761 research outputs found
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
Living Between Dialectics: A Bakhtinian and Lacanian Reading of Jade Snow Wong\u27s Fifth Chinese Daughter and Maxine Hong Kingston\u27s The Woman Warrior
This dissertation creates a dialogic web encompassing the sociocultural and psychological aspects of Jade Snow Wong\u27s and Maxine Hong Kingston\u27s autobiographies Fifth Chinese Daughter and The Woman Warrior. The American mainstream society and Chinese patriarchal community have conceived insurmountable ethnic and gender differences that are inherent in Wong\u27s and Kingston\u27s growing-up environment. The dissertation argues that how the two authors perceive the way of how these differences have been conceived is central to our understanding of their representations of ethnic female consciousness when they are writing as both subjects and writers. The dissertation notes, to be more specific, Wong\u27s and Kingston\u27s texts are like a mirror reflecting how they are informed by the principal social ideologies such as Orientalism, the American dream, and the patriarchal definition of womanhood.
The dissertation goes on to argue that the literary strategies Wong and Kingston have
adopted in their texts function to represent the difference of the protagonist\u27s world longtime suppressed by dominant social values, and the mixture of different cultural and literary elements in the text metaphorically manifest the dialogic nature of American ethnic literature and culture. The dissertation contends that all these strategies have political implications because they are used to challenge the conventional unitary literary language
Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition
As a fundamental aspect of human life, two-person interactions contain
meaningful information about people's activities, relationships, and social
settings. Human action recognition serves as the foundation for many smart
applications, with a strong focus on personal privacy. However, recognizing
two-person interactions poses more challenges due to increased body occlusion
and overlap compared to single-person actions. In this paper, we propose a
point cloud-based network named Two-stream Multi-level Dynamic Point
Transformer for two-person interaction recognition. Our model addresses the
challenge of recognizing two-person interactions by incorporating local-region
spatial information, appearance information, and motion information. To achieve
this, we introduce a designed frame selection method named Interval Frame
Sampling (IFS), which efficiently samples frames from videos, capturing more
discriminative information in a relatively short processing time. Subsequently,
a frame features learning module and a two-stream multi-level feature
aggregation module extract global and partial features from the sampled frames,
effectively representing the local-region spatial information, appearance
information, and motion information related to the interactions. Finally, we
apply a transformer to perform self-attention on the learned features for the
final classification. Extensive experiments are conducted on two large-scale
datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The
results show that our network outperforms state-of-the-art approaches across
all standard evaluation settings
Unconditionally secure quantum bit commitment: Revised
Bit commitment is a primitive task of many cryptographic tasks. It has been proved that the unconditionally secure quantum bit commitment is impossible from Mayers-Lo-Chau No-go theorem. A variant of quantum bit commitment requires cheat sensible for both parties. Another results shows that these no-go theorem can be evaded using the non-relativistic transmission or Minkowski causality. Our goal in this paper is to revise unconditionally secure quantum bit commitment. We firstly propose new quantum bit commitments using distributed settings and quantum entanglement which is used to overcome Mayers-Lo-Chau No-go Theorems. Both protocols are perfectly concealing, perfectly binding, and cheating sensible in asymptotic model against entanglement-based attack and splitting attack from quantum networks. These schemes are then extended to commit secret bits against eavesdroppers. We further propose two new applications. One is to commit qubit states. The other is to commit unitary circuits. These new schemes are useful for committing several primitives including sampling model, randomness, and Boolean functions in cryptographic protocols
Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method
Feature alignment is the primary means of fusing multimodal data. We propose
a feature alignment method that fully fuses multimodal information, which
alternately shifts and expands feature information from different modalities to
have a consistent representation in a feature space. The proposed method can
robustly capture high-level interactions between features of different
modalities, thus significantly improving the performance of multimodal
learning. We also show that the proposed method outperforms other popular
multimodal schemes on multiple tasks. Experimental evaluation of ETT and
MIT-BIH-Arrhythmia, datasets shows that the proposed method achieves state of
the art performance.Comment: 8 pages,7 figure
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