activity
20172026
most citedBenchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

58 citations · 212 across the 41 of their papers we have counts for

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12 papers · 1 filter

cs.CV2025

PDGS: Part-Level Decoupling and Continuous Deformation of Articulated Objects via Gaussian Splatting

Haowen Wang, Xiaoping Yuan, Zhao Jin +6

Articulated objects are ubiquitous and important in robotics, AR/VR, and digital twins. Most self-supervised methods for articulated object modeling reconstruct discrete interactio…

cs.CV2025

Occupancy World Model for Robots

Zhang Zhang, Qiang Zhang, Wei Cui +12

Understanding and forecasting the scene evolutions deeply affect the exploration and decision of embodied agents. While traditional methods simulate scene evolutions through trajec…

cs.CV20242 cited

2023 Low-Power Computer Vision Challenge (LPCVC) Summary

Leo Chen, Benjamin Boardley, Ping Hu +27

This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of compu…

cs.CV2024

SM: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

Haowen Wang, Zhen Zhao, Zhao Jin +6

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused o…

cs.CV2023

Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering

Chengxiang Yin, Zhengping Che, Kun Wu +2

Visual Question Answering (VQA) has emerged as one of the most challenging tasks in artificial intelligence due to its multi-modal nature. However, most existing VQA methods are in…

cs.CV2023

Cross-Modal Reasoning with Event Correlation for Video Question Answering

Chengxiang Yin, Zhengping Che, Kun Wu +3

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the…