Search Results for author:
Found 26 papers, 11 papers with code
Highly Accurate Dichotomous Image Segmentation
We present a systematic study on a new task called dichotomous image segmentation (DIS) , which aims to segment highly accurate objects from natural images.
Ranked #4 on
Dichotomous Image Segmentation
on DIS-TE1
MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving
We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigate the modality heterogeneity.
Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection
Leveraging LiDAR-based detectors or real LiDAR point data to guide monocular 3D detection has brought significant improvement, e. g., Pseudo-LiDAR methods.
Ranked #1 on
Monocular 3D Object Detection
on KITTI Cyclist Hard
(using extra training data)
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image.
Ranked #1 on
Monocular 3D Object Detection
on KITTI Cars Moderate
(AP metric)
M3DSSD: Monocular 3D Single Stage Object Detector
In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores.
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection.
From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder
In this paper, we present an Intersection-over-Union (IoU) guided two-stage 3D object detector with a voxel-to-point decoder.
Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud
We propose an attentive module to fit the sparse feature maps to dense mostly on the object regions through the deformable convolution tower and the supervised mask-guided attention.
High-resolution Iterative Feedback Network for Camouflaged Object Detection
Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings.
Scribble-based Boundary-aware Network for Weakly Supervised Salient Object Detection in Remote Sensing Images
To this end, in this paper, we propose a novel weakly-supervised salient object detection framework to predict the saliency of remote sensing images from sparse scribble annotations.
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Segmentation is a fundamental task in medical image analysis.
Ranked #1 on
Optic Disc Segmentation
on REFUGE
Non-rigid 3D Shape Registration using an Adaptive Template
We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to accelerate the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD.
Functional Faces: Groupwise Dense Correspondence Using Functional Maps
In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps.
A 3D Morphable Model of Craniofacial Shape and Texture Variation
We present a fully automatic pipeline to train 3D Morphable Models (3DMMs), with contributions in pose normalisation, dense correspondence using both shape and texture information, and high quality, high resolution texture mapping.
Penalizing small errors using an Adaptive Logarithmic Loss
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
PointAE: Point Auto-Encoder for 3D Statistical Shape and Texture Modelling
The data with refined correspondence can be fed to the PointAE again and bootstrap the constructed statistical models.
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds
In order to obtain a more accurate IoU prediction, we propose a 3D IoU-Net with IoU sensitive feature learning and an IoU alignment operation.
A Human Ear Reconstruction Autoencoder
The ear, as an important part of the human head, has received much less attention compared to the human face in the area of computer vision.
Adversarially robust deepfake media detection using fused convolutional neural network predictions
The proposed technique outperforms state-of-the-art models with 96. 5% accuracy, when tested on publicly available DeepFake Detection Challenge (DFDC) test data, comprising of 400 videos.
Video Transformer for Deepfake Detection with Incremental Learning
The comprehensive experiments on various public deepfake datasets demonstrate that the proposed video transformer model with incremental learning achieves state-of-the-art performance in the deepfake video detection task with enhanced feature learning from the sequenced data.
CpT: Convolutional Point Transformer for 3D Point Cloud Processing
It achieves this feat due to its effectiveness in creating a novel and robust attention-based point set embedding through a convolutional projection layer crafted for processing dynamically local point set neighbourhoods.
Semi-Supervised Cross-Modal Salient Object Detection with U-Structure Networks
Salient Object Detection (SOD) is a popular and important topic aimed at precise detection and segmentation of the interesting regions in the images.
Laplacian ICP for Progressive Registration of 3D Human Head Meshes
We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP).
UniVision: A Unified Framework for Vision-Centric 3D Perception
The past few years have witnessed the rapid development of vision-centric 3D perception in autonomous driving.
GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation
We propose a novel transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation and demonstrate its state-of-the-art performance.
