Source-linked AI summary
Cross-Modal Scene Networks
Yusuf Aytar, Lluis Castrejon, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
TL;DR
The paper addresses the lack of alignment between visual representations across modalities, which makes cross-modal transfer difficult. It introduces a scene dataset and regularizes convolutional networks to learn shared representations; experiments suggest improved cross-modal retrieval and visualizations show units responding to concepts across modalities.
Problem
Standard visual approaches learn separate, unaligned representations for each modality, making cross-modal transfer difficult.
Source: §1 · p. 1Method
The paper introduces a scene dataset and regularizes cross-modal convolutional networks to align intermediate representations using weak scene-category alignment.
Source: §1 · p. 1Results
The method enables better cross-modal retrieval representations than fine-tuning, while visualizations show modality-agnostic units and the learned representation enables image reconstruction across modalities.
Source: §1 · pp. 1–2Takeaways & Limitations
The experiments suggest that alignment can emerge automatically across modalities even when training data is unaligned.
Source: §6 · p. 10Most modalities lack training data for some categories, the setting motivating the paper’s zero-shot recognition and retrieval experiments.
Source: §1 · p. 1, §5.2 · p. 7
Abstract
from arXiv · showhide
People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for cross-modal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.
1 INTRODUCTION
People can recognize concepts across modalities, but standard visual representations are not aligned across them, making cross-modal transfer difficult. The paper introduces CMPlaces and methods for learning aligned scene representations from weakly aligned data, then reports retrieval, zero-shot, visualization, and reconstruction results.
1 INTRODUCTION
- The goal is strong object-level alignment across modalities without paired data or explicitly annotated correspondences.The paper builds on domain adaptation and multimodal learning while targeting substantially different modalities with minimal supervision.Source: §1 · p. 1
- CMPlaces covers hundreds of natural scene types in five modalities, and the proposed representation uses scene-category annotations rather than paired examples.The dataset is introduced to study cross-modal scene representations.Source: §1 · p. 1
- Two regularization approaches align intermediate representations across modalities even when training provides only weak scene-category alignment.Visualizations show hidden units activating on the same object across modalities.Source: §1 · p. 1
- Cross-modal representations may support retrieval from easier-to-produce queries and model training when data in one modality is difficult to acquire.The paper names privacy, legal, and logistical barriers as examples.Source: §1 · p. 1
- Our method enables better representations for cross-modal retrieval than a fine-tuning approach.Experiments also suggest good zero-shot recognition and retrieval when some categories lack training data in some modalities.Source: §1 · p. 1
- Shared-representation units emerge that activate on high-level concepts independently of modality, and the learned representation enables image reconstruction across modalities.The network is trained without paired data.Source: §1 · pp. 1–2
2 RELATED WORK
The paper situates its approach among domain adaptation, zero-shot learning, cross-modal retrieval, and visual abstraction, emphasizing alignment across highly different modalities without explicit correspondences.
2 RELATED WORK
- Unlike domain adaptation work focused on close distribution shifts, this method seeks cross-modal representations between highly different modalities.The cited work includes metric-learning and CNN approaches to domain adaptation.Source: §2 · p. 2
- Zero-shot learning addresses new classifiers without domain-specific training examples, while this paper learns aligned representations that could be used for zero-shot learning.The passage lists textual descriptions and attributes as additional knowledge used in prior approaches.Source: §2 · p. 2
- Earlier cross-modal retrieval methods use sketches, text, CCA, or joint image-text embeddings; this method targets many modalities without explicit one-to-one correspondences.The cited examples include sketch-to-image and sketch-to-3D retrieval, as well as image-text embeddings.Source: §2 · p. 2
- Visual-abstraction work learns concepts in abstract imagery and transfers them to natural images; modality-invariant representations could complement that effort.The passage describes clip art as one such abstract visual domain.Source: §2 · p. 2
- The shared high-level representation is visualized separately from modality-specific low-level representations, with units tending to respond to the same objects across modalities.The figure shows masks of inputs that most activate selected units.Source: Figure 2 · p. 3
3 CROSS-MODAL PLACES DATASET
CMPlaces provides scene-labeled, unpaired examples across five modalities and 205 categories. Its modalities and collection procedures support experiments on transfer between substantially different forms of scene data.
3 CROSS-MODAL PLACES DATASET
- CMPlaces contains natural images, line drawings, cartoons, descriptions, and spatial text images across 205 scene categories, with scene labels but no cross-modal pairing.The unpaired design supports learning alignments from weakly aligned data and eased data collection.Source: §3 · p. 3
- For four non-natural modalities, validation uses 10 random examples per category; natural images use the Places splits, containing 2.5 million images.The modalities were selected for differing statistics from natural images and relative ease of generation.Source: §3 · p. 3
- Line drawings were collected through Mechanical Turk, yielding 14,830 training and 2,050 validation examples; scene descriptions were also collected through Mechanical Turk.The descriptions split into 9,752 training and 2,050 validation examples and were designed to avoid trivial category-revealing words.Source: §3 · p. 3
- Clip art was gathered through scene-category searches and manual filtering, producing 11,372 training and 1,954 validation images.Some categories had fewer than 10 examples.Source: §3 · p. 3
- Spatial text combines images with words placed at object locations, with 456,300 training and 2,050 validation images.The modality is intended to support queries specifying object names and spatial layout for natural-image retrieval.Source: §3 · p. 3
4 CROSS-MODAL SCENE REPRESENTATION
The section aims to learn strongly aligned scene representations from weakly aligned data, where only scene labels—not one-to-one correspondences—are available. It presents shared-layer networks and complementary training and activation-regularization methods for that goal.
4.1 Cross-Modal Scene Networks
- The network uses modality-specific early layers and shared higher layers, but joint training alone can produce units specific to one modality rather than concepts shared across modalities.The shared layers are intended to capture higher-level concepts, while the early layers specialize to modality-specific features.Source: §4.1 · p. 4
4.2 Method A: Modality Tuning
- Modality tuning transfers a high-level representation by training modality-specific earlier layers while initially freezing shared later layers, then unfreezing them for joint training.The method starts from Places-CNN and uses this staged training to let later layers accommodate other modalities while avoiding overfitting to modality-specific representations.Source: §4.2 · pp. 4–5
4.3 Method B: Statistical Regularization
- Statistical regularization penalizes hidden activations according to their likelihood under distributions estimated from Places-CNN, encouraging similar intermediate-layer statistics across modalities.The distribution is estimated separately for each regularized layer; the explored choices include a multivariate Gaussian and a Gaussian mixture.Source: §4.3 · p. 5
4.4 Method C: Joint Method
- Method C first fixes shared layers for a set number of iterations, then unfreezes them and applies statistical regularization to encourage cross-modal similarity and avoid modality-specific overfitting.Source: §4.4 · p. 5
4.5 Implementation Details
- Method C performs best among the reported methods, and the proposed methods outperform fine-tuning baselines on average for cross-modal retrieval.Table 1 reports mAP using fc7 features; Table 2 reports PR@10. Models use modality-specific lower layers and shared layers from pool5 onward, with textual descriptions adapted through a two-layer MLP.Source: §4.5 · pp. 5–6, Table 1 · p. 6, Table 2 · p. 6
5 EXPERIMENTAL RESULTS
The experiments evaluate cross-modal retrieval and recognition, then examine whether the learned representation aligns concepts and discards modality information. Reconstructions provide a qualitative check on which scene information is preserved across modalities.
5.1 Cross-Modal Retrieval
- Method C outperforms all reported retrieval methods, matching method A on descriptions while retaining method B’s superior performance on other modalities.For statistical regularization, using a GMM also notably improves performance over a single Gaussian.Source: §5.1 · p. 7
5.2 Zero-Shot Recognition and Retrieval
- For held-out-category classification, statistical regularization helps visual modalities, modality tuning helps descriptions, and the approach tends to beat PlacesCNN on modalities far from natural images.The evaluation holds out 55 categories from clip art, spatial text, line drawings, and descriptions.Source: §5.2 · pp. 7–8
- On held-out categories, combining both methods improves retrieval; classification instead favors one regularizer, as stronger constraints may hurt it.The paper attributes retrieval gains to features becoming more instance-specific under stronger internal-activation regularization.Source: §5.2 · p. 8
5.3 Hidden Unit Visualizations
- The same pool5 units can detect concepts across visual modalities without explicitly aligned training data, suggesting the method learns strong cross-modal alignments using only weak labels from scene categories.Among results retained only when at least 4 images for the real modality were selected, subjects selected at least one image from each other modality 33% of the time with our method, versus 25% for the baseline.Source: §5.3 · pp. 8–9
5.4 Analyzing Modality Invariance
- The t-SNE visualization shows baseline fc7 features separating by modality, while the full approach tends to discard modality information better, though it is not completely invariant.Statistical regularization offers some invariance, except for text.Source: §5.4 · p. 9
5.5 Feature Reconstructions
- Pool5-to-image inversions often preserve scene concepts and output-modality statistics, but are blurry from compression and harder when modality feature statistics differ.Reconstructions from clip art and spatial text are less informative, which the authors suggest may contribute to the difficulty.Source: §5.5 · p. 9
6 CONCLUSION
The paper proposes learning aligned cross-modal representations without paired data. Its conclusion reports that alignment can emerge even when training data are unaligned, with potential relevance to retrieval and recognition.
6 CONCLUSION
- Reconstructions from line drawings add colors while preserving scene composition; text reconstructions roughly preserve the locations, shapes, and colors of scene parts.Source: Figure 8 · p. 10
- The proposed approach learns aligned cross-modal representations without paired data.Source: §6 · p. 10
- Experiments suggest that alignment can emerge automatically across modalities even when training data are unaligned.Source: §6 · p. 10