Search Results for author:
Found 20 papers, 7 papers with code
VirtualModel: Generating Object-ID-retentive Human-object Interaction Image by Diffusion Model for E-commerce Marketing
Due to the significant advances in large-scale text-to-image generation by diffusion model (DM), controllable human image generation has been attracting much attention recently.
ShoeModel: Learning to Wear on the User-specified Shoes via Diffusion Model
Specifically, we propose a shoe-wearing system, called Shoe-Model, to generate plausible images of human legs interacting with the given shoes.
Strictly-ID-Preserved and Controllable Accessory Advertising Image Generation
Customized generative text-to-image models have the ability to produce images that closely resemble a given subject.
Virtual Classification: Modulating Domain-Specific Knowledge for Multidomain Crowd Counting
Multidomain crowd counting aims to learn a general model for multiple diverse datasets.
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background.
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research.
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing.
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing
Along with the widespread use of face recognition systems, their vulnerability has become highlighted.
Dense Learning based Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data.
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting
In crowd counting, due to the problem of laborious labelling, it is perceived intractability of collecting a new large-scale dataset which has plentiful images with large diversity in density, scene, etc.
VirFace: Enhancing Face Recognition via Unlabeled Shallow Data
In this paper, we propose a novel face recognition method, named VirFace, to effectively apply the unlabeled shallow data for face recognition.
MedSRGAN: medical images super-resolution using generative adversarial networks
Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI).
Mixed High-Order Attention Network for Person Re-Identification
Then, rethinking person ReID as a zero-shot learning problem, we propose the Mixed High-Order Attention Network (MHN) to further enhance the discrimination and richness of attention knowledge in an explicit manner.
Ranked #4 on
Person Re-Identification
on CUHK03-C
Person Re-Identification
Vocal Bursts Intensity Prediction
+1
Hybrid-Attention based Decoupled Metric Learning for Zero-Shot Image Retrieval
In zero-shot image retrieval (ZSIR) task, embedding learning becomes more attractive, however, many methods follow the traditional metric learning idea and omit the problems behind zero-shot settings.
Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning
In this paper, different from the approaches on learning the loss structures, we propose a robust SNR distance metric based on Signal-to-Noise Ratio (SNR) for measuring the similarity of image pairs for deep metric learning.
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
However, in this paper, we first emphasize that the generalization ability is a core ingredient of this 'good' embedding as well and largely affects the metric performance in zero-shot settings as a matter of fact.
Virtual Class Enhanced Discriminative Embedding Learning
Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged.
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
We assume that problems inside are inadequate use of supervision information and imbalance between semantics and details at all level feature maps in CNN even with Feature Pyramid Networks (FPN).
ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating
Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performance-sensitive.
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation
In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration.
