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A review on methods and applications in multimodal deep learning
S Jabeen, X Li, MS Amin, O Bourahla, S Li… - ACM Transactions on …, 2023 - dl.acm.org
Deep Learning has implemented a wide range of applications and has become increasingly
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …
popular in recent years. The goal of multimodal deep learning (MMDL) is to create models …
Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …
deep learning (DL). However, the latter faces various issues, including the lack of data or …
Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining
Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …
modeling pretext tasks, where great improvements in performance on various downstream …
Point-bind & point-llm: Aligning point cloud with multi-modality for 3d understanding, generation, and instruction following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image,
language, audio, and video. Guided by ImageBind, we construct a joint embedding space …
language, audio, and video. Guided by ImageBind, we construct a joint embedding space …
Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning?
The success of deep learning heavily relies on large-scale data with comprehensive labels,
which is more expensive and time-consuming to fetch in 3D compared to 2D images or …
which is more expensive and time-consuming to fetch in 3D compared to 2D images or …
Cross-modal retrieval: a systematic review of methods and future directions
With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …
methods struggle to meet the needs of users seeking access to data across various …
RONO: robust discriminative learning with noisy labels for 2D-3D cross-modal retrieval
Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval
becomes popular with a burst of 2D and 3D data. However, this problem is challenging …
becomes popular with a burst of 2D and 3D data. However, this problem is challenging …
Hypergraph-based multi-modal representation for open-set 3D object retrieval
The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes
the categories of objects in the retrieval stage are all seen in the training stage. Existing …
the categories of objects in the retrieval stage are all seen in the training stage. Existing …
[HTML][HTML] Deep vision multimodal learning: Methodology, benchmark, and trend
Deep vision multimodal learning aims at combining deep visual representation learning with
other modalities, such as text, sound, and data collected from other sensors. With the fast …
other modalities, such as text, sound, and data collected from other sensors. With the fast …
Gssf: Generalized structural sparse function for deep cross-modal metric learning
Cross-modal metric learning is a prominent research topic that bridges the semantic
heterogeneity between vision and language. Existing methods frequently utilize simple …
heterogeneity between vision and language. Existing methods frequently utilize simple …