This paper researches on the problem of object recognition using RGB-D data. Although deep convolutional neural networks have so far made progress in this area, they are still suffering a lot from lack of large-scale manually labeled RGB-D data. Labeling large-scale RGB-D dataset is a time-consuming and boring task. More importantly, such large-scale datasets often exist a long tail, and those hard positive examples of the tail can hardly be recognized. To solve these problems, we propose a multimodal self-augmentation and adversarial network (MSANet) for RGB-D object recognition, which can augment the data effectively at two levels while keeping the annotations. Toward the first level, series of transformations are leveraged to generate class-agnostic examples for each instance, which supports the training of our MSANet. Toward the second level, an adversarial network is proposed to generate class-specific hard positive examples while learning to classify them correctly to further improve the performance of our MSANet. Via the above schemes, the proposed approach wins the best results on several available RGB-D object recognition datasets, e.g., our experimental results indicate a 1.5% accuracy boost on benchmark Washington RGB-D object dataset compared with the current state of the art.
The Visual Computer – Springer Journals
Published: May 29, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera