activity
20102025
most citedCOVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution

115 citations · 293 across the 31 of their papers we have counts for

collaborators

46 papers

cs.CV2025

The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning

Mingkai Tian, Guorong Li, Yuankai Qi +4

Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem le…

cs.CV20242 cited

Categorical Keypoint Positional Embedding for Robust Animal Re-Identification

Yuhao Lin, Lingqiao Liu, Javen Shi

Animal re-identification (ReID) has become an indispensable tool in ecological research, playing a critical role in tracking population dynamics, analyzing behavioral patterns, and…

cs.CE2024

InvariantStock: Learning Invariant Features for Mastering the Shifting Market

Haiyao Cao, Jinan Zou, Yuhang Liu +4

Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution…

cs.LG2024

Rethinking State Disentanglement in Causal Reinforcement Learning

Haiyao Cao, Zhen Zhang, Panpan Cai +7

One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical supp…

cs.CV202211 cited

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…

cs.CV20228 cited

Implicit Sample Extension for Unsupervised Person Re-Identification

Xinyu Zhang, Dongdong Li, Zhigang Wang +5

Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different…