Object-Level Context Modeling For Scene Classification with Context-CNN
This addresses scene understanding in computer vision, but it is incremental as it builds on existing methods with modest improvements.
The paper tackled scene classification by integrating object-level context modeling into a CNN architecture, achieving results comparable to state-of-the-art on the LSUN dataset.
Convolutional Neural Networks (CNNs) have been used extensively for computer vision tasks and produce rich feature representation for objects or parts of an image. But reasoning about scenes requires integration between the low-level feature representations and the high-level semantic information. We propose a deep network architecture which models the semantic context of scenes by capturing object-level information. We use Long Short Term Memory(LSTM) units in conjunction with object proposals to incorporate object-object relationship and object-scene relationship in an end-to-end trainable manner. We evaluate our model on the LSUN dataset and achieve results comparable to the state-of-art. We further show visualization of the learned features and analyze the model with experiments to verify our model's ability to model context.