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Areas of Attention for Image Captioning
Marco Pedersoli, Thomas Lucas, Cordelia Schmid, Jakob Verbeek
TL;DR
Image captioning needs detailed, spatially localized visual analysis beyond static CNN representations. Areas of Attention jointly models words, image regions, and RNN state, and combines this attention mechanism with trainable spatial-transformer regions to achieve state-of-the-art MSCOCO performance.
Problem
Existing encoder-decoder captioning models use static, non-spatially localized image analysis, limiting detailed visual description.
Method
The model uses three pairwise interactions among RNN state, image regions, and caption words, with jointly trained image-specific attention areas from a convolutional spatial transformer.
Results
State-of-the-art MSCOCO performance is obtained by combining the proposed attention mechanism with spatial-transformer attention areas.
Takeaways & Limitations
Image-specific attention areas are important for improved sentence generation, and spatial transformers outperform other tested region types while using fewer regions and no external proposal mechanism.
Abstract
from arXiv · showhide
We propose "Areas of Attention", a novel attention-based model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attention-based approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic structure in the generated captions.
1. Introduction
Image captioning requires detailed, temporally adaptive visual analysis because captions describe more than object identities. Areas of Attention addresses this by jointly modeling words, image regions, and RNN state while learning image-specific attention areas.
- Image captioning supports visually impaired users and natural-language image search, but its structured sentence output demands detailed scene analysis.Captions can refer to objects, scene type or location, object properties, and interactions.
- Standard CNN-RNN encoder-decoder systems encode an image into a compact representation and decode it word-by-word, but their image analysis is static and not spatially localized.
- Areas of Attention directly models pairwise interactions among the RNN state, image regions, and caption words in one end-to-end trainable system.The model jointly predicts the next caption word and its associated image region, inferring word-region associations from image-level captions.
- The model compares CNN activation grids, object proposals, and a convolutional spatial-transformer network that regresses attention areas from image content.The spatial-transformer approach is end-to-end trainable and produces image-specific attention areas without bounding-box supervision.
- Spatial-transformer attention areas outperform the alternatives while using fewer regions and avoiding an external proposal mechanism, yielding state-of-the-art MSCOCO performance with the proposed attention mechanism.
2. Related work
Prior image-captioning systems use CNN-RNN encoder-decoder models and attention over predefined image regions, while related localization methods infer textual-region correspondences from image-level supervision. Areas of Attention unifies these ideas in a single model with trainable image-adaptive regions and compares region types systematically.
- Encoder-decoder captioning models use a CNN image representation to initialize or condition an RNN that predicts captions sequentially.
- Xu et al. introduced image-captioning attention that selects CNN activation-grid regions from the previous RNN state, using soft combinations or hard single-region selection.The hard variant performed slightly better but was more difficult to train because of non-differentiable sampling.
- Later approaches use attribute detectors or object proposals as attention regions, but these detectors may be learned independently and prior to the language model.
- Visual grounding and weakly supervised localization infer latent correspondences between caption words and image regions from image-level supervision, often using object proposals designed around object boundaries.
- Areas of Attention models region-word, word-state, and region-state interactions in one less-complex interaction layer that supports fixed grids, object proposals, and spatial transformers.The region-state interaction provides dynamic appearance-based salience, while the paper presents a systematic comparison of region types.
3. Attention in encoder-decoder captioning
The model extends CNN–RNN image captioning with joint word–region attention and image-adaptive attention areas. It compares activation grids, object proposals, and spatial transformers within a common attention framework.
- 3.1. Baseline CNN-RNN encoder-decoder model: The baseline CNN encodes an image into a compact vector used to initialize an RNN language model that generates captions word by word.The RNN state is updated recursively from generated words.
- 3.2. Attention for prediction and feedback: The attention model defines a joint distribution over the next word and attended image region conditioned on the current RNN state.Marginalizing regions predicts words, while marginalizing words supplies visual feedback for the state update.
- 3.2. Attention for prediction and feedback: Its score function combines state–word, word–region, and region–state pairwise interactions, with the region–state term acting as dynamic salience.The word–region interaction supports associations between caption words and region appearances.
- 3.2. Attention for prediction and feedback: The pooled visual feedback is a convex combination of region descriptors derived from the marginal region distribution and concatenated with the generated word for state updating.Figure 2 summarizes this feedback loop across time steps.
- 3.3. Areas of attention: The framework supports CNN activation grids, object proposals, and spatial-transformer regions, which differ in whether their receptive fields are fixed, proposal-based, or image-adaptive.Activation grids use fixed-shape regions, whereas object proposals adapt to image content and spatial transformers regress affine transformations over anchor boxes.
- 3.3. Areas of attention: Spatial-transformer attention areas are generated by a convolutional localization network and trained using image captions rather than bounding-box annotations.The network regresses affine transformations at feature-map locations and applies them to anchor boxes for feature resampling.
4. Experimental evaluation
Experiments evaluate model components, attention-region choices, attention correctness, qualitative focus, and comparison with state-of-the-art captioning systems. The results show gains from pairwise attention and visual feedback, advantages for spatial-transformer regions, and state-of-the-art performance.
- Experimental setup: MSCOCO experiments use approximately 80K training images, 40K development images, five captions per image, and BLEU4, METEOR, and CIDEr-D metrics.The setup uses 5K development images for validation and another 5K for measurement.
- Component analysis: Adding word-region interactions raises CIDEr from the 78.9 baseline to 83.6, while region-state interactions raise it further to 85.5.The word-region term supports localized visual input, while the region-state term scores region compatibility with the RNN state.
- Component analysis: Adding visual feedback increases CIDEr-D by another 1.9 points, reaching 87.4 with activation-grid regions.Marginal-distribution feedback is more effective than the word-conditional version because it tends to focus on one semantically meaningful area.
- Attention regions: Performance increases with the number of regions before plateauing; four or fewer regions fall below baseline, while spatial-transformer regions outperform activation grids and object proposals.The comparison varies region counts at test time and uses object proposals ranging from 1 to 2,000 regions.
- Attention regions: Spatial-transformer regions remain appealing because they are trainable end-to-end, avoid an external proposal pipeline, and are more computationally efficient.Their differences from other image-dependent regions are reduced after fine-tuning, while their performance remains comparable to edge-box proposals.
- Qualitative attention: Spatial-transformer attention focuses on relational terms and remains stable across meaningful noun and verb phrases during sequential word generation.Examples include attention to elephant legs for “standing” and the space between an airplane and ground for “low.”
- State-of-the-art comparison: The model achieves state-of-the-art MSCOCO results on par with Wu et al. and Bengio et al.; Bengio et al. report 95.7 CIDEr with standard training.The paper also compares an ensemble trained with 30K additional validation images and random horizontal flips.
5. Conclusion
The paper presents an attention-based image-captioning model, image-specific attention-area proposal network, and evaluation across three region types. Experiments show that the model components and image-adaptive attention regions are important, establishing a first step toward weakly supervised learning of objects and relations from captions.
- The model uses a score function with three pairwise interactions among the RNN state, image regions, and caption words.
- A convolutional spatial-transformer-based region proposal network derives image-specific attention areas without bounding-box supervision.
- The evaluation compares CNN activation grids, object proposals, and regions generated by the proposed region proposal network.
- The experimental results show the importance of all model components and of image-adaptive attention regions.
- The work is a first step toward weakly supervised learning of objects and relations from image captions.