Search Results for author:
Found 6 papers, 5 papers with code
Generation-Guided Multi-Level Unified Network for Video Grounding
Video grounding aims to locate the timestamps best matching the query description within an untrimmed video.
SimViT: Exploring a Simple Vision Transformer with sliding windows
Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
Ranked #9 on
Video Retrieval
on MSVD
(using extra training data)
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #12 on
Multi-Label Classification
on MS-COCO
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
Odd-even layer-number effect and layer-dependent magnetic phase diagrams in MnBi2Te4
However, the layer-dependent magnetism of MnBi2Te4, which is fundamental and crucial for further exploration of quantum phenomena in this system, remains elusive.
Materials Science
