Self-Supervised Cross-Modal Retrieval

Updated 6 September 2025

Self-supervised cross-modal retrieval is a technique that learns unified representations from unlabelled, co-occurring data across modalities.
It employs architectures like CNNs, transformers, and fusion networks to align diverse feature spaces using contrastive, adversarial, and clustering losses.
The method enables robust retrieval in domains such as multimedia, remote sensing, and medical imaging, effectively addressing challenges like modality gaps and negative sampling.

Self-supervised cross-modal retrieval is a family of representation learning and matching techniques that enable retrieval (search) and alignment between heterogeneous data modalities—such as image, audio, video, text, or point cloud—without explicit manual annotation of correspondences. These methods exploit the natural co-occurrence of multiple modalities within large unlabelled datasets (e.g., video with audio, image with caption, video with transcript) and employ self-supervised objectives that align the learned feature spaces, enabling the retrieval of content in one modality with a query from another. Recent advances have yielded high-performing systems in diverse domains, including multimedia search, video-audio pairing, 3D model retrieval, medical imaging, and remote sensing.

Central to self-supervised cross-modal retrieval is the design of architectures that project heterogeneous features into a shared embedding space. Projects such as "Cross-modal Embeddings for Video and Audio Retrieval" (Surís et al., 2018) employ parallel neural networks (often MLPs, CNNs, or transformers) for each modality; these branch-specific modules process modality-appropriate features and produce embeddings Φᵃ, Φᶦ that are subsequently compared.

Embedding alignment is typically enforced via self-supervised objectives. The common approach uses a contrastive similarity loss; for instance, the cosine loss takes the form: $L_{\text{cos}}((\Phi^a, \Phi^i), y) = \begin{cases} 1 - \text{cos}(\Phi^a, \Phi^i), & \text{if } y = 1\ \max(0, \text{cos}(\Phi^a, \Phi^i) - \alpha), & \text{if } y = -1 \end{cases}$ where y = 1 denotes a positive (co-occurring) pair and y = –1 a negative pair, and

$\text{cos}(x, z) = \frac{\sum_k x_k z_k}{\sqrt{\sum_k x_k^2}\;\sqrt{\sum_k z_k^2}}.$

Other frameworks supplement the similarity loss with additional regularization terms, such as a cross-entropy classification loss to exploit available class or multi-label metadata, or employ triplet/diversity losses to ensure inter-class discrimination while encouraging intra-class alignment (Sayed et al., 2018).

Recent architectures extend beyond paired encoders, introducing fusion networks (e.g., TIRG in (Gu et al., 2021)), hierarchical transformers for structured domain inputs (recipes, (Salvador et al., 2021)), discrete vector quantization for fine-grained semantic clustering (Liu et al., 2021), and hybrid transformer pipelines for multi-product multi-instance scenarios (Zhan et al., 2021).

Self-supervised approaches exploit inherent synchrony or co-occurrence of modalities—such as images with text (Wikipedia articles and captions (Patel et al., 2019)), video and audio tracks (YouTube-8M, (Surís et al., 2018)), or multiple sensor readings (Sentinel-1 SAR and Sentinel-2 MSI in remote sensing (Sumbul et al., 2022)). Features may be precomputed (e.g., Inception or VGGish on video and audio), or end-to-end representation learning may be applied with large transformer/cnn backbones.

Data structuring is often modality aware. For example, in cross-modal recipe retrieval (Salvador et al., 2021), titles, ingredient lists, and instructions are encoded via hierarchical transformers. Temporal modeling is addressed via specialized fusion strategies; for instance, bitemporal remote sensing image pairs employ feature difference, concatenation, or transformer attention-based fusion capturing both global and fine-grained temporal change (Hoxha et al., 31 Jan 2025).

Negative sampling is non-trivial and typically involves selecting random pairs that do not co-occur and ensuring that negatives are not semantically similar (e.g., by label exclusion (Surís et al., 2018)). Several works also introduce clustering or pseudo-labeling to allow “soft” semantic groupings and account for ambiguous negatives (Chen et al., 2021, Kim, 2021).

3. Retrieval Tasks and Evaluation

Self-supervised cross-modal retrieval frameworks are evaluated using diverse query–target scenarios:

Bidirectional retrieval: Given a sample from modality A, retrieve the corresponding paired sample in modality B (e.g., video-to-audio, audio-to-video (Surís et al., 2018)).
Time-series/document retrieval: For multi-temporal RS, text-to-image time series retrieval and vice versa (Hoxha et al., 31 Jan 2025).
Instance-level/semantic-level retrieval: For example, EEG signal directly retrieving the exact visual stimulus (Ye et al., 2022).
Fine-grained or open-set retrieval: Open-set 3D cross-modal retrieval, where previously unseen categories may appear at test time (Xu et al., 22 Jul 2024).

Standard metrics are Recall@K, Mean Average Precision (MAP), median rank (medR), normalized discounted cumulative gain (NDCG), and Percentage of Correct Keypoints (PCK) for dense correspondence tasks (Shrivastava et al., 3 Jun 2025).

Qualitative analysis is also common, particularly where retrieval candidates may have ambiguous semantic relationships—e.g., retrieving "better than ground-truth" soundtracks for silent videos (Surís et al., 2018).

4. Advanced Strategies: Clustering, Adversarial, and Cycle-Consistent Learning

Modern self-supervised cross-modal retrieval systems increasingly incorporate additional mechanisms:

Semantic clustering: Online K-means clustering infuses high-level structure, enabling alignment of semantically similar but non-paired instances (Chen et al., 2021). Swapped assignment of pseudo-labels encourages grouping based on latent semantic similarity, relaxing rigid instance discrimination (Kim, 2021).
Adversarial alignment: Networks such as SSAH deploy adversarial discriminators to penalize modality- or generator-specific differences, learning mode-invariant hash codes (Li et al., 2018).
Cycle-consistency constraints: For spatially dense correspondence, cycle-consistent random walks constrain the embedded representations, enforcing that round-trips across modalities recover the original position (Shrivastava et al., 3 Jun 2025).

Discrete codebook-based systems leverage vector quantization to generate tokenized, interpretable embeddings—enabling cross-modal matching not just at the global (instance) level, but also at local (e.g., pixel/word/frame) semantics (Liu et al., 2021).

5. Domain-Specific Innovations and Applications

Application domains drive specific innovations:

Audio–visual pairing and retrieval: Joint embeddings from video-audio data facilitate, for instance, the sonorization of silent video by retrieving suitable audio tracks (Surís et al., 2018, Sarkar et al., 2021).
Remote sensing: Cross-modal retrieval for SAR/MSI, and advanced time-series retrieval tasks (text-image time series) exploit paired/synchronized satellite imagery (Sumbul et al., 2022, Hoxha et al., 31 Jan 2025).
Product retrieval in e-commerce: Weakly supervised schemes address instance-level retrieval across noisy and inconsistent multi-modal data with fine-grained labels (Zhan et al., 2021).
Medical and brain imaging: Direct retrieval of visual stimulus from EEG or medical scans leverages modality-adaptive encoders and mutual information maximization (Ye et al., 2022).
Open-set and 3D retrieval: Residual-center embedding and hypergraph-based structure learning enable robust retrieval under significant category shift (Xu et al., 22 Jul 2024).

Self-supervised cross-modal retrieval approaches are further distinguished by their applicability to low-resource settings, with resilience to limited annotated data, and strong open-set/zero-shot capabilities, as supported by empirical studies across multiple works.

6. Future Prospects, Challenges, and Open Questions

Emergent research challenges and directions include:

Temporal/modeling enhancements: Incorporating temporal dependencies with RNNs or transformer-based models, particularly for video, time-series, and change detection tasks (Surís et al., 2018, Hoxha et al., 31 Jan 2025).
Mitigating modality gaps: Information-theoretic objectives (Deep InfoMax (Gu et al., 2021)), model regularization (II loss (Chen et al., 28 Jul 2024)), and adversarial strategies address distribution mismatch between modalities.
Handling false negatives and label noise: The II loss reduces overfitting to false negatives by enforcing intra-modal feature consistency (Chen et al., 28 Jul 2024).
Scalability and efficiency: Large-scale deployment and transfer to previously unseen classes or domains are key goals, as are model simplification and reduction of hyperparameter sensitivity (Chen et al., 2021, Salvador et al., 2021).
Explainability and semantic localization: Cross-modal code matching and discretized token spaces provide interpretable retrieval and facilitate cross-modal localization and clustering (Liu et al., 2021).
Extension to new modalities and domains: The frameworks are beginning to generalize to more complex structures, such as graph-based representations for 3D retrieval or spatio-temporal video-text retrieval (Wei et al., 11 Aug 2024, Xu et al., 22 Jul 2024).

Potential limitations include optimization instability for adversarial components (Li et al., 2018), challenges in perfect modality distribution alignment without labels, and the dependency of some frameworks on the quality of negative sampling or clustering.

7. Summary and Impact

Self-supervised cross-modal retrieval constitutes an active research frontier, uniting advances in deep representation learning, contrastive and clustering losses, adversarial alignment, and data-driven fusion strategies. These methods consistently demonstrate that, given only naturally occurring, weakly aligned, or co-occurring data, it is possible to learn semantically meaningful, transferable, and robust feature spaces that enable cross-modal search, retrieval, and alignment at scale. The approach generalizes to numerous domains—audio-visual, text-image, image-3D, remote sensing, and beyond—and continues to expand in both methodological sophistication and practical applicability.