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Found 18 papers, 10 papers with code
BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports.
FCA-RAC: First Cycle Annotated Repetitive Action Counting
To address this issue, we propose a framework called First Cycle Annotated Repetitive Action Counting (FCA-RAC).
Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning
Federated learning has gained recognitions as a secure approach for safeguarding local private data in collaborative learning.
Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.
ResNeRF: Geometry-Guided Residual Neural Radiance Field for Indoor Scene Novel View Synthesis
In the first stage, we focus on geometry reconstruction based on SDF representation, which would lead to a good geometry surface of the scene and also a sharp density.
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Therefore, localization has its unique challenges different from segmentation or detection.
GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges
However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment.
Prior Based Human Completion
One is a structure prior, it uses a human parsing map to represent the human body structure.
Layout-Guided Novel View Synthesis from a Single Indoor Panorama
Then, we leverage the room layout prior, a strong structural constraint of the indoor scene, to guide the generation of target views.
Crowd Counting With Partial Annotations in an Image
To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i. e. only using partial annotations in each image as training data.
Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation.
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions.
PPGNet: Learning Point-Pair Graph for Line Segment Detection
In this paper, we present a novel framework to detect line segments in man-made environments.
Semantic Human Matting
SHM is the first algorithm that learns to jointly fit both semantic information and high quality details with deep networks.
Ranked #5 on
Image Matting
on AIM-500
Saliency Detection in 360° Videos
Considering that the 360° videos are usually stored with equirectangular panorama, we propose to implement the spherical convolution on panorama by stretching and rotating the kernel based on the location of patch to be convolved.
Encoding Crowd Interaction With Deep Neural Network for Pedestrian Trajectory Prediction
Specifically, motivated by the residual learning in deep learning, we propose to predict displacement between neighboring frames for each pedestrian sequentially.
Gaze Prediction in Dynamic 360° Immersive Videos
This paper explores gaze prediction in dynamic $360^circ$ immersive videos, emph{i. e.}, based on the history scan path and VR contents, we predict where a viewer will look at an upcoming time.
Personalized Saliency and its Prediction
Specifically, we propose to decompose a personalized saliency map (referred to as PSM) into a universal saliency map (referred to as USM) predictable by existing saliency detection models and a new discrepancy map across users that characterizes personalized saliency.
