4 citations · 4 across the 7 of their papers we have counts for
8 papers
Unleashing MLLMs on the Edge: A Unified Framework for Cross-Modal ReID via Adaptive SVD Distillation
Hongbo Jiang, Jie Li, Xinqi Cai +4
Practical cloud-edge deployment of Cross-Modal Re-identification (CM-ReID) faces challenges due to maintaining a fragmented ecosystem of specialized cloud models for diverse modali…
RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
Zui Chen, Han Li, Xinhao Zhang +12
We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. Rubik…
One Framework to Rule Them All: Unifying RL-Based and RL-Free Methods in RLHF
Xin Cai
In this article, we primarily examine a variety of RL-based and RL-free methods designed to address Reinforcement Learning from Human Feedback (RLHF) and Large Reasoning Models (LR…
DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flow
Ken Deng, Yuan-Chen Guo, Jingxiang Sun +6
Modern 3D generation methods can rapidly create shapes from sparse or single views, but their outputs often lack geometric detail due to computational constraints. We present Detai…
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
Xingyu Song, Zhan Li, Shi Chen +2
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Netwo…
Revisiting the Encoding of Satellite Image Time Series
Xin Cai, Yaxin Bi, Peter Nicholl +1
Satellite Image Time Series (SITS) representation learning is complex due to high spatiotemporal resolutions, irregular acquisition times, and intricate spatiotemporal interactions…
Tampered VAE for Improved Satellite Image Time Series Classification
Xin Cai, Yaxin Bi, Peter Nicholl
The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectur…
FlatteNet: A Simple Versatile Framework for Dense Pixelwise Prediction
Xin Cai, Yi-Fei Pu
In this paper, we focus on devising a versatile framework for dense pixelwise prediction whose goal is to assign a discrete or continuous label to each pixel for an image. It is we…