DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
Citations Over TimeTop 10% of 2016 papers
Abstract
The recognition of human actions and the determination of human attributes are two tasks that call for fine-grained classification. Indeed, often rather small and inconspicuous objects and features have to be detected to tell their classes apart. In order to deal with this challenge, we propose a novel convolutional neural network that mines mid-level image patches that are sufficiently dedicated to resolve the corresponding subtleties. In particular, we train a newly designed CNN (DeepPattern) that learns discriminative patch groups. There are two innovative aspects to this. On the one hand we pay attention to contextual information in an original fashion. On the other hand, we let an iteration of feature learning and patch clustering purify the set of dedicated patches that we use. We validate our method for action classification on two challenging datasets: PASCAL VOC 2012 Action and Stanford 40 Actions, and for attribute recognition we use the Berkeley Attributes of People dataset. Our discriminative mid-level mining CNN obtains state-of-the-art results on these datasets, without a need for annotations about parts and poses.
Related Papers
- Stable Discriminative Dictionary Learning Via Discriminative Deviation(2012)
- → Accurate And Fast Fine-Grained Image Classification via Discriminative Learning(2019)3 cited
- → Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences(2014)2 cited
- → Diffusion-TTA: Test-time Adaptation of Discriminative Models via Generative Feedback(2023)