33 citations · 66 across the 12 of their papers we have counts for
10 papers · 1 filter
UnCommon Objects in 3D
Xingchen Liu, Piyush Tayal, Jianyuan Wang +10
We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-reso…
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
Xiaoyuan Guo, Jiali Duan, C. -C. Jay Kuo +2
Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning. In contrast, visual mo…
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
Xiaoyuan Guo, Jiali Duan, Saptarshi Purkayastha +3
Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied…
Multi-modal Alignment using Representation Codebook
Jiali Duan, Liqun Chen, Son Tran +4
Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusi…
Vision-Language Pre-Training with Triple Contrastive Learning
Jinyu Yang, Jiali Duan, Son Tran +6
Vision-language representation learning largely benefits from image-text alignment through contrastive losses (e.g., InfoNCE loss). The success of this alignment strategy is attrib…
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey
Jiali Duan, C. -C. Jay Kuo
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long…
SLADE: A Self-Training Framework For Distance Metric Learning
Jiali Duan, Yen-Liang Lin, Son Tran +2
Most existing distance metric learning approaches use fully labeled data to learn the sample similarities in an embedding space. We present a self-training framework, SLADE, to imp…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…
Interpretable Convolutional Neural Networks via Feedforward Design
C. -C. Jay Kuo, Min Zhang, Siyang Li +2
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any B…
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition
Jiali Duan, Shuai Zhou, Jun Wan +2
Recently, the popularity of depth-sensors such as Kinect has made depth videos easily available while its advantages have not been fully exploited. This paper investigates, for ges…