2,890 research outputs found
Graph Distillation for Action Detection with Privileged Modalities
We propose a technique that tackles action detection in multimodal videos
under a realistic and challenging condition in which only limited training data
and partially observed modalities are available. Common methods in transfer
learning do not take advantage of the extra modalities potentially available in
the source domain. On the other hand, previous work on multimodal learning only
focuses on a single domain or task and does not handle the modality discrepancy
between training and testing. In this work, we propose a method termed graph
distillation that incorporates rich privileged information from a large-scale
multimodal dataset in the source domain, and improves the learning in the
target domain where training data and modalities are scarce. We evaluate our
approach on action classification and detection tasks in multimodal videos, and
show that our model outperforms the state-of-the-art by a large margin on the
NTU RGB+D and PKU-MMD benchmarks. The code is released at
http://alan.vision/eccv18_graph/.Comment: ECCV 201
Multimodal Sentiment Analysis: A Survey
Multimodal sentiment analysis has become an important research area in the
field of artificial intelligence. With the latest advances in deep learning,
this technology has reached new heights. It has great potential for both
application and research, making it a popular research topic. This review
provides an overview of the definition, background, and development of
multimodal sentiment analysis. It also covers recent datasets and advanced
models, emphasizing the challenges and future prospects of this technology.
Finally, it looks ahead to future research directions. It should be noted that
this review provides constructive suggestions for promising research directions
and building better performing multimodal sentiment analysis models, which can
help researchers in this field.Comment: It needs to be returned for major modification
- …