Search Results for author:
Found 32 papers, 18 papers with code
ATM-Net: Anatomy-Aware Text-Guided Multi-Modal Fusion for Fine-Grained Lumbar Spine Segmentation
Accurate lumbar spine segmentation is crucial for diagnosing spinal disorders.
Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection
Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations.
Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
We propose a novel fully automated Weakly Supervised method using Vision transformer and Multiple instance learning WSVM for tongue extraction and tooth-marked tongue recognition.
Long-Tailed Out-of-Distribution Detection: Prioritizing Attention to Tail
Current out-of-distribution (OOD) detection methods typically assume balanced in-distribution (ID) data, while most real-world data follow a long-tailed distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Towards Adversarial Robustness via Debiased High-Confidence Logit Alignment
Despite the significant advances that deep neural networks (DNNs) have achieved in various visual tasks, they still exhibit vulnerability to adversarial examples, leading to serious security concerns.
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness
Ensuring the robustness of deep neural networks against adversarial attacks remains a fundamental challenge in computer vision.
Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustness
Furthermore, we argue that harmonizing feature maps via graph and employing graph convolution can calibrate contaminated features.
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing
This paper aims to enhance the transferability of adversarial samples in targeted attacks, where attack success rates remain comparatively low.
Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability
In the meta-train step, we leverage the low-frequency components of adversarial samples to boost the transferability of attacks against defense models.
Selective Domain-Invariant Feature for Generalizable Deepfake Detection
To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles.
Cross-Modality Perturbation Synergy Attack for Person Re-identification
For instance, infrared images are typically grayscale, unlike visible images that contain color information.
A brief introduction to a framework named Multilevel Guidance-Exploration Network
Human behavior anomaly detection aims to identify unusual human actions, playing a crucial role in intelligent surveillance and other areas.
Zero-Shot Co-salient Object Detection Framework
Despite recent advancements in deep learning models, these models still rely on training with well-annotated CoSOD datasets.
Boundary Difference Over Union Loss For Medical Image Segmentation
However, current losses for medical image segmentation mainly focus on overall segmentation results, with fewer losses proposed to guide boundary segmentation.
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization
The rapid advancements in computer vision have stimulated remarkable progress in face forgery techniques, capturing the dedicated attention of researchers committed to detecting forgeries and precisely localizing manipulated areas.
Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification
Furthermore, we conduct a comprehensive evaluation of different loss functions within consistent training frameworks, including noise-based and generator-based.
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed Feature
Recent research has shown that Deep Neural Networks (DNNs) are highly vulnerable to adversarial samples, which are highly transferable and can be used to attack other unknown black-box models.
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration
In this work, we provide a comprehensive investigation of the CE loss function and find that the logit margin between the targeted and untargeted classes will quickly obtain saturation in CE, which largely limits the transferability.
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Ranked #2 on
Drone navigation
on University-1652
Federated and Generalized Person Re-identification through Domain and Feature Hallucinating
During local training, the DFS are used to synthesize novel domain statistics with the proposed domain hallucinating, which is achieved by re-weighting DFS with random weights.
Cross-Modality Earth Mover's Distance for Visible Thermal Person Re-Identification
In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment.
Neighborhood Contrastive Learning for Novel Class Discovery
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes.
Source-Free Open Compound Domain Adaptation in Semantic Segmentation
Second, CPSS can reduce the influence of noisy pseudo-labels and also avoid the model overfitting to the target domain during self-supervised learning, consistently boosting the performance on the target and open domains.
Joint Noise-Tolerant Learning and Meta Camera Shift Adaptation for Unsupervised Person Re-Identification
This paper considers the problem of unsupervised person re-identification (re-ID), which aims to learn discriminative models with unlabeled data.
Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification
In this paper, we study the problem of multi-source domain generalization in ReID, which aims to learn a model that can perform well on unseen domains with only several labeled source domains.
OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World
In this paper, we tackle the problem of discovering new classes in unlabeled visual data given labeled data from disjoint classes.
Asymmetric Co-Teaching for Unsupervised Cross Domain Person Re-Identification
This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively.
Ranked #10 on
Unsupervised Domain Adaptation
on Market to Duke
Learning to Adapt Invariance in Memory for Person Re-identification
This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain.
Ranked #8 on
Unsupervised Domain Adaptation
on Market to MSMT
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties.
Domain Adaptive Person Re-Identification
Person Re-Identification
+1
Leveraging Virtual and Real Person for Unsupervised Person Re-identification
For training of deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data.
Non-Local Deep Features for Salient Object Detection
Saliency detection aims to highlight the most relevant objects in an image.
Ranked #2 on
RGB Salient Object Detection
on UCF
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge.
