Source-linked AI summary
Recurrent Convolutional Neural Networks for Scene Parsing
Pedro H. O. Pinheiro, Ronan Collobert
TL;DR
Scene parsing requires long-range context for coherent pixel labels, but large contexts and graphical models can increase model capacity or test-time cost. This paper uses a recurrent convolutional neural network that reuses parameters and refines predictions from raw pixels without segmentation or task-specific features, achieving state-of-the-art results on both Stanford Background and SIFT Flow while keeping inference fast.
Problem
Scene parsing needs long-range image dependencies for coherent pixel labeling, while graphical-model approaches have high test-time computational costs.
Method
A recurrent convolutional neural network reuses parameters across instances, taking previous classification attempts as input to refine labels from raw pixels without segmentation or task-specific features.
Results
The system achieved state-of-the-art accuracy on both the Stanford Background and SIFT Flow datasets while remaining fast at test time.
Takeaways & Limitations
The recurrent feed-forward design captures long-range dependencies while controlling model capacity and keeping computational cost low at test time.
Abstract
from arXiv · showhide
Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accuracy, it is essential for a scene parser to capture image long range dependencies. In a feed-forward architecture, this can be simply achieved by considering a sufficiently large input context patch, around each pixel to be labeled. We propose an approach consisting of a recurrent convolutional neural network which allows us to consider a large input context, while limiting the capacity of the model. Contrary to most standard approaches, our method does not rely on any segmentation methods, nor any task-specific features. The system is trained in an end-to-end manner over raw pixels, and models complex spatial dependencies with low inference cost. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. Our approach yields state-of-the-art performance on both the Stanford Background Dataset and the SIFT Flow Dataset, while remaining very fast at test time.
JUNE 2013
The paper lists its authors, institutional affiliation, and research keywords.
- Pedro H. O. Pinheiro and Ronan Collobert are the paper’s authors.
- The authors are affiliated with the Idiap Research Institute in Martigny, Switzerland.
- The listed topics include scene parsing, convolutional networks, deep learning, image classification, and image segmentation.
1 Introduction
Scene parsing requires pixel-level labeling while maintaining global visual coherence, but graphical-model approaches can be computationally expensive. The paper proposes a feed-forward recurrent CNN that captures long-range dependencies from raw pixels without segmentation or engineered features.
- Scene parsing labels every image pixel by the object class it belongs to, combining detection, segmentation, and multi-label recognition.
- Graphical-model scene-parsing methods impose global decisions over local classifiers, but their test-time cost limits contextual features.
- The proposed recurrent CNN uses shared-parameter network instances that receive the previous classification attempt, progressively smoothing predictions as context grows.
- The system captures long-range dependencies while controlling model capacity, achieving state-of-the-art accuracy with low test-time computational cost.
- Unlike graphical-model approaches, the method trains end-to-end without engineered features or label-space searching during prediction.
2 Related Work
Prior scene-parsing systems use deep networks, segmentation, multiscale representations, or graphical models to improve contextual consistency. The proposed method emphasizes simpler feed-forward inference from raw pixels without task-specific features or image segmentation.
- Earlier deep convolutional approaches parsed scenes from raw pixels and captured texture, shape, and contextual information without graphical models.
- Another recurrent approach merged features from pre-segmented regions, whereas this paper’s recurrence smooths scene class annotations directly.
- Multiscale convolutional methods obtained satisfactory pixel categorization but poor visual coherence, motivating additional over-segmentation strategies.
- Table 1 compares scene-parsing methods, highlighting the proposed method’s simple inference without task-specific feature extraction or segmentation.
- The proposed approach is completely feed-forward and avoids image segmentation and multiscale input pyramids.
- Its stated advantage over existing approaches is extracting contextual information from raw pixels without task-specific features.
3 Systems Description
The system uses convolutional networks to classify pixels from image context, then extends context through recurrence while controlling model capacity with shared parameters. It also restores full-resolution labels efficiently from pooled outputs.
- 3.1 Convolutional Neural Networks for Scene Parsing: Convolutional scene parsing feeds each pixel's surrounding context patch through convolutional and pooling stages whose 1 × 1 outputs become class scores.Training selects the input patch size so the final output planes are 1 × 1, while pooling reduces output resolution.
- 3.2 Long Range Dependencies with Convolutional Networks: Large input patches can capture long-range dependencies but may cause generalization problems because they often require larger-capacity models.The paper contrasts this trade-off with recurrence, which enlarges context without multiplying the parameter set.
- 3.2 Long Range Dependencies with Convolutional Networks: Recurrent networks expand the context needed to label a pixel while keeping capacity constant by sharing parameters across instances.The recurrent architecture feeds one network's output into another instance of the same network; the required context grows with the number of instances.
- 3.3 Recurrent Network Approach: Each recurrent instance receives the scaled RGB patch together with the previous instance's label planes, while the first instance uses empty feature maps.All instances share the same trainable parameters, preserving end-to-end training and fast test-time inference.
- 3.3 Recurrent Network Approach: Full-resolution label planes are obtained by running shifted versions of the input through the network and merging the resulting downscaled predictions.This remains faster than forwarding separate patches for every image location, even when multiple pooling layers require more shifted inputs.
4 Experiments
Experiments evaluate plain and recurrent convolutional architectures on Stanford Background and SIFT Flow, including accuracy measures and compute-resolution tradeoffs. Recurrence is trained to correct earlier predictions while expanding context, and quarter-resolution labeling retains near-state-of-the-art performance with fast inference.
- Evaluation: The experiments compare pixel accuracy with averaged per-class accuracy, which weights all classes equally.Pixel accuracy gives more influence to frequent classes, whereas averaged per-class accuracy does not.
- State-of-the-art accuracy was achieved on both the Stanford Background and SIFT Flow datasets.
- Recurrent architectures: rCNN1 and rCNN2 use shared-parameter recurrent instances trained to correct mistakes from earlier predictions.rCNN1 uses two instances, while rCNN2 uses three; the latter expands its input patch from 67 to 155.
- Compute time and scene inference: Labeling about one-quarter of the pixels produced near-state-of-the-art performance while maintaining very fast inference.The comparison varies rCNN2 output resolution and measures the resulting compute-time and accuracy tradeoff.
- Recurrent architectures: The recurrent inference process feeds predictions back with the original image so the network can correct most mistakes.
5 Conclusion
The conclusion presents supervised feed-forward deep learning as a way to model non-local scene dependencies directly from RGB pixels without graphical models or segmentation trees. The system achieves state-of-the-art accuracy on both evaluated datasets while supporting low-cost inference.
- The approach models non-local class dependencies from raw pixels using supervised feed-forward deep learning.
- Scene labeling is inferred by forward evaluation of a function applied directly to an RGB image, without segmentation trees.
- The system achieved state-of-the-art accuracy on both the Stanford Background and SIFT Flow datasets.
- The conclusion identifies unsupervised or semi-supervised pre-training and larger datasets as future work.