by minimizing the empirical loss penalizing the difference between the distance in the original space and that in the privileged space.
A Neural Influence Diffusion Model for Social Recommendation
The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues.
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.
Neural Graph Collaborative Filtering
Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
Ranked #6 on
Link Prediction
on MovieLens 25M
Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation
To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space.
Personalized Multimedia Item and Key Frame Recommendation
We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.
Evidence for $Z_{c}^{\pm}$ decays into the $ρ^{\pm} η_{c}$ final state
We study $e^{+}e^{-}$ collisions with a $\pi^{+}\pi^{-}\pi^{0}\eta_{c}$ final state using data samples collected with the BESIII detector at center-of-mass energies $\sqrt{s}=4. 226$, $4. 258$, $4. 358$, $4. 416$, and $4. 600$ GeV.
High Energy Physics - Experiment
Joint Visual Grounding with Language Scene Graphs
In this paper, we alleviate the missing-annotation problem and enable the joint reasoning by leveraging the language scene graph which covers both labeled referent and unlabeled contexts (other objects, attributes, and relationships).
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).
Light Field Saliency Detection with Deep Convolutional Networks
Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light.
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
We have validated our approach on four recognized datasets (three synthetic and one real-world).
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
Structured Query Construction via Knowledge Graph Embedding
At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query.
Distribution-Guided Local Explanation for Black-Box Classifiers
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.
A SPIKING SEQUENTIAL MODEL: RECURRENT LEAKY INTEGRATE-AND-FIRE
Stemming from neuroscience, Spiking neural networks (SNNs), a brain-inspired neural network that is a versatile solution to fault-tolerant and energy efficient information processing pertains to the ”event-driven” characteristic as the analogy of the behavior of biological neurons.
Efficiently Embedding Dynamic Knowledge Graphs
Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems.
Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians
We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian.
In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics
Tens of millions of women suffer from infertility worldwide each year.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification
Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Kernelized Multiview Subspace Analysis by Self-weighted Learning
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
We intuitively find that M$^2$Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy.
Tensor Recovery from Noisy and Multi-Level Quantized Measurements
Existing works on tensor recovery have focused on data losses and random noises.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
Compressed DenseNet for Lightweight Character Recognition
LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
Convolutional Dictionary Pair Learning Network for Image Representation Learning
To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation
Recently, we propose a preliminary work of a neural influence diffusion network (i. e., DiffNet) for social recommendation (Diffnet), which models the recursive social diffusion process to capture the higher-order relationships for each user.
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Ranked #6 on
Collaborative Filtering
on Gowalla
RCC-Dual-GAN: An Efficient Approach for Outlier Detection with Few Identified Anomalies
To address these issues, we focus on semi-supervised outlier detection with few identified anomalies, in the hope of using limited labels to achieve high detection accuracy.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Properly handling missing data is a fundamental challenge in recommendation.
More Grounded Image Captioning by Distilling Image-Text Matching Model
To improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong supervision.
Iterative Context-Aware Graph Inference for Visual Dialog
Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts.
Ranked #12 on
Visual Dialog
on VisDial v0.9 val
Person Re-Identification via Active Hard Sample Mining
To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts.
Deep Multimodal Neural Architecture Search
Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks.
Ranked #19 on
Visual Question Answering (VQA)
on VQA v2 test-std
Memory-Augmented Relation Network for Few-Shot Learning
Metric-based few-shot learning methods concentrate on learning transferable feature embedding that generalizes well from seen categories to unseen categories under the supervision of limited number of labelled instances.
SimpleMKKM: Simple Multiple Kernel K-means
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).
Try This Instead: Personalized and Interpretable Substitute Recommendation
Then, by treating attributes as the bridge between users and items, we can thoroughly model the user-item preferences (i. e., personalization) and item-item relationships (i. e., substitution) for recommendation.
Real-time Semantic Segmentation via Spatial-detail Guided Context Propagation
In SGCPNet, we propose the strategy of spatial-detail guided context propagation.
Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation
The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test phase.
Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach
Therefore, item recommendation and attribute inference have become two main tasks in these platforms.
How to Retrain Recommender System? A Sequential Meta-Learning Method
Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference.
An Edge Information and Mask Shrinking Based Image Inpainting Approach
The mask shrinking strategy is employed in the image completion model to track the areas to be repaired.
Unsupervised Vehicle Re-identification with Progressive Adaptation
For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''.
Unsupervised Vehicle Re-Identification
Vehicle Re-Identification
Enhancing Factorization Machines with Generalized Metric Learning
Traditional FMs adopt the inner product to model the second-order interactions between different attributes, which are represented via feature vectors.
Recurrent Relational Memory Network for Unsupervised Image Captioning
Unsupervised image captioning with no annotations is an emerging challenge in computer vision, where the existing arts usually adopt GAN (Generative Adversarial Networks) models.
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
To facilitate video retrieval with complex queries, we propose a Tree-augmented Cross-modal Encoding method by jointly learning the linguistic structure of queries and the temporal representation of videos.
Model independent determination of the spin of the $Ω^{-}$ and its polarization alignment in $ψ(3686)\rightarrowΩ^{-}\barΩ^{+}$
We present an analysis of the process $\psi(3686) \to \Omega^- \bar{\Omega}^+$ ($\Omega^-\to K^-\Lambda$, $\bar{\Omega}^+\to K^+\bar{\Lambda}$, $\Lambda\to p\pi^-$, $\bar{\Lambda}\to \bar{p}\pi^+$) based on a data set of $448\times 10^6$ $\psi(3686)$ decays collected with the BESIII detector at the BEPCII electron-positron collider.
High Energy Physics - Experiment
Learning to Discretely Compose Reasoning Module Networks for Video Captioning
However, existing visual reasoning methods designed for visual question answering are not appropriate to video captioning, for it requires more complex visual reasoning on videos over both space and time, and dynamic module composition along the generation process.
Feature Pyramid Transformer
Yet, the non-local spatial interactions are not across scales, and thus they fail to capture the non-local contexts of objects (or parts) residing in different scales.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).
A Survey on Large-scale Machine Learning
Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
Dual Encoding for Video Retrieval by Text
In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own.
Ranked #3 on
Ad-hoc video search
on TRECVID-AVS16 (IACC.3)
(using extra training data)
Revealing Secrets in SPARQL Session Level
Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services.
Online Action Detection in Streaming Videos with Time Buffers
We formulate the problem of online temporal action detection in live streaming videos, acknowledging one important property of live streaming videos that there is normally a broadcast delay between the latest captured frame and the actual frame viewed by the audience.
Bias and Debias in Recommender System: A Survey and Future Directions
This motivates us to provide a systematic survey of existing work on RS biases.
LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing
To address this issue, in this work, we propose a Leaky Integrate and Analog Fire (LIAF) neuron model, so that analog values can be transmitted among neurons, and a deep network termed as LIAF-Net is built on it for efficient spatiotemporal processing.
Positive-Congruent Training: Towards Regression-Free Model Updates
Reducing inconsistencies in the behavior of different versions of an AI system can be as important in practice as reducing its overall error.
Search for the reaction $e^{+}e^{-} \rightarrow π^{+}π^{-} χ_{cJ}$ and a charmonium-like structure decaying to $χ_{cJ}π^{\pm}$ between 4.18 and 4.60 GeV
We search for the process $e^{+}e^{-}\rightarrow \pi ^{+}\pi ^{-} \chi_{cJ}$ ($J=0, 1, 2$) and for a charged charmonium-like state in the $\pi ^{\pm} \chi_{cJ}$ subsystem.
High Energy Physics - Experiment
R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching
Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.
Measurements of the center-of-mass energies of $e^{+}e^{-}$ collisions at BESIII
During the 2016-17 and 2018-19 running periods, the BESIII experiment collected 7. 5~fb$^{-1}$ of $e^+e^-$ collision data at center-of-mass energies ranging from 4. 13 to 4. 44 GeV.
High Energy Physics - Experiment
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.
Motion Prediction Using Trajectory Cues
Predicting human motion from a historical pose sequence is at the core of many applications in computer vision.
Learning One-hidden-layer Neural Networks on Gaussian Mixture Models with Guaranteed Generalizability
Instead of following the conventional and restrictive assumption in the literature that the input features follow the standard Gaussian distribution, this paper, for the first time, analyzes a more general and practical scenario that the input features follow a Gaussian mixture model of a finite number of Gaussian distributions of various mean and variance.
Graphonomy: Universal Image Parsing via Graph Reasoning and Transfer
Prior highly-tuned image parsing models are usually studied in a certain domain with a specific set of semantic labels and can hardly be adapted into other scenarios (e. g., sharing discrepant label granularity) without extensive re-training.
Cross section measurement of $e^+e^- \to p\bar{p}η$ and $e^+e^- \to p\bar{p}ω$ at center-of-mass energies between 3.773 GeV and 4.6 GeV
Based on $14. 7~\textrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected with the BESIII detector at the BEPCII collider at 17 different center-of-mass energies between $3. 7730~\textrm{GeV}$ and $4. 5995~\textrm{GeV}$, Born cross sections of the two processes $e^+e^- \to p\bar{p}\eta$ and $e^+e^- \to p\bar{p}\omega$ are measured for the first time.
High Energy Physics - Experiment
Learning Fair Representations for Recommendation: A Graph-based Perspective
For each user, this transformation is achieved under the adversarial learning of a user-centric graph, in order to obfuscate each sensitive feature between both the filtered user embedding and the sub graph structures of this user.
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.
Measurement of the absolute branching fractions for purely leptonic $D_s^+$ decays
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it CP}(\tau^\pm\nu_\tau) = (-0. 1\pm1. 9\pm1. 0)\%$.
High Energy Physics - Experiment
Spectral Top-Down Recovery of Latent Tree Models
For large trees, a common approach, termed divide-and-conquer, is to recover the tree structure in two steps.
Flatband-Induced Itinerant Ferromagnetism in RbCo$_2$Se$_2$
$A$Co$_2$Se$_2$ ($A$=K, Rb, Cs) is a homologue of the iron-based superconductor, $A$Fe$_2$Se$_2$.
Superconductivity Materials Science
Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach
In this study, we propose a rotation-based connected automated vehicle (CAV) distributed cooperative control strategy for an on-ramp merging scenario.
Positive Sample Propagation along the Audio-Visual Event Line
To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.
Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling
As a well-established approach, factorization machine (FM) is capable of automatically learning high-order interactions among features to make predictions without the need for manual feature engineering.
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.
Privileged Graph Distillation for Cold Start Recommendation
The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.
Deconfounded Video Moment Retrieval with Causal Intervention
To fill the research gap, we propose a causality-inspired VMR framework that builds structural causal model to capture the true effect of query and video content on the prediction.
Learning Elastic Embeddings for Customizing On-Device Recommenders
The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.
Harnessing Unrecognizable Faces for Improving Face Recognition
The common implementation of face recognition systems as a cascade of a detection stage and a recognition or verification stage can cause problems beyond failures of the detector.
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching
Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective.
Semi-Autoregressive Transformer for Image Captioning
To tackle this issue, non-autoregressive image captioning models have recently been proposed to significantly accelerate the speed of inference by generating all words in parallel.
Single View Physical Distance Estimation using Human Pose
We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point.
Discrimination-Aware Mechanism for Fine-Grained Representation Learning
Therefore, there is a need for a training mechanism that enforces the discriminativeness of all the elements in the feature to capture more the subtle visual cues.
Few-shot Learning with Global Relatedness Decoupled-Distillation
To overcome these problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD) method using the global category knowledge and the Relatedness Decoupled-Distillation (RDD) strategy.
Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction
Extensive experiments on real-world data demonstrate the effectiveness of our approach.
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.
TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization
In this work, a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) is proposed for generic image manipulation localization in which the RGB stream, the frequency stream, and the boundary artifact location are explored in a unified framework.
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification
To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes.
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
Nevertheless, most existing VLP approaches have not fully utilized the intrinsic knowledge within the image-text pairs, which limits the effectiveness of the learned alignments and further restricts the performance of their models.
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
Multi View Spatial-Temporal Model for Travel Time Estimation
Specifically, we use graph2vec to model the spatial view, dual-channel temporal module to model the trajectory view, and structural embedding to model traffic semantics.
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Vibration-based Uncertainty Estimation for Learning from Limited Supervision
We investigate the problem of estimating uncertainty for training data, so that deep neural networks can make use of the results for learning from limited supervision.
Learning the Representation of Behavior Styles with Imitation Learning
Imitation learning is one of the methods for reproducing expert demonstrations adaptively by learning a mapping between observations and actions.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions
Our results show that our model can predict 4-hour future trends with high accuracy in the Forex dataset, which is crucial in realistic scenarios to assist foreign exchange trading decision making.
Decoupled Low-light Image Enhancement
On the other hand, since the parameter matrix learned from the first stage is aware of the lightness distribution and the scene structure, it can be incorporated into the second stage as the complementary information.
Uncovering the Local Hidden Community Structure in Social Networks
We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy on detection which can be caused by global hidden community detection methods.
HBReID: Harder Batch for Re-identification
Triplet loss is a widely adopted loss function in ReID task which pulls the hardest positive pairs close and pushes the hardest negative pairs far away.
TextRGNN: Residual Graph Neural Networks for Text Classification
Recently, text classification model based on graph neural network (GNN) has attracted more and more attention.
Deep Color Consistent Network for Low-Light Image Enhancement
Current low-light image enhancement methods can well improve the illumination.
Compact Bidirectional Transformer for Image Captioning
In this paper, we introduce a Compact Bidirectional Transformer model for image captioning that can leverage bidirectional context implicitly and explicitly while the decoder can be executed parallelly.
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Multi-modal Emotion Estimation for in-the-wild Videos
In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition.
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition
Speech emotion recognition (SER) is an important research topic in human-computer interaction.
Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Blind image deblurring (BID) remains a challenging and significant task.
Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy
Recently, audio-visual scene classification (AVSC) has attracted increasing attention from multidisciplinary communities.
A Review-aware Graph Contrastive Learning Framework for Recommendation
Second, while most current models suffer from limited user behaviors, can we exploit the unique self-supervised signals in the review-aware graph to guide two recommendation components better?
Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways
Real-time safety systems are crucial components of intelligent vehicles.
TBraTS: Trusted Brain Tumor Segmentation
In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences
Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images.
Solutions for Fine-grained and Long-tailed Snake Species Recognition in SnakeCLEF 2022
Automatic snake species recognition is important because it has vast potential to help lower deaths and disabilities caused by snakebites.
Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data
In addition, for the first time, this paper characterizes the impact of the input distributions on the sample complexity and the learning rate.
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data.
Recent Results of Energy Disaggregation with Behind-the-Meter Solar Generation
Compared with deterministic dictionary learning, the Bayesian dictionary learning-based approach provides the uncertainty measure for the disaggregation results, at the cost of increased computational complexity.
Audio-Visual Segmentation
To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
A Synergistic Compilation Workflow for Tackling Crosstalk in Quantum Machines
Evaluations through simulation and on real IBM-Q devices show that our framework can significantly reduce the error rate by up to 6$\times$, with only $\sim$60\% circuit depth compared to state-of-the-art gate scheduling approaches.
A Semantic-aware Attention and Visual Shielding Network for Cloth-changing Person Re-identification
In addition, a visual clothes shielding module (VCS) is also designed to extract a more robust feature representation for the cloth-changing task by covering the clothing regions and focusing the model on the visual semantic information unrelated to the clothes.
Cloth-Changing Person Re-Identification
Semantic Segmentation
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
Here, we regard an expressive face as the comprehensive result of a set of facial muscle movements on one's poker face (i. e., emotionless face), inspired by Facial Action Coding System.
Ranked #5 on
Facial Expression Recognition (FER)
on FER+
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning
In conclusion, TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines.
Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion
This paper presents a new method for deep uncalibrated photometric stereo, which efficiently utilizes the inter-image representation to guide the normal estimation.
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Recommender systems are prone to be misled by biases in the data.
Temporal Action Localization with Multi-temporal Scales
However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries.
Multi-modal Contrastive Representation Learning for Entity Alignment
Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with entities.
Ranked #2 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain
Specifically, we propose a novel learning strategy of SSID, which selects samples from both source and target domains as anchors, and then randomly fuses the object and style features of these anchors to generate labeled and style-rich intermediate auxiliary features for knowledge transfer.
Switchable Online Knowledge Distillation
Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate the gap at training phase, namely distillation gap, via a switching strategy between two modes -- expert mode (pause the teacher while keep the student learning) and learning mode (restart the teacher).
Delving Globally into Texture and Structure for Image Inpainting
Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting.
Hybrid Multimodal Fusion for Humor Detection
Our experiments demonstrate the effectiveness of our proposed model and hybrid fusion strategy on multimodal fusion, and the AUC of our proposed model on the test set is 0. 8972.
A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems
Using this information, the signal retrieval module can easily recover the original control signal and remove the injected false data.
OSIC: A New One-Stage Image Captioner Coined
To obtain rich features, we use the Swin Transformer to calculate multi-level features, and then feed them into a novel dynamic multi-sight embedding module to exploit both global structure and local texture of input images.
Long-Range Zero-Shot Generative Deep Network Quantization
We find it is because: 1) a normal generator is hard to obtain high diversity of synthetic data, since it lacks long-range information to allocate attention to global features; 2) the synthetic images aim to simulate the statistics of real data, which leads to weak intra-class heterogeneity and limited feature richness.
Contrastive Positive Sample Propagation along the Audio-Visual Event Line
Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs).
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts.
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.
Bayesian Optimization Enhanced Deep Reinforcement Learning for Trajectory Planning and Network Formation in Multi-UAV Networks
In this paper, we employ multiple UAVs coordinated by a base station (BS) to help the ground users (GUs) to offload their sensing data.
Domain Generalized Stereo Matching via Hierarchical Visual Transformation
In this way, we can prevent the model from exploiting the artifacts of synthetic stereo images as shortcut features, thereby estimating the disparity maps more effectively based on the learned robust and shortcut-invariant representation.
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes
To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder, where the decoder shares the geometric similarities among different local regions.
Vocabulary-informed Zero-shot and Open-set Learning
Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels.
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification
It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework.
Face Inverse Rendering via Hierarchical Decoupling
Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search
In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search.
