Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Xie, Zhongwei"'
Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent yea
Externí odkaz:
http://arxiv.org/abs/2212.10006
This paper introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding
Externí odkaz:
http://arxiv.org/abs/2110.11592
This paper presents a three-tier modality alignment approach to learning text-image joint embedding, coined as JEMA, for cross-modal retrieval of cooking recipes and food images. The first tier improves recipe text embedding by optimizing the LSTM ne
Externí odkaz:
http://arxiv.org/abs/2108.03788
It is widely acknowledged that learning joint embeddings of recipes with images is challenging due to the diverse composition and deformation of ingredients in cooking procedures. We present a Multi-modal Semantics enhanced Joint Embedding approach (
Externí odkaz:
http://arxiv.org/abs/2108.00724
This paper introduces a two-phase deep feature calibration framework for efficient learning of semantics enhanced text-image cross-modal joint embedding, which clearly separates the deep feature calibration in data preprocessing from training the joi
Externí odkaz:
http://arxiv.org/abs/2108.00705
Publikováno v:
In Computer Vision and Image Understanding April 2024 241
Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accuracy ensembles. The design of EnsembleBench offers three novel feature
Externí odkaz:
http://arxiv.org/abs/2010.10623
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Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenar
Externí odkaz:
http://arxiv.org/abs/1810.03989
Akademický článek
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