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Found 32 papers, 8 papers with code
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis
We propose a multimodal spatiotemporal graph neural network (STG) framework to predict colorectal cancer liver metastasis (CRLM) progression.
HER2 Expression Prediction with Flexible Multi-Modal Inputs via Dynamic Bidirectional Reconstruction
Current HER2 assessment models for breast cancer predominantly analyze H&E or IHC images in isolation, despite clinical reliance on their synergistic interpretation.
PupiNet: Seamless OCT-OCTA Interconversion Through Wavelet-Driven and Multi-Scale Attention Mechanisms
Given the technical complexity of OCTA image acquisition and potential mechanical artifacts, this study proposes a bidirectional image conversion framework called PupiNet, which accurately achieves bidirectional transformation between 3D OCT and 3D OCTA.
4D-ACFNet: A 4D Attention Mechanism-Based Prognostic Framework for Colorectal Cancer Liver Metastasis Integrating Multimodal Spatiotemporal Features
Postoperative prognostic prediction for colorectal cancer liver metastasis (CRLM) remains challenging due to tumor heterogeneity, dynamic evolution of the hepatic microenvironment, and insufficient multimodal data fusion.
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering
Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness.
A Residual Multi-task Network for Joint Classification and Regression in Medical Imaging
Detection and classification of pulmonary nodules is a challenge in medical image analysis due to the variety of shapes and sizes of nodules and their high concealment.
RetinaRegen: A Hybrid Model for Readability and Detail Restoration in Fundus Images
Fundus image quality is crucial for diagnosing eye diseases, but real-world conditions often result in blurred or unreadable images, increasing diagnostic uncertainty.
DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in Segmenting Hemorrhagic Lesions from Fundus Images
The hemorrhagic lesion segmentation plays a critical role in ophthalmic diagnosis, directly influencing early disease detection, treatment planning, and therapeutic efficacy evaluation.
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to represent driving-specific scenarios accurately, particularly in complex interactions and long-tail cases.
Active Fake: DeepFake Camouflage
However, these methods mainly concentrate on capturing the blending inconsistency in DeepFake faces, raising a new security issue, termed Active Fake, emerges when individuals intentionally create blending inconsistency in their authentic videos to evade responsibility.
A Diffusion Model Based Quality Enhancement Method for HEVC Compressed Video
Most of the existing methods need to train corresponding models for compressed videos with different quantization parameters to improve the quality of compressed videos.
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection
One straightforward way to address this issue is to simultaneous process multi-face by integrating face extraction and forgery detection in an end-to-end fashion by adapting advanced object detection architectures.
FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training
In light of these two traces, our method can effectively expose DeepFakes by identifying them.
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks
Network pruning is an effective method of model compression to handle such problems.
CenterNet++ for Object Detection
Our approach, named CenterNet, detects each object as a triplet keypoints (top-left and bottom-right corners and the center keypoint).
Ranked #38 on
Object Detection
on COCO test-dev
Location-Sensitive Visual Recognition with Cross-IOU Loss
Object detection, instance segmentation, and pose estimation are popular visual recognition tasks which require localizing the object by internal or boundary landmarks.
Ranked #60 on
Object Detection
on COCO test-dev
DeepFake-o-meter: An Open Platform for DeepFake Detection
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction
In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality.
LandmarkGAN: Synthesizing Faces from Landmarks
Face synthesis is an important problem in computer vision with many applications.
Corner Proposal Network for Anchor-free, Two-stage Object Detection
On the MS-COCO dataset, CPN achieves an AP of 49. 2% which is competitive among state-of-the-art object detection methods.
Ranked #98 on
Object Detection
on COCO test-dev
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
CenterNet: Keypoint Triplets for Object Detection
In object detection, keypoint-based approaches often suffer a large number of incorrect object bounding boxes, arguably due to the lack of an additional look into the cropped regions.
Ranked #121 on
Object Detection
on COCO test-dev
Exposing GAN-synthesized Faces Using Landmark Locations
Generative adversary networks (GANs) have recently led to highly realistic image synthesis results.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
Ranked #15 on
Fine-Grained Image Classification
on CUB-200-2011
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
Besides, we propose attention regularization and attention dropout to weakly supervise the generating process of attention maps.
Multi-Scale Supervised Network for Human Pose Estimation
Human pose estimation is an important topic in computer vision with many applications including gesture and activity recognition.
Multi-Scale Structure-Aware Network for Human Pose Estimation
We develop a robust multi-scale structure-aware neural network for human pose estimation.
Ranked #13 on
Pose Estimation
on MPII Human Pose
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor.
Ranked #16 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
Contrast Enhancement Estimation for Digital Image Forensics
Our method recovers the original pixel histogram and the contrast enhancement simultaneously from a single image with an iterative algorithm.
Geometric Hypergraph Learning for Visual Tracking
Graph based representation is widely used in visual tracking field by finding correct correspondences between target parts in consecutive frames.
UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking
In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset.
Improving Image Restoration with Soft-Rounding
In this work, we describe an effective and efficient approach to incorporate the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration framework.
