306 citations · 334 across the 8 of their papers we have counts for
8 papers
MUVOD: A Novel Multi-view Video Object Segmentation Dataset and A Benchmark for 3D Segmentation
Bangning Wei, Joshua Maraval, Meriem Outtas +3
The application of methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS) have steadily gained popularity in the field of 3D object segmentation in static…
Intra-Modal Constraint Loss For Image-Text Retrieval
Jianan Chen, Lu Zhang, Qiong Wang +2
Cross-modal retrieval has drawn much attention in both computer vision and natural language processing domains. With the development of convolutional and recurrent neural networks,…
Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms
Xianye Ben, Yi Ren, Junping Zhang +4
Unlike the conventional facial expressions, micro-expressions are involuntary and transient facial expressions capable of revealing the genuine emotions that people attempt to hide…
Learning Vision Transformer with Squeeze and Excitation for Facial Expression Recognition
Mouath Aouayeb, Wassim Hamidouche, Catherine Soladie +2
As various databases of facial expressions have been made accessible over the last few decades, the Facial Expression Recognition (FER) task has gotten a lot of interest. The multi…
Bipartite Graph based Construction of Compressed Sensing Matrices
Weizhi Lu, Kidiyo Kpalma, Joseph Ronsin
This paper proposes an efficient method to construct the bipartite graph with as many edges as possible while without introducing the shortest cycles of length equal to 4. The bina…
Sparse Matrix-based Random Projection for Classification
Weizhi Lu, Weiyu Li, Kidiyo Kpalma +1
As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional da…
Compressed Sensing Matrices: Binary vs. Ternary
Weizhi Lu, Kidiyo Kpalma, Joseph Ronsin
Binary matrix and ternary matrix are two types of popular sensing matrices in compressed sensing for their competitive performance and low computation. However, to the best of our…
Near-optimal Binary Compressed Sensing Matrix
Weizhi Lu, Weiyu Li, Kidiyo Kpalma +1
Compressed sensing is a promising technique that attempts to faithfully recover sparse signal with as few linear and nonadaptive measurements as possible. Its performance is largel…