6 citations · 21 across the 13 of their papers we have counts for
12 papers · 1 filter
High Dynamic Range 3D Gaussian Splatting via Luminance-Chromaticity Decomposition
Kaixuan Zhang, Minxian Li, Mingwu Ren +2
High Dynamic Range (HDR) 3D reconstruction is pivotal for professional content creation in filmmaking and virtual production. Existing methods typically rely on multi-exposure Low…
Dynamic Novel View Synthesis in High Dynamic Range
Kaixuan Zhang, Zhipeng Xiong, Minxian Li +3
High Dynamic Range Novel View Synthesis (HDR NVS) seeks to learn an HDR 3D model from Low Dynamic Range (LDR) training images captured under conventional imaging conditions. Curren…
High Dynamic Range Novel View Synthesis with Single Exposure
Kaixuan Zhang, Hu Wang, Minxian Li +3
High Dynamic Range Novel View Synthesis (HDR-NVS) aims to establish a 3D scene HDR model from Low Dynamic Range (LDR) imagery. Typically, multiple-exposure LDR images are employed…
RobustEMD: Domain Robust Matching for Cross-domain Few-shot Medical Image Segmentation
Yazhou Zhu, Minxian Li, Qiaolin Ye +3
Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current…
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification
Dapeng Jin, Minxian Li
Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we pr…
Unsupervised Clustering Active Learning for Person Re-identification
Wenjing Gao, Minxian Li
Supervised person re-identification (re-id) approaches require a large amount of pairwise manual labeled data, which is not applicable in most real-world scenarios for re-id deploy…
Unsupervised Noisy Tracklet Person Re-identification
Minxian Li, Xiatian Zhu, Shaogang Gong
Existing person re-identification (re-id) methods mostly rely on supervised model learning from a large set of person identity labelled training data per domain. This limits their…
Intra-Camera Supervised Person Re-Identification
Xiangping Zhu, Xiatian Zhu, Minxian Li +3
Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation…
Part-based Multi-stream Model for Vehicle Searching
Ya Sun, Minxian Li, Jianfeng Lu
Due to the enormous requirement in public security and intelligent transportation system, searching an identical vehicle has become more and more important. Current studies usually…
Intra-Camera Supervised Person Re-Identification: A New Benchmark
Xiangping Zhu, Xiatian Zhu, Minxian Li +2
Existing person re-identification (re-id) methods rely mostly on a large set of inter-camera identity labelled training data, requiring a tedious data collection and annotation pro…
Unsupervised Tracklet Person Re-Identification
Minxian Li, Xiatian Zhu, Shaogang Gong
Most existing person re-identification (re-id) methods rely on supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability…
Unsupervised Person Re-identification by Deep Learning Tracklet Association
Minxian Li, Xiatian Zhu, Shaogang Gong
Mostexistingpersonre-identification(re-id)methods relyon supervised model learning on per-camera-pair manually labelled pairwise training data. This leads to poor scalability in pr…