1.3k citations · 2.8k across the 97 of their papers we have counts for
16 papers · 1 filter
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
Bingliang Jiao, Lingqiao Liu, Liying Gao +5
In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictabl…
Unsupervised 3D Pose Transfer with Cross Consistency and Dual Reconstruction
Chaoyue Song, Jiacheng Wei, Ruibo Li +2
The goal of 3D pose transfer is to transfer the pose from the source mesh to the target mesh while preserving the identity information (e.g., face, body shape) of the target mesh.…
IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction
Kennard Yanting Chan, Guosheng Lin, Haiyu Zhao +1
We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predi…
Indoor Smartphone SLAM with Learned Echoic Location Features
Wenjie Luo, Qun Song, Zhenyu Yan +2
Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded p…
ManiCLIP: Multi-Attribute Face Manipulation from Text
Hao Wang, Guosheng Lin, Ana García del Molino +3
In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimiza…
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
Xingyi Li, Chaoyi Hong, Yiran Wang +3
We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fai…
CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks
Weide Liu, Chi Zhang, Guosheng Lin +1
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and L…
Collaborative Propagation on Multiple Instance Graphs for 3D Instance Segmentation with Single-point Supervision
Shichao Dong, Ruibo Li, Jiacheng Wei +2
Instance segmentation on 3D point clouds has been attracting increasing attention due to its wide applications, especially in scene understanding areas. However, most existing meth…
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval
Hao Wang, Guosheng Lin, Steven C. H. Hoi +1
This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a nov…
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Zhonghua Wu, Yicheng Wu, Guosheng Lin +2
Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy bu…
Few-shot Open-set Recognition Using Background as Unknowns
Nan Song, Chi Zhang, Guosheng Lin
Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required…
3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image
Hao Wang, Wenhao Shen, Guosheng Lin +2
In this paper, we investigate an open research task of generating 3D cartoon face shapes from single 2D GAN generated human faces and without 3D supervision, where we can also mani…
Long-tailed Recognition by Learning from Latent Categories
Weide Liu, Zhonghua Wu, Yiming Wang +4
In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing str…
A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection
Yukun Su, Jingliang Deng, Ruizhou Sun +2
Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co…
Efficient Few-Shot Object Detection via Knowledge Inheritance
Ze Yang, Chi Zhang, Ruibo Li +2
Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recent…
Self-Training Vision Language BERTs with a Unified Conditional Model
Xiaofeng Yang, Fengmao Lv, Fayao Liu +1
Natural language BERTs are trained with language corpus in a self-supervised manner. Unlike natural language BERTs, vision language BERTs need paired data to train, which restricts…