Search Results for author:
Found 28 papers, 7 papers with code
RenderWorld: World Model with Self-Supervised 3D Label
End-to-end autonomous driving with vision-only is not only more cost-effective compared to LiDAR-vision fusion but also more reliable than traditional methods.
Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving
Large Vision-Language Models (LVLMs) have recently garnered significant attention, with many efforts aimed at harnessing their general knowledge to enhance the interpretability and robustness of autonomous driving models.
Towards A Comprehensive Visual Saliency Explanation Framework for AI-based Face Recognition Systems
Subsequently, the explanation framework conceives a new evaluation methodology that offers quantitative measurement and comparison of the performance of general visual saliency explanation methods in face recognition.
Explainable Face Verification via Feature-Guided Gradient Backpropagation
Recent years have witnessed significant advancement in face recognition (FR) techniques, with their applications widely spread in people's lives and security-sensitive areas.
Towards the Detection of AI-Synthesized Human Face Images
However, the problem of detecting purely synthesized face images has been explored to a lesser extent.
OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries
Occupancy prediction has increasingly garnered attention in recent years for its fine-grained understanding of 3D scenes.
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations.
Discriminative Deep Feature Visualization for Explainable Face Recognition
To further interpret the decision of an FR model, a novel visual saliency explanation algorithm has been proposed.
Towards Visual Saliency Explanations of Face Verification
In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios.
Assessment Framework for Deepfake Detection in Real-world Situations
In this paper, a more reliable assessment framework is proposed to evaluate the performance of learning-based deepfake detectors in more realistic settings.
Explanation of Face Recognition via Saliency Maps
Recent studies have explored use of visual saliency maps as an explanation, but they often lack a deeper analysis in the context of face recognition.
Impact of Video Processing Operations in Deepfake Detection
Moreover, substantial experiments have been carried out on three popular deepfake detectors, which give detailed analyses on the impact of each operation and bring insights to foster future research.
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Cross-resolution face recognition has become a challenging problem for modern deep face recognition systems.
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field.
Cross-domain Few-shot Segmentation with Transductive Fine-tuning
Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images.
Unsupervised Homography Estimation with Coplanarity-Aware GAN
Estimating homography from an image pair is a fundamental problem in image alignment.
A Novel Framework for Assessment of Learning-based Detectors in Realistic Conditions with Application to Deepfake Detection
Deep convolutional neural networks have shown remarkable results on multiple detection tasks.
A New Approach to Improve Learning-based Deepfake Detection in Realistic Conditions
Extensive experiments show that the proposed data augmentation scheme improves generalization ability to unpredictable data distortions and unseen datasets.
Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training.
One-shot Unsupervised Domain Adaptation
Semantic Segmentation
+2
Impact of Benign Modifications on Discriminative Performance of Deepfake Detectors
Deepfakes are becoming increasingly popular in both good faith applications such as in entertainment and maliciously intended manipulations such as in image and video forgery.
Contour Transformer Network for One-shot Segmentation of Anatomical Structures
In this work, we present Contour Transformer Network (CTN), a one-shot anatomy segmentation method with a naturally built-in human-in-the-loop mechanism.
Learning to Segment Anatomical Structures Accurately from One Exemplar
Accurate segmentation of critical anatomical structures is at the core of medical image analysis.
Structured Landmark Detection via Topology-Adapting Deep Graph Learning
Image landmark detection aims to automatically identify the locations of predefined fiducial points.
Ranked #4 on
Face Alignment
on COFW-68
Does Haze Removal Help CNN-based Image Classification?
Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images.
Design Identification of Curve Patterns on Cultural Heritage Objects: Combining Template Matching and CNN-based Re-Ranking
The goal of this paper is to address the challenging problem of automatically identifying the underlying full design of curve patterns from such a sherd.
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval.
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map.
Scale-constrained Unsupervised Evaluation Method for Multi-scale Image Segmentation
Unsupervised evaluation of segmentation quality is a crucial step in image segmentation applications.
