7 citations · 20 across the 11 of their papers we have counts for
11 papers
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
Peng Jin, Hao Li, Zesen Cheng +5
Text-video retrieval is a challenging cross-modal task, which aims to align visual entities with natural language descriptions. Current methods either fail to leverage the local de…
TG-VQA: Ternary Game of Video Question Answering
Hao Li, Peng Jin, Zesen Cheng +5
Video question answering aims at answering a question about the video content by reasoning the alignment semantics within them. However, since relying heavily on human instructions…
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Kehan Li, Yian Zhao, Zhennan Wang +6
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and med…
Position Embedding Needs an Independent Layer Normalization
Runyi Yu, Zhennan Wang, Yinhuai Wang +5
The Position Embedding (PE) is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operation. By analyzing the input and output of each encod…
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation
Pengchong Qiao, Zhidan Wei, Yu Wang +6
Semi-supervised learning (SSL) essentially pursues class boundary exploration with less dependence on human annotations. Although typical attempts focus on ameliorating the inevita…
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Kehan Li, Zhennan Wang, Zesen Cheng +6
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the developmen…
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Kehan Li, Runyi Yu, Zhennan Wang +3
While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the…
BN: Enhancing Batch Normalization by Equalizing the Norms of Features
Zhennan Wang, Kehan Li, Runyi Yu +7
In this paper, we analyze batch normalization from the perspective of discriminability and find the disadvantages ignored by previous studies: the difference in norms of samp…
DPR-CAE: Capsule Autoencoder with Dynamic Part Representation for Image Parsing
Canqun Xiang, Zhennan Wang, Wenbin Zou +1
Parsing an image into a hierarchy of objects, parts, and relations is important and also challenging in many computer vision tasks. This paper proposes a simple and effective capsu…
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles
Zhennan Wang, Canqun Xiang, Wenbin Zou +1
The strong correlation between neurons or filters can significantly weaken the generalization ability of neural networks. Inspired by the well-known Tammes problem, we propose a no…
PR Product: A Substitute for Inner Product in Neural Networks
Zhennan Wang, Wenbin Zou, Chen Xu
In this paper, we analyze the inner product of weight vector w and data vector x in neural networks from the perspective of vector orthogonal decomposition and prove that the direc…