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Found 23 papers, 9 papers with code
Improving Sentence Classification by Multilingual Data Augmentation and Consensus Learning
Neural network based models have achieved impressive results on the sentence classification task.
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).
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.
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.
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.
Graph Classification via Discriminative Edge Feature Learning
Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets.
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.
Anomaly Detection In Surveillance Videos
audio-visual learning
+1
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).
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.
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.
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.
