39 citations · 200 across the 21 of their papers we have counts for
21 papers
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Zhuoxiao Chen, Yadan Luo, Zheng Wang +2
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing…
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Yadan Luo, Zhuoxiao Chen, Zhen Fang +3
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (A…
Federated Zero-Shot Learning for Visual Recognition
Zhi Chen, Yadan Luo, Sen Wang +2
Zero-shot learning is a learning regime that recognizes unseen classes by generalizing the visual-semantic relationship learned from the seen classes. To obtain an effective ZSL mo…
Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation
Zixin Wang, Yadan Luo, Peng-Fei Zhang +2
A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior…
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +2
Generalized Zero-Shot Learning (GZSL) aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promisi…
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +3
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…
Context-Aware Attention-Based Data Augmentation for POI Recommendation
Yang Li, Yadan Luo, Zheng Zhang +2
With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendati…
Enhanced Modality Transition for Image Captioning
Ziwei Wang, Yadan Luo, Zi Huang
Image captioning model is a cross-modality knowledge discovery task, which targets at automatically describing an image with an informative and coherent sentence. To generate the c…
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Yadan Luo, Zi Huang, Hongxu Chen +2
Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions…
Adversarial Bipartite Graph Learning for Video Domain Adaptation
Yadan Luo, Zi Huang, Zijian Wang +2
Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant sp…
Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zi Huang +1
Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation ap…
ORD: Object Relationship Discovery for Visual Dialogue Generation
Ziwei Wang, Zi Huang, Yadan Luo +1
With the rapid advancement of image captioning and visual question answering at single-round level, the question of how to generate multi-round dialogue about visual content has no…
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling
Yadan Luo, Zi Huang, Zheng Zhang +3
Meta-learning for few-shot learning allows a machine to leverage previously acquired knowledge as a prior, thus improving the performance on novel tasks with only small amounts of…
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Zijian Wang, Zheng Zhang, Yadan Luo +1
Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performa…
CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language
Zhi Chen, Jingjing Li, Yadan Luo +2
Existing methods using generative adversarial approaches for Zero-Shot Learning (ZSL) aim to generate realistic visual features from class semantics by a single generative network,…
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Yadan Luo, Zi Huang, Zheng Zhang +3
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way. In this paper, we address three criti…
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
Yadan Luo, Ziwei Wang, Zi Huang +2
With the increasing number of online stores, there is a pressing need for intelligent search systems to understand the item photos snapped by customers and search against large-sca…
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Yadan Luo, Zi Huang, Yang Li +3
Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly g…
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Yadan Luo, Ziwei Wang, Zi Huang +2
Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotatio…
Binary Subspace Coding for Query-by-Image Video Retrieval
Ruicong Xu, Yang Yang, Yadan Luo +3
The query-by-image video retrieval (QBIVR) task has been attracting considerable research attention recently. However, most existing methods represent a video by either aggregating…
Zero-Shot Hashing via Transferring Supervised Knowledge
Yang Yang, Weilun Chen, Yadan Luo +3
Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge e.g. semantic labels or pair-wise relationship) associa…