Search Results for author:
Found 48 papers, 15 papers with code
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
Chest X-rays (CXRs) are the most frequently performed imaging examinations in clinical settings.
EgoExo-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos
Next, we employ a video diffusion model to predict future ego-frames using the first ego-frame and textual instructions, while incorporating the HOI masks as structural guidance to enhance prediction quality.
Repurposing the scientific literature with vision-language models
Using NeuroPubs, VLMs generated publication-ready graphical abstracts (70% of 100 abstracts) and board-style questions indistinguishable from human-written ones (54% of 89, 587 questions).
Text-promptable Propagation for Referring Medical Image Sequence Segmentation
We develop a strong baseline model, Text-Promptable Propagation (TPP), designed to exploit the intrinsic relationships among sequential images and their associated textual descriptions.
FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training
We proposed FineMedLM-o1, which leverages high-quality synthetic medical data and long-form reasoning data for Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), enabling advanced dialogue and deep reasoning capabilities.
Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection
Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by their non-invasiveness, cost-effectiveness, and convenience.
Alzheimer's Disease Detection
Automatic Speech Recognition
+6
Cross-Fundus Transformer for Multi-modal Diabetic Retinopathy Grading with Cataract
Diabetic retinopathy (DR) is a leading cause of blindness worldwide and a common complication of diabetes.
CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
Most existing researches in MMQA only focus on two modalities such as image-text QA, table-text QA and chart-text QA, and there remains a notable scarcity in studies that investigate the joint analysis of text, tables, and charts.
A Medical Multimodal Large Language Model for Pediatric Pneumonia
This dataset comprised 2D chest X-ray images, 3D chest CT images, corresponding radiology reports, and outpatient and inpatient records.
ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising
We introduce a gain evaluation strategy to calculate information gain, aiding the model in learning helpful information for the target domain and providing the ability to reject noisy samples, thus avoiding negative transfer.
Clever Hans Effect Found in Automatic Detection of Alzheimer's Disease through Speech
We uncover an underlying bias present in the audio recordings produced from the picture description task of the Pitt corpus, the largest publicly accessible database for Alzheimer's Disease (AD) detection research.
QMix: Quality-aware Learning with Mixed Noise for Robust Retinal Disease Diagnosis
Previous noise learning methods mainly considered noise arising from images being mislabeled, i. e. label noise, assuming that all mislabeled images are of high image quality.
Domain Adaptation Using Pseudo Labels for COVID-19 Detection
In response to the need for rapid and accurate COVID-19 diagnosis during the global pandemic, we present a two-stage framework that leverages pseudo labels for domain adaptation to enhance the detection of COVID-19 from CT scans.
Advancing COVID-19 Detection in 3D CT Scans
To make a more accurate diagnosis of COVID-19, we propose a straightforward yet effective model.
Anatomical Structure-Guided Medical Vision-Language Pre-training
For finding and existence, we regard them as image tags, applying an image-tag recognition decoder to associate image features with their respective tags within each sample and constructing soft labels for contrastive learning to improve the semantic association of different image-report pairs.
Retrieval-Augmented Egocentric Video Captioning
In this paper, (1) we develop EgoInstructor, a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos to enhance the video captioning of egocentric videos.
Large Language Models are Complex Table Parsers
With the Generative Pre-trained Transformer 3. 5 (GPT-3. 5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering (QA) research has primarily centered around general QA tasks based on GPT, neglecting the specific challenges posed by Complex Table QA.
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM).
A WINNER+ Based 3-D Non-Stationary Wideband MIMO Channel Model
In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed.
Enhanced Knowledge Injection for Radiology Report Generation
Automatic generation of radiology reports holds crucial clinical value, as it can alleviate substantial workload on radiologists and remind less experienced ones of potential anomalies.
Open-Set Image Tagging with Multi-Grained Text Supervision
Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer
Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields.
DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images
Computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness.
Tag2Text: Guiding Vision-Language Model via Image Tagging
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features.
Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision
The former aims to infer all masked entities in the caption given the group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification
In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading.
CMC v2: Towards More Accurate COVID-19 Detection with Discriminative Video Priors
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022).
Cross-Field Transformer for Diabetic Retinopathy Grading on Two-field Fundus Images
However, automatic DR grading based on two-field fundus photography remains a challenging task due to the lack of publicly available datasets and effective fusion strategies.
End-to-End Stochastic Optimization with Energy-Based Model
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters.
Deep-OCTA: Ensemble Deep Learning Approaches for Diabetic Retinopathy Analysis on OCTA Images
In the image quality assessment task, we create an ensemble of InceptionV3, SE-ResNeXt, and Vision Transformer models.
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP.
Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection
In this paper, we analyze the modality asynchrony and undifferentiated instances phenomena of the multiple instance learning (MIL) procedure, and further investigate its negative impact on weakly-supervised audio-visual learning.
Ranked #10 on
Anomaly Detection In Surveillance Videos
on XD-Violence
Anomaly Detection In Surveillance Videos
audio-visual learning
+1
Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization
Although audio-visual representation has been proved to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated.
FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis
This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022).
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.
Reconfigurable intelligent surfaces: Channel characterization and modeling
Reconfigurable intelligent surfaces (RISs) are two dimensional (2D) metasurfaces which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements.
CREAM: Weakly Supervised Object Localization via Class RE-Activation Mapping
In this paper, we empirically prove that this problem is associated with the mixup of the activation values between less discriminative foreground regions and the background.
Self-Supervised Video Representation Learning with Motion-Contrastive Perception
Visual-only self-supervised learning has achieved significant improvement in video representation learning.
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
User sessions empower many search and recommendation tasks on a daily basis.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
MM-Pyramid: Multimodal Pyramid Attentional Network for Audio-Visual Event Localization and Video Parsing
Recognizing and localizing events in videos is a fundamental task for video understanding.
MPN: Multimodal Parallel Network for Audio-Visual Event Localization
The localization subnetwork consists of Multimodal Bottleneck Attention Module (MBAM), which is designed to extract fine-grained segment-level contents.
Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction
We argue that the event extraction models so trained are inherently label-hungry, and can generalize poorly across domains and text genres. We propose a reading comprehension framework for event extraction. Specifically, we formulate event detection as a textual entailment prediction problem, and argument detection as a question answer-ing problem.
Transformer-Based Neural Text Generation with Syntactic Guidance
We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation.
Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning
When watching videos, the occurrence of a visual event is often accompanied by an audio event, e. g., the voice of lip motion, the music of playing instruments.
Learning Error-Driven Curriculum for Crowd Counting
Density regression has been widely employed in crowd counting.
Learning Fair Representations via an Adversarial Framework
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval.
Representation Learning for Scale-free Networks
Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while the macroscopic scale-free property is largely ignored.
