Search Results for author:
Found 67 papers, 29 papers with code
Simple Online and Realtime Tracking
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications.
Ranked #2 on
Multi-Object Tracking
on MOT15
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation.
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations.
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
Such mutual consistency encourages the two decoders to have consistent and low-entropy predictions and enables the model to gradually capture generalized features from these unlabeled challenging regions.
Hierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.
Ranked #2 on
Stereo Disparity Estimation
on Scene Flow
Implicit Motion Handling for Video Camouflaged Object Detection
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.
Ranked #2 on
Camouflaged Object Segmentation
on Camouflaged Animal Dataset
(using extra training data)
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition
Long-tailed multi-label visual recognition (LTML) task is a highly challenging task due to the label co-occurrence and imbalanced data distribution.
Ranked #1 on
Long-tail Learning
on COCO-MLT
(using extra training data)
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios.
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos
We constrain the learned shape by tailoring multiple regularization strategies and disentangling geometry and appearance.
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding
We explore constructing the class hierarchy into a graph, with its nodes representing the textual or image features of each category.
Fine-Grained Visual Recognition
Graph Representation Learning
Diversified and Personalized Multi-rater Medical Image Segmentation
To address it, the common practice is to gather multiple annotations from different experts, leading to the setting of multi-rater medical image segmentation.
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information Maximization
In this work, we present a simple-yet-effective self-supervised node representation learning strategy via directly maximizing the mutual information between the hidden representations of nodes and their neighbourhood, which can be theoretically justified by its link to graph smoothing.
Hunting Attributes: Context Prototype-Aware Learning for Weakly Supervised Semantic Segmentation
Inspired by prototype learning theory, we propose leveraging prototype awareness to capture diverse and fine-grained feature attributes of instances.
Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
None of them investigate the influence of the ambiguous nature of the lesion itself. Inspired by image matting, this paper introduces alpha matte as a soft mask to represent uncertain areas in medical scenes and accordingly puts forward a new uncertainty quantification method to fill the gap of uncertainty research for lesion structure.
Deep Laparoscopic Stereo Matching with Transformers
The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection.
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition
Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.
Prompt-driven Latent Domain Generalization for Medical Image Classification
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
Vision-Language Navigation with Random Environmental Mixup
Then, we cross-connect the key views of different scenes to construct augmented scenes.
Ranked #38 on
Vision and Language Navigation
on VLN Challenge
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem.
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.
EventRPG: Event Data Augmentation with Relevance Propagation Guidance
Based on this, we propose EventRPG, which leverages relevance propagation on the spiking neural network for more efficient augmentation.
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification
Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.
Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation
We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.
Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization
Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks.
T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up
Our architecture is orthogonal to StackGAN++ , and focuses on person image generation, with all of them together to enrich the spectrum of GANs for the image generation task.
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Secondly, in CT scans, the lesions are often indistinguishable from the background since the lesion and non-lesion areas may have very similar appearances.
Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement
A probabilistic exploration enhancement method is accordingly devised to encourage intelligent exploration during the architecture search in the latent space, to avoid local optimal in architecture search.
Generative OpenMax for Multi-Class Open Set Classification
We present a conceptually new and flexible method for multi-class open set classification.
Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
Fine-grained classification is a relatively new field that has concentrated on using information from a single image, while ignoring the enormous potential of using video data to improve classification.
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
We present a novel deep convolutional neural network (DCNN) system for fine-grained image classification, called a mixture of DCNNs (MixDCNN).
Subset Feature Learning for Fine-Grained Category Classification
Fine-grained categorisation has been a challenging problem due to small inter-class variation, large intra-class variation and low number of training images.
Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification.
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem
In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data.
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning.
Geometry-constrained Car Recognition Using a 3D Perspective Network
Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category.
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information
Registration is an important task in automated medical image analysis.
ZSTAD: Zero-Shot Temporal Activity Detection
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation
We design a regularisation technique to regulate the domain adaptation.
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning
In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Existing studies for automated melanoma diagnosis are based on single-time point images of lesions.
Robust early-learning: Hindering the memorization of noisy labels
The \textit{early stopping} method therefore can be exploited for learning with noisy labels.
Ranked #32 on
Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
Leveraging Regular Fundus Images for Training UWF Fundus Diagnosis Models via Adversarial Learning and Pseudo-Labeling
We propose the use of a modified cycle generative adversarial network (CycleGAN) model to bridge the gap between regular and UWF fundus and generate additional UWF fundus images for training.
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.
Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization
In this work, we explore unsupervised domain adaptation in retinal vessel segmentation by using entropy-based adversarial learning and transfer normalization layer to train a segmentation network, which generalizes well across domains and requires no annotation of the target domain.
IDENTIFYING CONCEALED OBJECTS FROM VIDEOS
The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.
Early Melanoma Diagnosis with Sequential Dermoscopic Images
In this study, we propose a framework for automated early melanoma diagnosis using sequential dermoscopic images.
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks
The current generation of deep neural networks has achieved close-to-human results on "closed-set" image recognition; that is, the classes being evaluated overlap with the training classes.
Hierarchical Knowledge Guided Learning for Real-world Retinal Diseases Recognition
From a modeling perspective, most deep learning models trained on these datasets may lack the ability to generalize to rare diseases where only a few available samples are presented for training.
Medical Visual Question Answering: A Survey
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images.
Label uncertainty-guided multi-stream model for disease screening
The main idea is dividing the images into simple and hard cases by uncertainty information, and then developing a multi-stream network to deal with different cases separately.
Flexible Sampling for Long-tailed Skin Lesion Classification
To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task.
Camera Adaptation for Fundus-Image-Based CVD Risk Estimation
Most high-quality retinography databases ready for research are collected from high-end fundus cameras, and there is a significant domain discrepancy between different cameras.
Pseudo-Pair based Self-Similarity Learning for Unsupervised Person Re-identification
In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations.
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation
Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Accordingly, the learned prototypes preserve the semantic class relations in the embedding space and we can predict the label of an image by assigning its feature to the nearest hyperbolic class prototype.
3D Matting: A Soft Segmentation Method Applied in Computed Tomography
It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation.
3D Matting: A Benchmark Study on Soft Segmentation Method for Pulmonary Nodules Applied in Computed Tomography
In this work, we introduce the image matting into the 3D scenes and use the alpha matte, i. e., a soft mask, to describe lesions in a 3D medical image.
Multimorbidity Content-Based Medical Image Retrieval Using Proxies
Medical imaging data, such as radiology images, are often multimorbidity; a single sample may have more than one pathology present.
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision
We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.
Towards Open-Scenario Semi-supervised Medical Image Classification
Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification.
Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification
An ugly duckling is an obviously different skin lesion from surrounding lesions of an individual, and the ugly duckling sign is a criterion used to aid in the diagnosis of cutaneous melanoma by differentiating between highly suspicious and benign lesions.
Privacy-preserving Early Detection of Epileptic Seizures in Videos
In this work, we contribute towards the development of video-based epileptic seizure classification by introducing a novel framework (SETR-PKD), which could achieve privacy-preserved early detection of seizures in videos.
Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering
We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories.
Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis.
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities
Our ablation study demonstrates the importance of the proposed modules with CNN-Transformer networks and the convolutional blocks in Transformer for improving the performance of brain tumor segmentation with missing modalities.
