Search Results for author:
Found 104 papers, 26 papers with code
Supervised Edge Attention Network for Accurate Image Instance Segmentation
In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask.
Learnable Evolutionary Multi-Objective Combinatorial Optimization via Sequence-to-Sequence Model
Recent advances in learnable evolutionary algorithms have demonstrated the importance of leveraging population distribution information and historical evolutionary trajectories.
Knowledge-aware Evolutionary Graph Neural Architecture Search
According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset.
Masked Image Modeling Boosting Semi-Supervised Semantic Segmentation
This paradigm learns the knowledge through establishing connections between the masked and visible parts of masked image, during the pixel reconstruction process.
Self-Supervised Learning
Semi-Supervised Semantic Segmentation
Improving the Multi-label Atomic Activity Recognition by Robust Visual Feature and Advanced Attention @ ROAD++ Atomic Activity Recognition 2024
In the multi-label atomic activity recognition task, the robustness of visual feature extraction remains a key challenge, which directly affects the model performance and generalization ability.
MMDS: A Multimodal Medical Diagnosis System Integrating Image Analysis and Knowledge-based Departmental Consultation
In tests on 30 videos of facial paralysis patients, the system demonstrated a grading accuracy of 83. 3%. The second component is the generation of professional medical responses.
AIM 2024 Sparse Neural Rendering Challenge: Methods and Results
In this challenge, 5 teams submitted final results to Track 1 and 4 teams submitted final results to Track 2.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise.
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes.
CSS-Segment: 2nd Place Report of LSVOS Challenge VOS Track
In this technical report, we briefly introduce the solution of our team "yuanjie" for video object segmentation in the 6-th LSVOS Challenge VOS Track at ECCV 2024.
Masked Angle-Aware Autoencoder for Remote Sensing Images
We design a \textit{scaling center crop} operation to create the rotated crop with random orientation on each original image, introducing the explicit angle variation.
Fast and Efficient: Mask Neural Fields for 3D Scene Segmentation
Recent advancements in distilling 2D vision-language foundation models into neural fields, like NeRF and 3DGS, enable open-vocabulary segmentation of 3D scenes from 2D multi-view images without the need for precise 3D annotations.
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
V3Det Challenge 2024 on Vast Vocabulary and Open Vocabulary Object Detection: Methods and Results
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
Technique Report of CVPR 2024 PBDL Challenges
The intersection of physics-based vision and deep learning presents an exciting frontier for advancing computer vision technologies.
Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection
However, most existing deep learning-based methods are aimed at dealing with a specific band selection dataset, and need to retrain parameters for new datasets, which significantly limits their generalizability. To address this issue, a novel multi-teacher multi-objective meta-learning network (M$^3$BS) is proposed for zero-shot hyperspectral band selection.
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning.
Multiplane Prior Guided Few-Shot Aerial Scene Rendering
By investigating NeRF's and Multiplane Image (MPI)'s behavior, we propose to guide the training process of NeRF with a Multiplane Prior.
Automatic Graph Topology-Aware Transformer
Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities.
Transcending Fusion: A Multi-Scale Alignment Method for Remote Sensing Image-Text Retrieval
Our method comprises three key innovations: (1) Multi-scale Cross-Modal Alignment Transformer (MSCMAT), which computes cross-attention between single-scale image features and localized text features, integrating global textual context to derive a matching score matrix within a mini-batch, (2) a multi-scale cross-modal semantic alignment loss that enforces semantic alignment across scales, and (3) a cross-scale multi-modal semantic consistency loss that uses the matching matrix from the largest scale to guide alignment at smaller scales.
Edit-Your-Motion: Space-Time Diffusion Decoupling Learning for Video Motion Editing
In the second stage, we shift focus on learning the appearance features of the source video.
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
In S$^2$Mamba, two selective structured state space models through different dimensions are designed for feature extraction, one for spatial, and the other for spectral, along with a spatial-spectral mixture gate for optimal fusion.
Exploring Beyond Logits: Hierarchical Dynamic Labeling Based on Embeddings for Semi-Supervised Classification
Our approach has the potential to change the paradigm of pseudo-label generation in semi-supervised learning.
Unveiling and Mitigating Generalized Biases of DNNs through the Intrinsic Dimensions of Perceptual Manifolds
Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence.
SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
To eliminate the reliance on such priors, we propose a novel Structure-aware Mixup and Invariance Learning framework (SA-MixNet) for weakly supervised road extraction that improves the model invariance in a data-driven manner.
Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification
In this work, we propose to leverage the geometric information of the feature distribution of the well-represented head class to guide the model to learn the underlying distribution of the tail class.
When large language models meet evolutionary algorithms
Pre-trained large language models (LLMs) have powerful capabilities for generating creative natural text.
Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.
Semantic Connectivity-Driven Pseudo-labeling for Cross-domain Segmentation
This approach formulates pseudo-labels at the connectivity level and thus can facilitate learning structured and low-noise semantics.
Data-Centric Long-Tailed Image Recognition
In the context of the long-tail scenario, models exhibit a strong demand for high-quality data.
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference.
The Robust Semantic Segmentation UNCV2023 Challenge Results
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023.
Changes-Aware Transformer: Learning Generalized Changes Representation
After refinement, the changed pixels in the difference feature space are closer to each other, which facilitates change detection.
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
In this era of rapid technical evolution, this review aims to present a comprehensive review of the recent achievements in deep learning based RSOD methods.
SoccerNet 2023 Challenges Results
More information on the tasks, challenges, and leaderboards are available on https://www. soccer-net. org.
Multi-model fusion for Aerial Vision and Dialog Navigation based on human attention aids
Drones have been widely used in many areas of our daily lives.
DiffUCD:Unsupervised Hyperspectral Image Change Detection with Semantic Correlation Diffusion Model
In this work, we extend the diffusion model's application to the HSI-CD field and propose a novel unsupervised HSI-CD with semantic correlation diffusion model (DiffUCD).
DynamicKD: An Effective Knowledge Distillation via Dynamic Entropy Correction-Based Distillation for Gap Optimizing
This paper proposes a novel knowledge distillation algorithm based on dynamic entropy correction to reduce the gap by adjusting the student instead of the teacher.
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking
To this end, a multi-objective complex network pruning framework based on divide-and-conquer and global performance impairment ranking (EMO-DIR) is proposed in this paper.
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds
To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes.
Ranked #19 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images
Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper.
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search
It is computationally expensive to determine which LL Pareto weight in the LL Pareto weight set is the most appropriate for each UL solution.
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain.
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain.
Delving into Semantic Scale Imbalance
Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations.
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification
Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set.
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
In the exploiting stage, we utilize the extracted NDI to construct a novel negative contrastive learning mechanism and a negative guided instance selection strategy for dealing with the issues of part domination and missing instances, respectively.
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes.
Adaptively Enhancing Facial Expression Crucial Regions via Local Non-Local Joint Network
In the proposed method, two parts are constructed based on facial local and non-local information respectively, where an ensemble of multiple local networks are proposed to extract local features corresponding to multiple facial local regions and a non-local attention network is addressed to explore the significance of each local region.
Facial Expression Recognition
Facial Expression Recognition (FER)
Transformers Meet Visual Learning Understanding: A Comprehensive Review
Three image tasks and two video tasks of computer vision are investigated.
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition
Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks.
MidNet: An Anchor-and-Angle-Free Detector for Oriented Ship Detection in Aerial Images
Ship detection in aerial images remains an active yet challenging task due to arbitrary object orientation and complex background from a bird's-eye perspective.
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection
We convert a lightweight FCOSR model to TensorRT format, which achieves 73. 93 mAP on DOTA1. 0 at a speed of 10. 68 FPS on Jetson Xavier NX with single scale.
Stable and Compact Face Recognition via Unlabeled Data Driven Sparse Representation-Based Classification
The proposed model aims to mine the hidden semantic information and intrinsic structure information of all available data, which is suitable for few labeled samples and proportion imbalance between labeled samples and unlabeled samples problems in frontal face recognition.
LUAI Challenge 2021 on Learning to Understand Aerial Images
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in aerial images.
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models.
Multi-Scale Progressive Attention Network for Video Question Answering
Under the question{'}s guidance of progressive attention, we realize the fusion of all-scale video features.
Unsupervised Outlier Detection using Memory and Contrastive Learning
Instead, we think outlier detection can be done in the feature space by measuring the feature distance between outliers and inliers.
Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images
To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention (SEA) module and a Scale Complementary Mask Branch (SCMB).
Label Distribution Amendment with Emotional Semantic Correlations for Facial Expression Recognition
Based on this, we propose a new method that amends the label distribution of each facial image by leveraging correlations among expressions in the semantic space.
Facial Expression Recognition
Facial Expression Recognition (FER)
Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
The training of deep neural networks (DNNs) always requires intensive resources for both computation and data storage.
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
We propose a balanced coarsening scheme for multilevel hypergraph partitioning.
More Separable and Easier to Segment: A Cluster Alignment Method for Cross-Domain Semantic Segmentation
Feature alignment between domains is one of the mainstream methods for Unsupervised Domain Adaptation (UDA) semantic segmentation.
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment
When the precision of quantization is adjusted, it is necessary to fine-tune the quantized model or minimize the quantization noise, which brings inconvenience in practical applications.
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
Second, the group spatial attention and group spectral attention modules are proposed to extract image features.
MWQ: Multiscale Wavelet Quantized Neural Networks
Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method.
Effective and Fast: A Novel Sequential Single Path Search for Mixed-Precision Quantization
Since model quantization helps to reduce the model size and computation latency, it has been successfully applied in many applications of mobile phones, embedded devices and smart chips.
New Generation Deep Learning For Video Object Dection:A Survery
Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used.
Boosting Gradient for White-Box Adversarial Attacks
Existing white-box attack algorithms can generate powerful adversarial examples.
Contralaterally Enhanced Networks for Thoracic Disease Detection
Aimed at improving the performance of existing detection methods, we propose a deep end-to-end module to exploit the contralateral context information for enhancing feature representations of disease proposals.
C-CNN: Contourlet Convolutional Neural Networks
Second, the spatial-spectral feature fusion strategy is designed to incorporate the spectral features into CNN architecture.
MultiResolution Attention Extractor for Small Object Detection
Small objects are difficult to detect because of their low resolution and small size.
A Light-Weighted Convolutional Neural Network for Bitemporal SAR Image Change Detection
We verify our light-weighted neural network on four sets of bitemporal SAR images.
Counterfactual Detection meets Transfer Learning
We can consider Counterfactuals as belonging in the domain of Discourse structure and semantics, A core area in Natural Language Understanding and in this paper, we introduce an approach to resolving counterfactual detection as well as the indexing of the antecedents and consequents of Counterfactual statements.
A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset.
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification
To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed.
Ranked #7 on
Hyperspectral Image Classification
on Indian Pines
Few-Shot Image Classification
Generative Adversarial Network
+2
PolSF: PolSAR image dataset on San Francisco
In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area.
Hierarchical Feature-Aware Tracking
Moreover, with the novel expert selection strategy, overfitting caused by fixed experts for each frame can be mitigated.
signADAM: Learning Confidences for Deep Neural Networks
In this paper, we propose a new first-order gradient-based algorithm to train deep neural networks.
A Survey of Deep Learning-based Object Detection
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.
Imbalanced Learning-based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
Change detection is a quite challenging task due to the imbalance between unchanged and changed class.
SAR Image Change Detection via Spatial Metric Learning with an Improved Mahalanobis Distance
In this letter, we proposed a spatial metric learning method to obtain a difference image more robust to the speckle by learning a metric from a set of constraint pairs.
PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine
In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine.
Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification
However, their interpretation faces some challenges, e. g., deficiency of labeled data, inadequate utilization of data information and so on.
Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification
Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs).
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1
The training of deep neural networks (DNNs) requires intensive resources both for computation and for storage performance.
Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL).
Parameter-Free Spatial Attention Network for Person Re-Identification
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #20 on
Person Re-Identification
on DukeMTMC-reID
Efficient Computation of Quantized Neural Networks by {−1, +1} Encoding Decomposition
Deep neural networks require extensive computing resources, and can not be efficiently applied to embedded devices such as mobile phones, which seriously limits their applicability.
ASVRG: Accelerated Proximal SVRG
This paper proposes an accelerated proximal stochastic variance reduced gradient (ASVRG) method, in which we design a simple and effective momentum acceleration trick.
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification.
Deep Adaptive Proposal Network for Object Detection in Optical Remote Sensing Images
More recently, a two-stage detector Faster R-CNN is proposed and demonstrated to be a promising tool for object detection in optical remote sensing images, while the sparse and dense characteristic of objects in remote sensing images is complexity.
Polarimetric Convolutional Network for PolSAR Image Classification
Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information.
VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning
In this paper, we propose a simple variant of the original SVRG, called variance reduced stochastic gradient descent (VR-SGD).
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds
In this paper, we propose an accelerated first-order method for geodesically convex optimization, which is the generalization of the standard Nesterov's accelerated method from Euclidean space to nonlinear Riemannian space.
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented.
Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
This paper develops a novel iterative framework for subspace clustering in a learned discriminative feature domain.
Discriminative Nonlinear Analysis Operator Learning: When Cosparse Model Meets Image Classification
Linear synthesis model based dictionary learning framework has achieved remarkable performances in image classification in the last decade.
Target Oriented High Resolution SAR Image Formation via Semantic Information Guided Regularizations
Sparsity-regularized synthetic aperture radar (SAR) imaging framework has shown its remarkable performance to generate a feature enhanced high resolution image, in which a sparsity-inducing regularizer is involved by exploiting the sparsity priors of some visual features in the underlying image.
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new challenge in super-resolution problem.
Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification
For polarimetric SAR (PolSAR) image classification, it is a challenge to classify the aggregated terrain types, such as the urban area, into semantic homogenous regions due to sharp bright-dark variations in intensity.
Multiobjective Optimization of Classifiers by Means of 3-D Convex Hull Based Evolutionary Algorithm
The design of the algorithm proposed in this paper is inspired by indicator-based evolutionary algorithms, where first a performance indicator for a solution set is established and then a selection operator is designed that complies with the performance indicator.
