Search Results for author:
Found 35 papers, 15 papers with code
On 3D Face Reconstruction via Cascaded Regression in Shape Space
Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance.
Glasses Detection Using Convolutional Neural Networks
Glasses detection plays an important role in face recognition and soft biometrices for person identification.
Effective face landmark localization via single deep network
In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data.
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild
To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans.
Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition
This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously.
Robust Face Recognition with Deeply Normalized Depth Images
The PEN depth image is finally passed to $Net_{F}$, which extracts a robust feature representation via another DCNN for face recognition.
Point in, Box out: Beyond Counting Persons in Crowds
In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first.
Distinguishing Individual Red Pandas from Their Faces
We are going to release our database and model in the public domain to promote the research on automatic animal identification and particularly on the technique for protecting red pandas.
Audio-based automatic mating success prediction of giant pandas
Giant pandas, stereotyped as silent animals, make significantly more vocal sounds during breeding season, suggesting that sounds are essential for coordinating their reproduction and expression of mating preference.
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.
Ranked #6 on
RGB-D Salient Object Detection
on NLPR
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
We formulate the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models.
Siamese Network for RGB-D Salient Object Detection and Beyond
Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.
Ranked #3 on
RGB-D Salient Object Detection
on STERE
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
Light Field Salient Object Detection: A Review and Benchmark
Secondly, we benchmark nine representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses, including a comparison between light field SOD and RGB-D SOD models, are achieved.
RGB-D Salient Object Detection via 3D Convolutional Neural Networks
The proposed model, named RD3D, aims at pre-fusion in the encoder stage and in-depth fusion in the decoder stage to effectively promote the full integration of RGB and depth streams.
Watermark Faker: Towards Forgery of Digital Image Watermarking
Digital watermarking has been widely used to protect the copyright and integrity of multimedia data.
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
Depth information has been proved beneficial in RGB-D salient object detection (SOD).
Ranked #2 on
RGB-D Salient Object Detection
on DES
Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking
Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security.
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking
On the surface, correlation filter and convolution filter are usually used for different purposes.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images
With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection
To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.
Depth-Cooperated Trimodal Network for Video Salient Object Detection
However, existing video salient object detection (VSOD) methods only utilize spatiotemporal information and seldom exploit depth information for detection.
Unsupervised Homography Estimation with Coplanarity-Aware GAN
Estimating homography from an image pair is a fundamental problem in image alignment.
Rank-Based Filter Pruning for Real-Time UAV Tracking
Model compression is a promising way to narrow the gap (i. e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection
Inspired by the fact that depth quality is a key factor influencing the accuracy, we propose an efficient depth quality-inspired feature manipulation (DQFM) process, which can dynamically filter depth features according to depth quality.
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning
Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function.
Guided Focal Stack Refinement Network for Light Field Salient Object Detection
To this end, we propose a guided refinement and fusion module (GRFM) to refine focal stacks and aggregate multi-modal features.
3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability
Our method utilizes implicit functions as the 3D shape representation and combines a novel latent-space GAN with a linear subspace model to discover semantic dimensions in the local latent space of 3D shapes.
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation
We have high expectations regarding whether SAM, as a foundation model, can be improved towards highly accurate object segmentation, which is known as dichotomous image segmentation (DIS).
Optimal-Landmark-Guided Image Blending for Face Morphing Attacks
Our proposed method overcomes the limitations of previous approaches by optimizing the morphing landmarks and using Graph Convolutional Networks (GCNs) to combine landmark and appearance features.
Dynamic Patch-aware Enrichment Transformer for Occluded Person Re-Identification
To facilitate the seamless integration of global classification features with the finely detailed local features selected by DPSM, we introduce a novel feature blending module (FBM).
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection
The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner.
Hierarchical Generative Network for Face Morphing Attacks
To address the above issues, we propose a novel morphing attack method to improve the quality of morphed images and better preserve the contributing identities.
