# DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns

arXiv: https://arxiv.org/abs/1608.03217 · HTML: https://mlsanity.com/paper/1608.03217/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion](https://mlsanity.com/paper/2312.10692/) (2023)
- [SequencePAR: Understanding Pedestrian Attributes via A Sequence Generation Paradigm](https://mlsanity.com/paper/2312.01640/) (2023)
- [Pedestrian Attribute Recognition via Hierarchical Cross-Modality HyperGraph Learning](https://mlsanity.com/paper/2509.22331/) (2025)

## Abstract (arXiv)

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 de- signed 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 origi- nal 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 clas- sification on two challenging datasets: PASCAL VOC 2012 Action and Stanford 40 Actions, and for attribute recogni- tion 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.