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Works304 from public data
- Laplacian Score for Feature Selection2,330
This paper proposes a "filter" method for feature selection which is independent of any learning algorithm, based on the observation that, in many real world classification problems, data from the same class are often close to each other.
- Unsupervised feature selection for multi-cluster data1,149
Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, a new approach is proposed, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection, which select those features such that the multi-cluster structure of the data can be best preserved.
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- VIPS: a Vision-based Page Segmentation Algorithm698
An automatic top-down, tag-tree independent approach to detect web content structure that simulates how a user understands web layout structure based on his visual perception.
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- Fast approximate nearest neighbor search with the navigating spreading-out graph626
A novel graph structure called Monotonic Relative Neighborhood Graph (MRNG) is proposed which guarantees very low search complexity (close to logarithmic time) and is proposed to further lower the indexing complexity and make it practical for billion-node ANNS problems.
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- Constrained Nonnegative Matrix Factorization for Image Representation520
It is shown how explicitly combining label information improves the discriminating power of the resulting matrix decomposition, and the effectiveness of the novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.
- What to Do Next: Modeling User Behaviors by Time-LSTM510
Experimental results on two real-world datasets show the superiority of the recommendation method using Time-LSTM over the traditional methods.
- PandaGPT: One Model To Instruction-Follow Them All467
PandaGPT serves as an initial step toward building AGI that can perceive and understand inputs in different modalities holistically, as the authors humans do and is presented as an approach to emPower large lANguage moDels with visual and Auditory instruction-following capabilities.
- Locality sensitive discriminant analysis465
A novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA), which finds a projection which maximizes the margin between data points from different classes at each local area by discovering the local manifold structure.
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- Extracting Content Structure for Web Pages Based on Visual Representation426
This paper presents an automatic top-down, tag-tree independent approach to detect web content structure that simulates how a user understands web layout structure based on his visual perception.
- Large Scale Spectral Clustering with Landmark-Based Representation424
This paper proposes a novel approach, called Landmark-based Spectral Clustering (LSC), for large scale clustering problems, where the original data points are represented as the linear combinations of landmarks and the spectral embedding of the data can be efficiently computed with the landmark-based representation.
- Topic modeling with network regularization414
A novel solution to the problem of topic modeling with network structure (TMN) is proposed, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data.
- Tensor Subspace Analysis407
A new algorithm called Tensor Subspace Analysis (TSA) is proposed that detects the intrinsic local geometrical structure of the tensor space by learning a lower dimensional tensor subspace and achieves better recognition rate, while being much more efficient.
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- Self-taught hashing for fast similarity search395
This paper proposes a novel Self-Taught Hashing (STH) approach to semantic hashing: it first finds the optimal l-bit binary codes for all documents in the given corpus via unsupervised learning, and then train l classifiers via supervised learning to predict the l- bit code for any query document unseen before.
- Hierarchical clustering of WWW image search results using visual, textual and link information389
A hierarchical clustering method using visual, textual and link analysis to cluster the search results into different semantic clusters of image search results is proposed.
- Hierarchical Clustering of WWW Image Search Results Using Visual389
A hierarchical clustering method using visual, textual and link analysis to cluster the search results into different semantic clusters of image search results is proposed.
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- CLRNet: Cross Layer Refinement Network for Lane Detection329
This work presents Cross Layer Refinement Network (CLRNet), a novel network design which first detects lanes with high-level semantic features then performs refinement based on low-level features and introduces Line IoU loss which regresses the lane line as a whole unit to improve the localization accuracy.
- Domain Adaptation for Semantic Segmentation With Maximum Squares Loss323
The maximum squares loss prevents the training process being dominated by easy-to-transfer samples in the target domain, and introduces the image-wise weighting ratio to alleviate the class imbalance in the unlabeled target domain.
- Improving pseudo-relevance feedback in web information retrieval using web page segmentation317
This paper proposes a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page and achieves 27% performance improvement on Web Track dataset.
- PI-RCNN: An Efficient Multi-Sensor 3D Object Detector with Point-Based Attentive Cont-Conv Fusion Module248
A novel fusion approach named Point-based Attentive Cont-conv Fusion(PACF) module, which fuses multi-sensor features directly on 3D points and a 3D multi-Sensor multi-task network called Pointcloud-Image RCNN(PI-RCNN as brief), which handles the image segmentation and 3D object detection tasks.
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- Density Sensitive Hashing230
A novel hashing algorithm called density sensitive hashing (DSH) is proposed, which avoids the purely random projections selection and uses those projective functions which best agree with the distribution of the data.
- Speed up kernel discriminant analysis215
Spectral Regression Kernel Discriminant Analysis is presented, which casts discriminant analysis into a regression framework, which facilitates both efficient computation and the use of regularization techniques.
- An exploration of improving collaborative recommender systems via user-item subgroups211
This paper proposes an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance by formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it.
- Block-level link analysis203
Based on block-level link analysis, two new algorithms are proposed, Block Level PageRank and Block Level HITS, whose performances are studied extensively using web data.
- Adversarial-Learned Loss for Domain Adaptation201
The confusion matrix is introduced, which is learned through an adversarial manner in ALDA, to reduce the gap and align the feature distributions and outperforms state-of-the-art approaches in four standard domain adaptation datasets.
- Block-based web search200
The experimental results show that such a semantic partitioning of web pages effectively deals with the problem of multiple drifting topics and mixed lengths, and thus has great potential to boost up the performance of current web search engines.
- Mining hidden community in heterogeneous social networks199
Based on the observation that different relations have different importance with respect to a certain query, a new method for learning an optimal linear combination of these relations which can best meet the user's expectation is proposed.
- Mapping Users across Networks by Manifold Alignment on Hypergraph186
A novel subspace learning algorithm, Manifold Alignment on Hypergraph (MAH), is proposed, which uses hypergraph to model high-order relations here and ranks all users in the other network by their possibilities of being the corresponding user.
- Probabilistic dyadic data analysis with local and global consistency179
This work introduces a probabilistic framework for modeling both the topical and geometrical structure of the dyadic data that explicitly takes into account the local manifold structure.
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- Modeling hidden topics on document manifold175
This paper proposes a novel algorithm called Laplacian Probabilistic Latent Semantic Indexing (LapPLSI) for topic modeling, which models the document space as a submanifold embedded in the ambient space and directly performs the topic modeling on this document manifold in question.
- Locality preserving nonnegative matrix factorization170
This paper proposes a novel algorithm, called Locality Preserving Non-negative Matrix Factorization (LPNMF), and shows the encouraging results of the proposed algorithm in comparisons to the state-of-the-art algorithms on two large high-dimensional databases.
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- Isometric Projection157
This paper proposes a novel linear dimensionality reduction algorithm, called Isometric Projection, which is more capable of discovering the intrinsic geometrical structure of a manifold than Principal Component Analysis (PCA).
- Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning156
This paper designs useful user selection criteria based on items’ attributes and users’ rating history, and combines the criteria in an optimization framework for selecting users, and generates accurate rating predictions for the other unselected users.
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- TeCH: Text-Guided Reconstruction of Lifelike Clothed Humans142
A hybrid 3D representation based on DMTet, which consists of an explicit body shape grid and an implicit distance field and a personalized fine-tuned Text-to-Image diffusion model (T2I) which learns the “indescribable” appearance is proposed.
- Locality preserving indexing for document representation142
Experimental results show that LPI provides better representation in the sense of semantic structure, and a novel algorithm called Locality Preserving Indexing (LPI) is proposed for document indexing.
- Efficient manifold ranking for image retrieval140
The original manifold ranking algorithm is extended and a new framework named Efficient Manifold Ranking (EMR) is proposed, which significantly reduces the computational time and makes it a promising method to large scale real world retrieval problems.
- Document Summarization Based on Data Reconstruction136
This paper proposes a novel framework named Document Summarization based on Data Reconstruction (DSDR), which generates a summary which consist of those sentences that can best reconstruct the original document.
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- MaCAR: Urban Traffic Light Control via Active Multi-agent Communication and Action Rectification126
This work proposes a novel Multi-agent Communication and Action Rectification (MaCAR) framework which enables active communication between agents by considering the impact of synchronous actions of agents and outperforms state-of-the-art methods on both synthetic and real-world datasets.
- Document recommendation in social tagging services119
This paper considers the problem of document recommendation using purely tagging data and proposes a novel graph-based representation learning algorithm that can generate promising recommendations and outperforms traditional recommendation algorithms.
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- Video Question Answering via Hierarchical Spatio-Temporal Attention Networks117
The hierarchical spatio-temporal attention network is proposed for learning the joint representation of the dynamic video contents according to the given question and the multi-step reasoning process for open-ended video question answering is developed.
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- Locality preserving clustering for image database111
This paper proposes a clustering algorithm named Locality Preserving Clustering (LPC), which shares many of the data representation properties of nonlinear spectral method, yet provides an explicit mapping function which is defined everywhere, both on training data points and testing points.
- Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification110
This paper proposes a novel Mutual Centralized Learning (MCL) to fully affiliate these two disjoint dense features sets in a bidirectional paradigm and relates its method to learning a centrality on an affiliation network and demonstrates its capability to be plugged in existing methods by highlighting centralized local features.
- Regularized locality preserving indexing via spectral regression107
Benefit from recent progresses on spectral graph analysis, the original LPI algorithm is cast into a regression framework which enable us to avoid eigen-decomposition of dense matrices and this makes it an efficient and effective data preprocessing method for large scale text clustering, classification and retrieval.
- Spectral regression104
By using techniques from spectral graph embedding and regression, this paper proposes a unified framework, called spectral regression, for learning an image subspace, which provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.
- Training-Time-Friendly Network for Real-Time Object Detection102
This work starts with light-head, single-stage, and anchor-free designs, which enable fast inference speed, and focuses on shortening training time, noticing that encoding more training samples from annotated boxes plays a similar role as increasing batch size, which helps enlarge the learning rate and accelerate the training process.
- Dialogue Act Recognition via CRF-Attentive Structured Network102
This paper tackles the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structured dependencies without abandoning end-to-end training and incorporates hierarchical semantic inference with memory mechanism on the utterance modeling at multiple levels.
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- Parallel field alignment for cross media retrieval91
A novel method for the cross media retrieval task, named Parallel Field Alignment Retrieval (PFAR), which integrates a manifold alignment framework from the perspective of vector fields, and can effectively preserve the metric of data manifolds and project their relationship into intermediate latent semantic spaces during the process of manifold alignment.
- Gaussian Mixture Model with Local Consistency91
A novel method based on manifold structure for data clustering, called Locally Consistent Gaussian Mixture Model (LCGMM), is introduced, which construct a nearest neighbor graph and adopt Kullback-Leibler Divergence as the distance measurement to regularize the objective function of GMM.
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- Expert finding for community-based question answering via ranking metric network learning88
This paper proposes a novel ranking metric network learning framework for expert finding by exploiting both users' relative quality rank to given questions and their social relations and develops a random-walk based learning method with recurrent neural networks forranking metric network embedding.
- Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identification87
A joint learning framework to learn better feature embeddings via high precision neighbor pseudo labels and high recall group pseudo labels is proposed and can achieve state-of-the-art performance under the unsupervised domain adaptation re-ID setting.
- Multi-Task Vehicle Detection With Region-of-Interest Voting87
This paper proposes a novel vehicle detection scheme based on multi-task deep convolutional neural networks (CNNs) and region-of-interest (RoI) voting that achieves superior performance in vehicle detection compared with other existing published works.
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- Using Graph Model for Face Analysis80
It is shown that LPP provides a more general framework for subspace learning and a natural solution to the small sample issue in LDA.
- Cross domain recommendation based on multi-type media fusion77
This paper proposes a Bayesian hierarchical approach based on Latent Dirichlet Allocation (LDA) to transfer user interests cross domains or media, and combines multi-type media information: media descriptions, user-generated text data and ratings.
- Deep feature based contextual model for object detection76
This paper proposes an ensemble object detection system which incorporates the local appearance, the contextual information in term of relationships among objects and the global scene based contextual feature generated by a convolutional neural network.
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- Heterogeneous hypergraph embedding for document recommendation75
A heterogeneous hypergraph model which fully exploits high-order relational information in tagging data and, meanwhile, customizes the influence of different types of relations is proposed and a novel heterogeneoushypergraph embedding framework is developed for document recommendation.
- Statistical and computational analysis of locality preserving projection72
Different from Principal Component Analysis (PCA), LPP obtains a subspace spanned by the smallest eigenvectors of the local covariance matrix, which shows that the performance can be significantly improved in the subspace, and especially LPP works much better than PCA.
- Orthogonal locality preserving indexing72
Orthogonal LPI is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc, and shows that OLPI can have more locality preserving power than LPI.
- Translating Embeddings for Knowledge Graph Completion with Relation Attention Mechanism71
Extensive experiments show that the proposed novel knowledge graph embedding method named TransAt outperforms state-of-the-art methods significantly on public datasets, and the method can learn the true attention varying among relations.
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- Query-Biased Self-Attentive Network for Query-Focused Video Summarization68
This paper proposes a hierarchical self-attentive network to model the relative relationship at three levels, which are different frames from a segment, different segments of the same video, textual information of video description and its related visual contents, and the query-focused summary.
- Spectral regression: a regression framework for efficient regularized subspace learning68
This thesis introduces a novel dimensionality reduction framework, called Spectral Regression (SR), which casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices.
- Spectral Regression for Dimensionality Reduction68
Spectral Regression casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices and can be performed in supervised, unsupervised and semisupervised situation.
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- Subspace Learning Based on Tensor Analysis64
This paper considers an image as the second order tensor in Rm1⊗Rm2 and proposes two novel tensor subspace learning algorithms called TensorPCA and TensorLDA, which explicitly take into account the relationship between column vectors of the image matrix and the relationships between the row vectors ofThe image matrix.
- Tensor space model for document analysis63
This paper proposes a novel Tensor Space Model for document analysis, which represents documents as the second order tensors, or matrices, and develops a novel indexing algorithm called Tensor Latent Semantic Indexing (TensorLSI), developed in the tensor space.
- Abstractive Meeting Summarization via Hierarchical Adaptive Segmental Network Learning62
This paper proposes the hierarchical neural encoder based on adaptive recurrent networks to learn the semantic representation of meeting conversation with adaptive conversation segmentation and develops the reinforced decoder network to generate the high-quality summaries for abstractive meeting summarization.
- Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks61
This paper proposes the adaptive hierarchical encoder network to learn the joint representation of the long-form video contents according to the question with adaptive video segmentation and develops the reinforced decodernetwork to generate the natural language answer for open-ended video question answering.
- Clustering and searching WWW images using link and page layout analysis61
iFind is described, a system for clustering and searching WWW images, which is less sensitive to noisy links than previous methods like PageRank, HITS, and PicASHOW, and hence the image graph can better reflect the semantic relationship between images.
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- Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning59
This paper proposes a novel asymmetric multi-faceted ranking network learning framework for community-based question answering by jointly exploiting the deep semantic relevance between question-answer pairs and the answerers' authority to the given question.
- Learning Occupancy for Monocular 3D Object Detection58
This paper proposes Occu- pancyM3D, a method of learning occupancy for monocu-lar 3D detection that directly learns occupancy in frustum and 3D space, leading to more discriminative and informative 3D features and representations.
- Lidar Point Cloud Guided Monocular 3D Object Detection58
A simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG), capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs is proposed.
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- A Minimax Game for Instance based Selective Transfer Learning58
This work proposes a general Minimax Game based model for selective transfer learning that outperforms the competing methods by a large margin and is shown to speed up the training process of the learning task in the target domain than traditional TL methods.
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- MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection57
This work proposes MonoNeRD, a novel detection framework that can infer dense 3D geometry and occupancy, and is the first to introduce volume rendering for M3D, and demonstrates the potential of implicit reconstruction for image-based 3D perception.
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- Microblog Sentiment Classification via Recurrent Random Walk Network Learning54
This paper proposes a novel recurrent random walk network learning framework for the microblog sentiment classification problem by exploiting both users’ posted tweets and their social relations in microblogs, and introduces the deep recurrent neural networks with random-walk layer for heterogeneous MSC network embedding.
- Relational Multimanifold Coclustering54
A novel coc Lustering algorithm called relational multimanifold coclustering based on symmetric nonnegative matrix trifactorization, which decomposes the relational data matrix into three submatrices is developed, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.
- A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search52
A simple but effective novel hash index search approach is proposed and a thorough comparison of eleven popular hashing algorithms is made and Surprisingly, the random-projection-based Locality Sensitive Hashing ranked the first, which is in contradiction to the claims in all the other 10 hashing article.
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- Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network50
This paper proposes the hierarchical attention context network for context-aware question understanding by modeling the hierarchically sequential conversation context structure and develops the multi-stream spatio-temporal attention network for learning the joint representation of the dynamic video contents and context- aware question embedding.
- One-shot Implicit Animatable Avatars with Model-based Priors49
Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT outperforms strong baseline methods of avatar creation when only a single image is available.
- Video Question Answering via Hierarchical Dual-Level Attention Network Learning46
This paper develops the hierarchical duallevel attention networks to learn the question-aware video representations with word-level and question-level attention mechanisms, and devise thequestion-level fusion attention mechanism for the proposed networks to learning the questionaware joint video representation.
- Iterative Multi-View Hashing for Cross Media Indexing46
This paper studies the cross media indexing problem by learning the discriminative hashing functions to map the multi-view datum into a shared hamming space and proposes a novel hashing algorithm called Iterative Multi-View Hashing (IMVH), which not only greatly improves the retrieval accuracy but also performs strong robustness.
- Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation46
A novel approach named Discriminative Orthogonal Nonnegative matrix factorization (DON) is presented, which preserves both the local manifold structure and the global discriminant information simultaneously through manifold discriminant learning.
- Image clustering with tensor representation45
This paper represents the images as points in the tensor space Rn1 mathcal Rn2 which is a tensor product of two vector spaces and proposes a novel image representation and clustering algorithm which explicitly considers the manifold structure of the Tensor space.
- Apparel-Invariant Feature Learning for Person Re-Identification44
The semi-supervised Apparel-invariant Feature Learning (AIFL) framework is proposed to learn an apparel- Invariant pedestrian representation using images of the same person wearing different clothes, and an unsupervised apparel-simulation GAN (AS-GAN) to synthesize cloth-changing images according to the target cloth embedding.
- Pre-training the deep generative models with adaptive hyperparameter optimization44
A new holdout loss is proposed, the free energy gap, which takes into account both factors of the model fitting and over-fitting, and improves the performances of DGMs significantly in both the supervised and unsupervised learning tasks.
- Improving face recognition with domain adaptation43
This paper crawls a large face dataset whose distribution is different from LFW and shows the improvement of LFW accuracy with a simple domain adaptation technique, which is the first time that domain adaptation is applied in the unconstrained face recognition problem with a million scale dataset.
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- Discourse Marker Augmented Network with Reinforcement Learning for Natural Language Inference41
This paper proposes to transfer knowledge from some important discourse markers to augment the quality of the NLI model by using reinforcement learning to optimize a new objective function with a reward defined by the property of theNLI datasets to make full use of the labels information.
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- Laplacian optimal design for image retrieval40
A novel active learning algorithm, called Laplacian Optimal Design (LOD), is proposed, based on aregression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space.
- DMN4: Few-Shot Learning via Discriminative Mutual Nearest Neighbor Neural Network39
It is argued that a Mutual Nearest Neighbor (MNN) relation should be established to explicitly select the query descriptors that are most relevant to each task and discard less relevant ones from aggregative clutters in FSL.
- Laplacian Co-hashing of Terms and Documents38
This paper introduces the novel problem of co-hashing where both documents and terms are hashed simultaneously according to their semantic similarities and proposes a novel algorithm Laplacian Co-Hashing (LCH) to solve this problem which directly optimises the Hamming distance.
- Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training37
It is argued that increasing the number of networks (ensemble) can achieve better accuracy-efficiency trade-offs than purely increasing the width and validated with 8 different neural architectures on common benchmarks through extensive experiments.
- Online robust principal component analysis via truncated nuclear norm regularization37
A novel online RPCA algorithm is proposed by adopting a recently proposed truncated nuclear norm as a tighter approximation of low rank constraint and decompose the objective function as a summation of sample-wise cost.
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- Question retrieval for community-based question answering via heterogeneous social influential network32
A novel framework named HSIN is proposed which encodes not only the question contents but also the asker’s social interactions to enhance the question embedding performance and demonstrates that employing the heterogeneous social network information outperforms the other state-of-the-art solutions in this task.
- Multi-view based multi-label propagation for image annotation32
By taking full advantage of the dual-heterogeneity from views and labels, MMP is able to propagate the labels better than state of the art and introduces an iterative algorithm to solve the optimization problem.
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- ES-Net: Erasing Salient Parts to Learn More in Re-Identification29
A novel network, Erasing-Salient Net (ES-Net), to learn comprehensive features by erasing the salient areas in an image by using a trainable pooling layer P-pooling that generalizes global max and global average pooling.
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- Constrained Low-Rank Learning Using Least Squares-Based Regularization23
This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner and proposes a novel constrained LRR method formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm.
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- Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning20
A novel attentional multi-faceted ranking network learning framework with multi-modal neural networks is developed for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task.
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- User Personalized Satisfaction Prediction via Multiple Instance Deep Learning18
This paper designs an efficient framework exploiting multiple instance learning property with deep learning tactic to model the question-answer pairs relevance and rank the asker's satisfaction possibility and demonstrates the feasibility of the proposed framework in predicting asker personalized satisfaction.
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- Link Prediction via Ranking Metric Dual-Level Attention Network Learning16
This paper proposes a novel proximity ranking metric attention network learning framework by jointly exploiting both node-level and path-level attention proximity of the endpoints to their betweenness paths for learning the discriminative feature representation for link prediction.
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- Dual Graph for Traffic Forecasting15
A novel dual graph framework, called DualGraph, is proposed, to model the propagation behavior of traffic on road networks, and shows that even for node or edge traffic forecasting alone, the model still outperforms compared ones, especially for long term prediction.
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- Localizing Unseen Activities in Video via Image Query14
This work considers a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization, and proposes a novel self-attention interaction localizer to retrieve unseen Activities in an end-to-end fashion.
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- Progressive Transfer Learning for Person Re-identification11
This paper introduces a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the global information of the dataset into a latent state and uses this latent state to rectify the extracted feature.
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- The forgettable-watcher model for video question answering10
A TGIF-QA dataset for video question answering with the help of automatic question generation and a forgettable-watcher model to better utilize the temporal structure of the videos and the phrasal structures of the answers is proposed.
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- SplitNet: Divide and Co-training.7
It is shown that the number of networks is a new dimension of effective model scaling, besides depth/width/resolution, and small networks can achieve better ensemble performance than the large one with few or no extra parameters or FLOPs.
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- Decouple co-adaptation: Classifier randomization for person re-identification7
This paper proposes a very simple and effective training method, called DeAda, to construct a series of weak classifiers during training by randomization of parameters, so that optimization on the feature extractor could be strengthened in the training stage.
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- Numerical Simulation Investigation on Fracture Propagation of Fracturing for Crossing Coal Seam Roof6
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- A General Framework of Generating Estimation Functions for Computing the Mutual Information of Terms1
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- Special issue on selected and extended papers from the 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015)–
A synopsis of papers collected in the special issue of IScIDE 2015 are presented, which address some aspects of the above reearch problems.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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