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Works485 from public data
- Transformers in Time Series: A Survey1,712
This paper systematically review Transformer schemes for time series modeling by highlighting their strengths as well as limitations and categorizes time series Transformers based on common tasks including forecasting, anomaly detection, and classification.
- R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object1,234
This paper proposes an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity and designs a feature refinement module to improve detection performance by getting more accurate features.
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- Image Matching from Handcrafted to Deep Features: A Survey988
This survey introduces feature detection, description, and matching techniques from handcrafted methods to trainable ones and provides an analysis of the development of these methods in theory and practice, and briefly introduces several typical image matching-based applications.
- Arbitrary-Oriented Object Detection with Circular Smooth Label640
This paper designs a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection.
- Generalizing Face Forgery Detection with High-frequency Features638
This paper finds that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize, and proposes to utilize the high-frequency noises for face forgery detection with superior generalization performance.
- Ternary Weight Networks604
Extensive experiments show that the TWNs achieve much better result than the Binary-Weight-Networks (BWNs) and the classification performance on MNIST and CIFAR-10 is very close to the full precision networks.
- MMRotate490
An open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning, and implements 18 state-of-the-art algorithms.
- Learning Modulated Loss for Rotated Object Detection412
This paper refers to the above issues as rotation sensitivity error (RSE) and proposes a modulated rotation loss to dismiss the discontinuity of loss and can achieve consistent improvement on the five parameter methods and the eight parameter methods.
- SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing372
Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects and a novel IoU constant factor is added to the smooth L1 loss to address the long standing boundary problem.
- Dense Label Encoding for Boundary Discontinuity Free Rotation Detection357
This paper explores a relatively less-studied methodology based on classification for rotation detection, and proposes new techniques to inherently dismiss the boundary discontinuity issue as encountered by the regression-based detectors.
- Unsupervised Deep Learning for Optical Flow Estimation327
This work devise a simple yet effective unsupervised method for learning optical flow, by directly minimizing photometric consistency by using image warping by the estimated flow.
- Deep Spectral Clustering Using Dual Autoencoder Network313
A joint learning framework for discriminative embedding and spectral clustering is proposed, which can significantly outperform state-of-the-art clustering approaches and be more robust to noise.
- Modeling the intensity function of point process via recurrent neural networkss290
This paper model the background by a Recurrent Neural Network with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
- Modeling the Intensity Function of Point Process Via Recurrent Neural Networks290
This paper model the background by a Recurrent Neural Network with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics.
- HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding272
Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges, achieves state-of-the-art performance for the task of trajectory prediction, on INTERACTION Prediction Challenge and Waymo Open Motion Challenge.
- Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving258
The above refinement module could be stacked in a cascaded fashion, which extends the capacity of the decoder with spatial-temporal prior knowledge about the conditioned future, and achieves state-of-the-art performance in closed-loop benchmarks.
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- Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks252
This study shows that the iDeepS method identifies the sequence and structure motifs to accurately predict RBP binding sites, and outperforms the state-of-the-art methods.
- A Short Survey of Recent Advances in Graph Matching223
The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.
- Anomaly Detection of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder214
This article proposes a smoothness-inducing sequential variational auto-encoder (VAE) model for the robust estimation and anomaly detection of multidimensional time series and shows the effectiveness of the model on both synthetic data sets and public real-world benchmarks.
- DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving204
This work aims to explore the possibility of directly adopting the strong teacher model to conduct planning while letting the student model focus more on the perception part, and proposes DriveAdapter, which employs adapters with the feature alignment objective function between the student (perception) and teacher (planning) modules.
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- On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited195
A Circular Smooth Label technique is devised to handle the periodicity of angle and increase the error tolerance to adjacent angles and an object heading detection module is developed, which can be useful when the exact heading orientation information is needed e.g. for ship and plane heading detection.
- Neural Graph Matching Network: Learning Lawler’s Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching190
This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is translated into a constrained vertex classification task, one of the first network to directly learn with the general Lawler's QAP.
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- ARS-DETR: Aspect Ratio-Sensitive Detection Transformer for Aerial Oriented Object Detection181
This article proposes an Aspect Ratio-Sensitive DEtection TRansformer, termed ARS-DETR, for oriented object detection in aerial images, and proposes a new angle classification method, called aspect ratio-aware circle smooth label (AR-CSL), to smooth the angle label more reasonably and discard the hyperparameter.
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- Person Re-Identification with Correspondence Structure Learning175
A boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair is introduced, which integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images.
- IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction168
This study proposes a computational method IPMiner (Interaction Pattern Miner) to predict ncRNA-protein interactions from sequences, which makes use of deep learning and further improves its performance using stacked ensembling, and applies it for large-scale prediction of ncRNA-protein network, that achieves promising prediction performance.
- Graph Convolutional Network Hashing for Cross-Modal Retrieval149
A Graph Convolutional Hashing approach, which learns modality-unified binary codes via an affinity graph, and demonstrates that the proposed GCH outperforms the state-of-the-art methods.
- UniRE: A Unified Label Space for Entity Relation Extraction144
A unified classifier is applied to predict each cell’s label, which unifies the learning of two sub-tasks and achieves competitive accuracy with the best extractor, and is faster.
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- MoleRec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning141
This work proposes a molecular substructure-aware encoding method entitled MoleRec that entails a hierarchical architecture aimed at modeling inter-substructure interactions and individual substructures’ impact on patient’s health condition, in order to identify those subststructures that really contribute to healing patients.
- Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization133
By analyzing the BBox parameters’ gradients under the authors' Gaussian-based KLD loss, it is shown that these parameters are dynamically updated with interpretable physical meaning, which help explain the effectiveness of the approach, especially for high-precision detection.
- Deep Extreme Multi-label Learning133
This paper proposes a practical deep embedding method for extreme multi-label classification, which harvests the ideas of non-linear embedding and graph priors-based label space modeling simultaneously.
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- Learning deep graph matching with channel-independent embedding and Hungarian attention116
A novel node and edge embedding strategy which stimulates the multi-head strategy in attention models and allows the information in each channel to be merged independently and improves the smoothness of objective learning for graph matching.
- Combinatorial Learning of Robust Deep Graph Matching: An Embedding Based Approach114
Deep neural networks are resorts to to learn the node and edge feature, as well as the affinity model for graph matching in an end-to-end fashion, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference.
- On Estimating Air Pollution from Photos Using Convolutional Neural Network114
An effective convolutional neural network is devised to estimate air's quality based on photos to alleviate the vanishing gradient issue effectively and a modified activation function is developed for photo based air pollution estimation.
- Personalized Key Frame Recommendation109
This paper makes use of video images and user time-synchronized comments to design a novel key frame recommender that can simultaneously model visual and textual features in a unified framework and can thus select key frames in a personalized manner, which, to the best of the knowledge, is the first time in the research field of video content analysis.
- CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion104
CodeAttack is introduced, a framework that transforms natural language inputs into code inputs, presenting a novel environment for testing the safety generalization of LLMs and highlighting new safety risks in the code domain and the need for more robust safety alignment algorithms to match the code capabilities of LLMs.
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- On modeling and predicting individual paper citation count over time97
This work proposes a method for predicting the citation counts of individual publications, over an arbitrary time period, and explores paper-specific covariates and a point process model to account for the aging effect and triggering role of recent citations, through which papers lose and gain their popularity, respectively.
- Joint Link Prediction and Network Alignment via Cross-graph Embedding94
It is argued that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation.
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- Visual Saliency Based on Conditional Entropy94
A novel visual saliency detection method - the conditional saliency for both image and video, approximate the conditional entropy by the lossy coding length of multivariate Gaussian data and indicates a robust and reliable feature invariance saliency.
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- Self-Paced Multi-Task Learning91
This paper proposes a novel multi-task learning framework that attempts to learn the tasks by simultaneously taking into consideration the complexities of both tasks and instances per task, by presenting a new task-oriented regularizer that can jointly prioritize tasks and instance.
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- STAR: A First-Ever Dataset and a Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery86
This paper constructs a large-scale dataset for SGG in large-size VHR SAI with image sizes ranging from 512 × 768 to 27 860 × 31 096 pixels, named STAR, encompassing over 210K objects and over 400K triplets.
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- Learning structured communication for multi-agent reinforcement learning84
A novel framework termed Learning Structured Communication is proposed by learning a flexible and efficient communication topology (hierarchical structure) that has high communication efficiency and global cooperation capability.
- TransMS: Knowledge Graph Embedding for Complex Relations by Multidirectional Semantics82
This model translates and transmits multidirectional semantics of head/tail entities and relations to tail/head entities with nonlinear functions and the semantics from entities to relations with linear bias vectors, which results in its better scalability in large-scale knowledge graph.
- Adversarial Learning for Robust Deep Clustering78
Experimental results on two popular datasets show that the proposed adversarial learning method can significantly enhance the robustness and further improve the overall clustering performance.
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- Graph Out-of-Distribution Generalization via Causal Intervention74
A bottom-up data-generative perspective is adopted and a key observation is revealed through causal analysis: the crux of GNNs' failure in OOD generalization lies in the latent confounding bias from the environment.
- The Diversified Ensemble Neural Network74
Results show the proposed principled ensemble technique by constructing the so-called diversified ensemble layer to combine multiple networks as individual modules can notably improve the accuracy and stability of the original neural networks with ignorable extra time and space overhead.
- RSDet++: Point-Based Modulated Loss for More Accurate Rotated Object Detection72
This work reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-param-based regression method with different measurement units, and introduces a novel modulated rotation loss to alleviate the problem and a rotation sensitivity detection network (RSDet) which consists of an eight-param single-stage rotated object detector and the modulate rotation loss.
- Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning72
A computation efficient yet accurate network based on the proposed attentive auxiliary features (A$^2$F) for SISR that outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features.
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- Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting72
This work proposes a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks and the dependency is modeled by recurrent neural nets.
- Combinatorial Learning of Graph Edit Distance via Dynamic Embedding71
This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.
- ZeroBind: a protein-specific zero-shot predictor with subgraph matching for drug-target interactions67
A protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures, which achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs.
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- PointOBB: Learning Oriented Object Detection via Single Point Supervision64
PointOBB is proposed, the first single Point-based OBB generation method, for oriented object detection, and achieves promising performance, and significantly outperforms potential point-supervised baselines.
- Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions64
An Adaptive Multi-Interest Debiasing framework for cross-domain sequential recommendation (AMID), which consists of a multi-interest information module (MIM) and a doubly robust estimator (DRE), which is adaptive for open-world environments and can improve the model of most off-the-shelf single-domain sequential backbone models for CDSR.
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- Learning Correspondence Structures for Person Re-Identification62
This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification by introducing a boosting-based approach to learn a correspondence structure, which indicates the patchwise matching probabilities between images from a target camera pair and introduces a global constraint-based matching process.
- Optimum Subspace Learning and Error Correction for Tensors62
The optimal rank-(R1,R2, ...Rn) tensor decomposition model, proposed in this paper, could automatically explore the low-dimensional structure of the tensor data, seeking optimal dimension and basis for each mode and separating the irregular patterns.
- Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-End Oriented Object Detection with Single Point Supervision60
This paper presents the first end-to-end solution for point-supervised OOD using a lightweight paradigm, yet it achieves a competitive performance among point-supervised alternatives, 41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.
- A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories59
This paper addresses the problem of representing motion trajectories in a highly informative way, and utilizes the droplet-based process for analyzing trajectories and applies it to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition.
- Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm58
This work proposes a novel method called ActiveFT for active finetuning task to select a subset of data distributing similarly with the entire unlabeled pool and maintaining enough diversity by optimizing a parametric model in the continuous space.
- Learning Conditional Generative Models for Temporal Point Processes57
This paper builds upon the sequence to sequence (seq2seq) prediction network and introduces the likelihood-free loss based on Wasserstein distance between point processes, besides negative maximum likelihood loss used in the traditional seq2seq model.
- On Machine Learning towards Predictive Sales Pipeline Analytics57
A profile-specific two-dimensional Hawkes processes model is developed to capture the influence from seller's activities on their leads to the win outcome, coupled with lead's personalized profiles to estimate the win-propensity of sales leads over time.
- On Predictive Patent Valuation: Forecasting Patent Citations and Their Types55
By devising a point process based patent citation type aware (self-citation and non-self-Citation) prediction model which incorporates the various information of a patent, it opens up the possibility for performing predictive patent valuation which can be especially useful for newly granted patents with emerging technology.
- Graduated Consistency-Regularized Optimization for Multi-graph Matching55
This paper proposes a novel multi-graph matching algorithm to incorporate the two aspects by iteratively approximating the global-optimal affinity score, meanwhile gradually infusing the consistency as a regularizer, which improves the performance of the initial solutions obtained by existing pairwise graph matching solvers.
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- GFTE: Graph-Based Financial Table Extraction53
A standard Chinese dataset named FinTab is published, which contains more than 1,600 financial tables of diverse kinds and their corresponding structure representation in JSON and a novel graph-based convolutional neural network model named GFTE is proposed as a baseline for future comparison.
- Group Re-Identification53
Multi-grain representations are computed to characterize the appearance and spatial features of multi-grain objects and evaluate the importance weight of each object for group Re-ID, so as to handle the interferences from group dynamics.
- On the Convergence of Graph Matching: Graduated Assignment Revisited51
This paper proposes a method for guiding the solver converging to a fixed point, resulting a convergent algorithm for graph matching in discrete domain, and extends the algorithms to their counterparts in continuous domain.
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- Low-rank matrix approximation with stability48
A new algorithm design framework is presented, which introduces new optimization objectives to guide stable matrix approximation algorithm design, and solves the optimization problem to obtain stable low-rank approximation solutions with good generalization performance.
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- Pre-training Entity Relation Encoder with Intra-span and Inter-span Information46
Experimental results show that the proposed pre-training method outperforms distantly supervised pre- training, and achieves promising performance on two entity relation extraction benchmark datasets (ACE05, SciERC).
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- Improving Maximum Likelihood Estimation of Temporal Point Process via Discriminative and Adversarial Learning44
This paper aims to improve MLE by discriminative and adversarial learning by devising a gradient based learning procedure with two complementary recipes that directly reflects the prediction accuracy of the model and can add sharpness to the smooth effect inherently caused by the MSE loss.
- MergeNAS: Merge Operations into One for Differentiable Architecture Search43
A one-shot neural architecture search method referred to as MergeNAS is proposed by merging different types of operations e.g. convolutions into one operation, which not only reduces the search cost, but also alleviates over-fitting by reducing the redundant parameters.
- Prompt-based Zero-shot Video Moment Retrieval42
This work proposes a Prompt-based Zero-shot Video Moment Retrieval (PZVMR) method that utilizes task-relevant knowledge distilled from pre-trained CLIP and adapts the knowledge to VMR and outperforms several methods using stronger supervision.
- A Self-Paced Regularization Framework for Multilabel Learning42
This work proposes a new multilabel learning formulation by introducing a self-paced function as a regularizer, so as to simultaneously prioritize label learning tasks and instances in each iteration, and provides a general way to find the desiredSelf-paced functions.
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- Fusing motion patterns and key visual information for semantic event recognition in basketball videos41
This work proposes a scheme to fuse global and local motion patterns and key visual information (KVI) for semantic event recognition in basketball videos and demonstrates that the proposed method obtains state-of-the-art performance.
- Object Localization under Single Coarse Point Supervision39
A coarse point refinement (CPR) approach, which to the best knowledge is the first attempt to alleviate semantic variance from the perspective of algorithm, which ensures training high-performance object localizer under coarse point supervision.
- Predicting protein–RNA interaction amino acids using random forest based on submodularity subset selection36
A novel approach for binding sites identification in proteins is proposed, which not only integrates local features and global features from protein sequence directly, but also constructed a balanced training dataset using sub-sampling based on submodularity subset selection.
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- Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment34
A deep neural network is devised with a user/image relation encoding input for collaborative filtering and an attentive mechanism is designed to capture the user-specific taste for image semantic tags and regions of interest by fusing the image and user's review.
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- Theoretically Achieving Continuous Representation of Oriented Bounding Boxes31
A novel representation method called Continuous OBB (COBB), which can be readily integrated into existing de-tectors e.g. Faster-RCNN as a plugin, is proposed, which can theoreti-cally ensure continuity in bounding box regression which to the best knowledge, has not been achieved in literature for rectangle-based object representation.
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- Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances27
The Point2RBox-v2 is the first approach to explore the spatial layout among instances for learning point-supervised OOD from point annotations and is expected to give a competitive performance especially in densely packed scenes.
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- StyleFace: Towards Identity-Disentangled Face Generation on Megapixels26
This work proposes StyleFace, a unified framework for 1024 2 resolution high-fidelity identity swapping and de-identification, and proposes an Adaptive Attribute Extractor, which adaptively preserves the identity-irrelevant attributes in a simple yet effective way.
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- GLOMA: Embedding Global Information in Local Matrix Approximation Models for Collaborative Filtering23
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- Wholly-WOOD: Wholly Leveraging Diversified-Quality Labels for Weakly-Supervised Oriented Object Detection22
Wholly-WOOD is developed, a weakly-supervised OOD framework, capable of wholly leveraging various labeling forms (Points, HBoxes, RBoxes, and their combination) in a unified fashion, and achieves performance very close to that of the RBox-trained counterpart on remote sensing and other areas.
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- PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection20
This paper proposes PointOBB-v3, a stronger single point-supervised OOD framework that generates pseudo rotated boxes without additional priors and incorporates support for the end-to-end paradigm and introduces an Instance-Aware Weighting strategy to focus on high-quality predictions.
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- Dynamic Reactive Spiking Graph Neural Network18
Inspired by the dynamic cognition in the brain, a Dynamic Reactive Spiking Graph Neural Network is proposed that can enhance model's expressive ability in higher biological fidelity and obtain a powerful and highly bio-fidelity model with low energy consumption.
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- ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation17
Results demonstrate that the proposed Reconstruction-Simulation-Perception (ReSimAD) scheme is beneficial to boost the domain generalization ability, even promising for 3D pre-training.
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- An Accelerated Human Motion Tracking System Based on Voxel Reconstruction under Complex Environments13
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- Learning Comprehensive Motion Representation for Action Recognition12
A Comprehensive Motion Representation (CMR) learning method for action recognition, which achieves competitive performance on Something-Something V1 & V2 and Kinetics-400, and outperforms the current state-of-the-art on the temporal reasoning datasets Something- something V1 and V2.
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- On neural architecture search and hyperparameter optimization: A max-flow based approach10
A novel max-flow based search algorithm for AutoML is proposed by representing NAS and HPO as a Max-Flow problem on a graph and thus a couple of novel AutoML strategies, dubbed MF-NAS and MF-HPO, which handle the search space and the search strategy graphically are derived.
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- SPOT: Scalable 3D Pre-Training via Occupancy Prediction for Learning Transferable 3D Representations7
SPOT, namely Scalable Pre-training via Occupancy prediction for learning Transferable 3D representations under such a label-efficient fine-tuning paradigm, achieves effectiveness on various public datasets with different downstream tasks, showcasing its general representation power, cross-domain robustness and data scalability.
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- Correction to: On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited7
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- FastDARTSDet: Fast Differentiable Architecture Joint Search on Backbone and FPN for Object Detection6
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- Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching2
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- Not All Samples Should Be Utilized Equally: Towards Understanding and Improving Dataset Distillation–
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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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