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- 1,164
- 1,147
- Relation-Shape Convolutional Neural Network for Point Cloud Analysis1,050
RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis, achieves the state of the arts and a hierarchical architecture can be developed to achieve contextual shape-aware learning for Point cloud analysis.
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- AugFPN: Improving Multi-Scale Feature Learning for Object Detection588
This paper begins by first analyzing the design defects of feature pyramid in FPN, and then introduces a new feature pyramid architecture named AugFPN to address these problems, which consists of three components: Consistent Supervision, Residual Feature Augmentation, and Soft RoI Selection.
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- Spatio-Temporal Graph Structure Learning for Traffic Forecasting314
A novel framework named Structure Learning Convolution (SLC) is proposed that enables to extend the traditional convolutional neural network (CNN) to graph domains and learn the graph structure for traffic forecasting and significantly outperforms the state-of-the-art ones.
- DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing300
DensePoint is proposed, a general architecture to learn densely contextual representation for point cloud processing that extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns.
- Semantic labeling in very high resolution images via a self-cascaded convolutional neural network289
A novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet), for confusing manmade objects and fine-structured objects, ScasNet improves the labeling coherence with sequential global- to-local contexts aggregation.
- Spectral Unmixing via Data-Guided Sparsity276
This paper proposes a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel and applies the ℓp (0 <; p <; 1) constraint in an adaptive manner.
- Data guided Sparse Method for Hyperspectral Unmixing.257
This paper proposes a novel sparsity-based method by learning a data-guided map (DgMap) to describe the individual mixed level of each pixel and applies the ℓp (0 <; p <; 1) constraint in an adaptive manner.
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- Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images201
A gated convolutional neural network is proposed that achieves competitive segmentation accuracy on the public ISPRS 2D Semantic Labeling benchmark, which is challenging for segmentation by only using the RGB images.
- GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction189
A Global Spatial-Temporal Network (GSTNet) is proposed, which consists of several layers of spatial-temporal blocks, which can simultaneously extract the dynamic temporal dependencies and the global spatial correlations.
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- Robust level set image segmentation via a local correntropy-based K-means clustering157
A novel segmentation algorithm via a local correntropy-based K-means (LCK) clustering algorithm that can be robust to the outliers is proposed, and is incorporated into the region-based level set segmentation framework.
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- Scene text detection and recognition with advances in deep learning: a survey114
A thorough and in-depth reviews on the recent advances on scene text detection and recognition, mainly focusing on the methods that appeared in the past 5 years for text detection and recognition in images and videos, including the recent state-of-the-art techniques on the following three related topics.
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- You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization103
The motivation behind DSO-NAS is to address the task in the view of model pruning, and it enjoys both advantages of differentiability and efficiency, therefore it can be directly applied to large datasets like ImageNet and tasks beyond classification.
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- Progressive Sparse Local Attention for Video Object Detection99
A novel module called Progressive Sparse Local Attention (PSLA) is proposed, which establishes the spatial correspondence between features across frames in a local region with progressively sparser stride and uses the correspondence to propagate features.
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- DetNAS: Neural Architecture Search on Object Detection.94
This paper proposes DetNAS to automatically search neural architectures for the backbones of object detectors, formulated into a supernet and the search method relies on evolution algorithm (EA).
- A Performance Evaluation of Local Features for Image-Based 3D Reconstruction89
This paper performs a comprehensive and comparative evaluation of the state-of-the-art local features for the task of image-based 3D reconstruction and shows that binary features are competent to reconstruct scenes from controlled image sequences with only a fraction of processing time compared to using float type features.
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- Robust Hyperspectral Unmixing With Correntropy-Based Metric83
This paper presents a robust model for unsupervised hyperspectral unmixing with the correntropy-based metric where the nonnegative constraints on both endmembers and abundances are imposed to keep physical significance.
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- TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers75
TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches and the superiority of the proposed model that provides an effective method for transformers in other low-level vision tasks.
- Learning Where to Focus for Efficient Video Object Detection70
A novel module called Learnable Spatio-Temporal Sampling (LSTS) has been proposed to learn semantic-level correspondences among adjacent frame features accurately and achieves state-of-the-art performance on the ImageNet VID dataset with less computational complexity and real-time speed.
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- Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting66
A fitting based algorithm is proposed to overcome the ineffectiveness of existing methods in the road intersections and achieve higher quantitative results, as well as more satisfactory visual performances by comparing with state-of-the-art methods.
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- Ensemble based deep networks for image super-resolution65
Through the combination of multiple models, the ensemble based sparse coding network (ESCN) can generate more robust reconstructed results and achieve state-of-the-art performance.
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- Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression61
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- Learning graph structure via graph convolutional networks57
A method that learns Graph Structure via graph Convolutional Networks (GSCN), which introduces the graph structure parameters measuring the correlation degrees of adjacent nodes, which is better to handle the graph-structured data of non-stationarity.
- Multitask Learning for Ship Detection From Synthetic Aperture Radar Images56
A multitask learning-based object detector (MTL-Det) is proposed in this article to distinguish ships in SAR images, which involves two auxiliary subtasks that are focused on learning object-specific cues for the ship detection task, which is constrained by the pseudoground truth generated by the main task.
- DATA: Differentiable ArchiTecture Approximation56
Differentiable ArchiTecture Approximation (DATA) with an Ensemble Gumbel-Softmax (EGS) estimator is developed to automatically approximate architectures during searching and validating in a differentiable manner, guaranteeing the requisite efficiency during searching.
- Video Object Detection with Locally-Weighted Deformable Neighbors56
This paper proposes LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks and adopts brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes, called Memory-Guided Propagation.
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- A generalized least-squares approach regularized with graph embedding for dimensionality reduction54
The PCA (Principal Component Analysis) idea of minimizing least-squares reconstruction errors is regularized with graph embedding, to unify various local manifold embedding methods within a generalized framework to keep global and local low dimensional subspace.
- Blind image quality assessment via learnable attention-based pooling53
The core idea is to introduce a learnable pooling that can model human visual attention in a data-driven manner and is built by incorporating an attention module and allows for a joint learning of local quality and local weights.
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- Image-Text Cross-Modal Retrieval via Modality-Specific Feature Learning49
Extensive cross-modal retrieval experiments are carried out on three challenging datasets that consist of image-document pairs or image-query click-through data from a search engine, and the results firmly demonstrate that the proposed model is much more effective.
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- Dense semantic embedding network for image captioning48
A Dense Semantic Embedding Network (DSEN) is proposed to densely embed the attributes with the multi-modal of image and text at each step of word generation and can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation.
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- Structure-Aware Convolutional Neural Networks45
By replacing the classical convolution in CNNs with the structure-aware convolution, Structure-Aware Convolutional Neural Networks (SACNNs) are readily established and there is strong evidence that SACnns outperform current models on various machine learning tasks, including image classification and clustering.
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- Region-based image segmentation with local signed difference energy40
The proposed method considers the local image information by describing it as a novel local signed difference (LSD) energy, which possesses both local separability and global consistency, and is integrated into an objective energy functional, which is minimized via a level set evolution process.
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- Image Deblurring with Coupled Dictionary Learning37
This paper presents a learning-based framework where the knowledge hidden in huge amounts of available data is explored and exploited for image deblurring, developed under the conceptual framework of coupled dictionary learning.
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- Change detection based on auto-encoder model for VHR images35
A novel unsupervised change detection algorithm is proposed based on deep learning, where the complex correspondence between the images is established by Auto-encoder Model, by taking advantages of the powerful ability of deep learning in compensating the impacts implicitly.
- Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation34
The proposed cross-modal similarity learning algorithm takes a bilinear formulation, and with the nuclear-norm penalization, it achieves low-rank representation and the accelerated proximal gradient algorithm is successfully imported to find the optimal solution with a fast convergence rate O(1/t2).
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- 3D PostureNet: A unified framework for skeleton-based posture recognition33
An end-to-end framework based on 3D CNN, called 3D PostureNet, is developed for robust posture recognition, and achieves significantly superior performance on both skeleton-based human posture and hand posture recognition tasks.
- Geometric rectification of document images using adversarial gated unwarping network33
This model can rectify arbitrarily distorted document images with complicated page layouts and cluttered backgrounds and outperforms the state-of-the-art methods in terms of OCR accuracy and several widely used quantitative evaluation metrics.
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- Exploiting Vector Fields for Geometric Rectification of Distorted Document Images32
A segment-free method for geometric rectification of a distorted document image captured by a hand-held camera that does not have to extract curved text lines or textual blocks and can be freely applied to document images with extremely complicated page layouts and severe image quality degradation.
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- HAN: An efficient hierarchical self-attention network for skeleton-based gesture recognition31
This work proposes an efficient hierarchical self-attention network (HAN) for skeleton-based gesture recognition, which is based on pure self-Attention without any CNN, RNN or GCN operators.
- Efficient nearest neighbor search in high dimensional hamming space31
The proposed method significantly outperforms the state of the arts, obtaining improved search accuracy at various speed up factors, e.g., at least 16% improvement of search accuracy over previous methods (from 67.7% to 83.7%) when the search speed is 200 times faster than the linear scan for a one million database.
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- Automatic blur-kernel-size estimation for motion deblurring30
A modified automap method is developed that contains a directional attenuation component, which can effectively reduce the influence of structural edges in the image, leading to more accurate and reliable kernel size estimation.
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- Joint spatial-temporal attention for action recognition29
To extract robust motion representations of videos, a new spatial attention module based on 3D convolution is proposed, which can pay attention to the salient parts of the spatial areas, and a new bidirectional LSTM based temporal attention module is introduced.
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- Efficient sea–land segmentation using seeds learning and edge directed graph cut28
A supervised method combining seeds learning and graph cut (GC) is proposed to address the problem of sea-land segmentation, which outperforms that of state-of-the-art methods in terms of quantitative and visual performance.
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- Multi-task prompt tuning with soft context sharing for vision–language models26
Inspired by the fact that modeling task relationship by multi-task learning can usually boost performance, this work proposes a novel method SoftCPT (Soft Context Sharing for Prompt Tuning) to tune pre-trained vision-language models on multiple target few-shot tasks jointly.
- SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion25
A spatiotemporal inference network (STIN) to produce precipitation prediction from multimodal meteorological data with spatiotemporal specific filters is proposed, fusing the multimodal information, including temperature, humidity, and wind.
- Learning from the Target: Dual Prototype Network for Few Shot Semantic Segmentation25
A dual prototype network (DPNet) is proposed to dispose of few shot semantic segmentation from a new perspective and exceeds previous state-of-the-arts with a sizable margin, verifying the effectiveness of the proposed method.
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- Aircraft Detection by Deep Convolutional Neural Networks25
Two types of deep learning methods are implemented, deep convolutional neural network (DNN) and deep belief net (DBN), comparing their performances with that of the traditional methods in the task of aircraft detection, and both reduce at least 40% of the false alarm rate of the traditional methods.
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- No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking24
This paper forms the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly and improves the state-of-the-art by over 2% on the CSIQ benchmark and outperforms most compared opinion-aware models on TID2013.
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- Multisource data registration based on NURBS description of contours23
This paper presents a novel contour‐based approach for multisource image registration that is robust to image noise and parameterized with Non‐Uniform Rational B‐Splines (NURBS).
- DCAT: Dual Cross-Attention-Based Transformer for Change Detection22
This work has developed a new approach that utilizes the dual cross-attention-transformer (DCAT) method, which mimics the visual change observation procedure of human beings and interacts with and merges bi-temporal features.
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- Multifocus image fusion via focus segmentation and region reconstruction20
This paper proposes three intuitive constraints on the fusion process and model them into three energy terms, i.e., reconstruction error, out-of-focus energy and smoothness regularization, which are formulated into an optimization framework problem to solve a segmentation map.
- Image-guided regularization level set evolution for MR image segmentation and bias field correction20
A level-set-based segmentation method for MR images with intensity inhomogeneous problem is proposed, and the maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework.
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- Fusing multiple features for object tracking based on uncertainty measurement20
An extensive number of comparative experiments show that the proposed algorithm is more stable and robust than the single feature,multiplicative fusion, and additive fusion tracking algorithms.
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- An Interrater Reliability Study of Pulmonary Function Assessment With a Portable Spirometer19
The portable spirometer is an alternative to the conventional device for the measurement of pulmonary function and is expected to provide convenient, operational, and financial advantages.
- A label propagation method using spatial-spectral consistency for hyperspectral image classification19
Through seed selection and label propagation on the graph, the problem of salt-and-pepper appearance is solved elegantly – the noisy labels are highly suppressed and most of the structures are preserved.
- Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition19
This paper presents a kernel coordinate descent algorithm for the LASSO problem in the kernel space, and successfully integrates it in the SRC framework (called KCD-SRC) for face recognition, making it discriminative and robust against illumination variations and occlusions.
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- Medical Image Segmentation Based on Novel Local Order Energy18
A novel localhomogeneous region-based level set segmentation method that interprets the local intensity constraint and integrates this energy into the objective energy function, and minimize the energy function via a level set evolution process.
- NJUST-CCTD: An Image Database for Milling Tool Wear Classification with Deep Learning17
A classification-based image dataset for carbide milling tool wear (NJUST-CCTD) and a novel cemented carbide milling tool wear intelligent classification framework (CMCNet), which performed exceptionally well when categorizing photos intelligently.
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- MsIFT: Multi-Source Image Fusion Transformer16
A novel multi-source image fusion transformer that has non-local fusion receptive fields, and it is more robust to spatial misalignment is proposed, which achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise Classification dataset GRSS-DFC-2013.
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- AsyNCE16
A novel AsyNCE loss is proposed to flexibly disentangle the positive pairs from negative ones in frame-level MIL, which allows for mitigating the uncertainty of false-positive frames effectively and demonstrates the superiority and robustness of the method over state-of-the-art methods.
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- Vehicle Detection in Satellite Images by Incorporating Objectness and Convolutional Neural Network16
This paper proposes a simple and efficient approach to detect vehicles automatically from high-resolution remote sensing images that is more than 10 times faster than traditional methods, and the average accuracy is higher than state-of-the-art methods.
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- Interactive blood-coil simulation in real-time during aneurysm embolization16
This paper describes a complete process for patient-specific simulations of coil embolization, from mesh generation with medical datasets to computation of coil-flow bilateral influence, and proposes a new method for simulating the complex blood flow patterns that take place within the aneurysm, and forsimulating the interaction of coils with this flow.
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- Deep generative video prediction14
A new deep generative video prediction network under the framework of generative adversarial nets is proposed and a novel cross channel color gradient (3CG) loss is designed as a guidance of deblurring.
- Pseudo low rank video representation14
A new Video Low Rank Representation model is first proposed to recover the inherent robust component of a given video, and then the recovered term is introduced to a convolutional Network (denoted pLRN) as an auxiliary pseudo Low Rank guidance.
- Deep unsupervised learning with consistent inference of latent representations14
Experimental results show that the proposed end-to-end approach can significantly improve the performance on various tasks, including image classification and clustering and indicate that the method can guide DNNs to learn more invariant feature representations in comparison with traditional unsupervised methods.
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- Adaptive ε LBP for Background Subtraction14
This work proposes a novel adaptive eLBP operator, in which the threshold is adaptively calculated by compromising two criterions, i.e. the description stability and the discriminative ability, and adopts the naive Bayesian technique to effectively model the probability distribution of local patterns in the pixel level.
- Image deblurring with matrix regression and gradient evolution14
This paper presents a supervised learning algorithm for image deblurring that learns a matrix mapping to map each of its image patches directly to be a new one with more sharp details to be deblurred in way of gradient evolution.
- Fast and effective background subtraction based on eLBP14
A novel LBP (Local Binary Pattern) operator called eLBP is applied to extract the local description of each pixel and a thresholding operator is applied on the probability to determine whether a pixel can be classified as foreground.
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- Nonrigid medical image registration with locally linear reconstruction12
A novel locally linear reconstruction based dissimilarity measurement (LLR) that outperforms the state-of-the-art approaches in registration accuracy and is presented as a multi-resolution nonrigid image registration algorithm.
- Fast construction of plant architectural models based on substructure decomposition12
A new method to construct plant architectural models of most plant species with high efficiency is presented and enables users to check the detail information of plant structure.
- GFFNet: Global Feature Fusion Network for Semantic Segmentation of Large-Scale Remote Sensing Images11
GFFNet serves as a versatile plugin for existing RSI semantic segmentation models, particularly beneficial when the target dataset involves cropping, and proposes a cross feature fusion module that seamlessly integrates local features obtained from the convolutional network with the global contextual features.
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- Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag10
An Adaptive Semantic Guidance Network (ASGN), which instantiates the whole video semantics to different POS-aware semantics with the supervision of part of speech (POS) tag, which improves the interpretability of the network.
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- UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation9
UNIP, a UNified Infrared Pre-training framework, is proposed, which significantly surpasses state-of-the-art (SOTA) infrared or RGB segmentation methods and demonstrates broad potential for application in other modalities, such as RGB and depth.
- Stereo Depth Estimation with Echoes9
An end-to-end framework named StereoEchoes for stereo depth estimation with echoes is proposed, designed to transfer the complementary knowledge of the audio modality to the visual modality at feature level and yield pixel-wise confidence for multimodal depth fusion at output space.
- Visual object tracking via a manifold regularized discriminative dual dictionary model9
A new visual tracking algorithm via a manifold regularized discriminative dual dictionary (MD3) model is proposed, in which the observation model is calculated as the reconstruction error between learned dictionaries and the candidate template.
- Facade repetition detection in a fronto-parallel view with fiducial lines extraction9
This work observes that repetitions are usually horizontally and vertically aligned, and thereby can be localized by the horizontal and vertical lines passing along the repetition boundaries, and proposes to detect repetitions by extracting these fiducial lines.
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- IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting8
This work introduces IF-Bench, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images and proposes a training-free generative visual prompting (GenViP) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts.
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- SafeNet: Scale-normalization and Anchor-based Feature Extraction Network for Person Re-identification8
The advantage of jointly utilizing multi-scale abstract information to learn powerful features over full body and parts is shown, and an anchor-based method to capture the local contents by stacking convolutions of kernels with various aspect ratios is proposed.
- Large Scale Image Annotation via Deep Representation Learning and Tag Embedding Learning8
A novel model based on deep representation learning and tag embedding learning is proposed and an objective function modeling triplet relationships (irrelevant tag, image, relevant tag) is proposed with maximum margin pursuit.
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- Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images7
A novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner and introduces a scale cumulative ascent (SCA) method.
- PAD: Self-Supervised Pre-Training with Patchwise-Scale Adapter for Infrared Images7
A pre-training paradigm called Pre-training with ADapter (PAD), which uses adapters to learn domain-specific features while freezing parameters pre-trained on ImageNet to retain the general feature extraction capability is proposed, applicable to any transformer-based SSL method.
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- Do We Need Binary Features for 3D Reconstruction?7
It is revealed that not all binary features are capable of 3D reconstruction and most of them are inferior to the classical SIFT based method in terms of reconstruction accuracy and completeness with a not significant better computational performance.
- Active Rectification of Curved Document Images Using Structured Beams7
This method uses structured beams projected upon the curved document page to recover the spatial curves on it and uses a Lambertian illumination model as guidance to rectify the non-uniform illumination in the images.
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- Reusable Architecture Growth for Continual Stereo Matching6
This work proposes to perform continual stereo matching where a model is tasked to continually learn new scenes, overcome forgetting previously learned scenes, and continuously predict disparities at inference, by introducing a Reusable Architecture Growth (RAG) framework.
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- Image tag-ranking via pairwise supervision based semi-supervised model6
A novel model which uses images with ranked tag lists as its supervision information and sufficiently exploits the tag relevance to images as well as the ranking structures of tag lists to capture the intrinsic ranking structures.
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- Graph convolutional network with tree-guided anisotropic message passing5
This paper proposes a tree-guided anisotropic GCN, which applies an anisotrop aggregation strategy with competitive expressiveness and a large receptive field, and outperforms baseline methods and recent works on several synthetic benchmarks and datasets from different real-world tasks.
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- Subgraph-aware graph structure revision for spatial–temporal graph modeling4
A subgraph-aware structure revision graph convolution module (SASR-GCM) is designed, which revises the learned stable graph to obtain a dynamic one to automatically infer the dynamics of spatial correlations and can efficiently capture the spatial-temporal correlations and extract complex spatial- Temporal graph features.
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- Adaptive regularization level set evolution for medical image segmentation and bias field correction4
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- Image Guided Tone Mapping with Locally Nonlinear Model4
An effective locally nonlinear tone mapping algorithm for compressing the High Dynamic Range (HDR) images by introducing local gamma correction with adaptive parameters on small overlapping patches over the entire input image.
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- Exploring Universal Principles for Graph Contrastive Learning: A Statistical Perspective3
The comparison experiments with current state-of-the-arts demonstrate the effectiveness and sufficiency of two principles for high-quality graph representations and constructs a general self-supervised framework for negative-free graph contrastive learning.
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- Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation3
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- Incremental Poisson Surface Reconstruction for Large Scale Three-Dimensional Modeling3
A novel Incremental Poisson Surface Reconstruction method based on point clouds and the adaptive octree and providing an octree node classification mechanism to achieve the seamless reconstruction between adjacent blocks is proposed.
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- WS-R-IR Adapter: A Multimodal RGB–Infrared Remote Sensing Framework for Water Surface Object Detection2
The WS-R-IR Adapter is proposed, a parameter-efficient vision foundation model (VFM)-based framework for shipborne RGB–IR object detection that adapts frozen VFM representations via lightweight task-specific designs and demonstrates the effectiveness of parameter-efficient VFM adaptation for multimodal water surface remote sensing.
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- What and Where the Themes Dominate in Image2
A novel framework is proposed, which explicitly applies the known salient objects in image captioning to address the exposure bias problem between the training and the testing modes and formulates two constraints in the theme learning process to encourage distinctiveness between different images.
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- Facade Labeling via Explicit Matrix Factorization2
An alternating optimization algorithm is thus developed to solve the matrix factorization problem, where dynamic programming is used to optimize the block matrices.
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- Cross Modal Deep Model and Gaussian Process Based Model for MSR-Bing Challenge2
A regression based cross modal deep learning model and a Gaussian Process scoring model are proposed that can score the query-image pairs based on the relevance between queries and images in the MSR-Bing Image Retrieval Challenge.
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- Refer-ASV: Referring Multi-Object Tracking in Autonomous Surface Vehicle Navigation Scenes1
Refer-ASV is introduced, the first RMOT dataset tailored for ASV navigation in complex water-surface scenes and RAMOT is proposed, an end-to-end baseline framework that enhances visual–language alignment throughout the tracking pipeline by improving visual–language alignment and robustness in challenging maritime environments.
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- Learning Motion Priors for Efficient Video Object Detection.1
This paper first initialize some motion priors for each location and then use them to propagate features across frames and achieves state-of-the-art performance on the ImageNet VID dataset with real-time speed.
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- Fine-structured object segmentation via neighborhood propagation1
A novel method for fine-structured object segmentation via label propagation, which introduces a novel neighborhood system combining both local and nonlocal connections, with a robust scheme for edge weight calculation, is presented.
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- Transferring Segmentation from Image to Image via Contextual Sparse Representation1
This work proposes a transferring segmentation framework, which aims to automatically segment new images when a single segmented example is given, and proposes to construct a contextual dictionary by incorporating neighboring information as context.
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- Guided tone mapping–
A simple but effective tone mapping operator is proposed, which can achieve impressive results with low complexity in both the formulation and computation of the operator.
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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 2026-10-16. 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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