# Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method

arXiv: https://arxiv.org/abs/2306.16950 · HTML: https://mlsanity.com/paper/2306.16950/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [Unifying Vision-and-Language Tasks via Text Generation](https://mlsanity.com/paper/2102.02779/) (2021)
- [SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://mlsanity.com/paper/2106.09305/) (2021)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting](https://mlsanity.com/paper/2205.08897/) (2022)
- [CAT: Cross Attention in Vision Transformer](https://mlsanity.com/paper/2106.05786/) (2021)
- [Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval](https://mlsanity.com/paper/2204.09868/) (2022)
- [Deep Multimodal Learning for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1501.05396/) (2015)
- [Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement](https://mlsanity.com/paper/2210.03501/) (2022)
- [Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network](https://mlsanity.com/paper/1708.04776/) (2017)
- [A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance](https://mlsanity.com/paper/2207.07089/) (2022)

## Built on by (most cited first, 6 of 6)

- [Multimodal Alignment and Fusion: A Survey](https://mlsanity.com/paper/2411.17040/) (2024)
- [Learning Domain-Invariant Representations for Cross-Domain Image Registration via Scene-Appearance Disentanglement](https://mlsanity.com/paper/2601.08875/) (2026)
- [Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models](https://mlsanity.com/paper/2409.11263/) (2024)
- [SAS-Net: Cross-Domain Image Registration as Inverse Rendering via Structure-Appearance Factorization](https://mlsanity.com/paper/2602.09050/) (2026)
- [PCReg-Net: Progressive Contrast-Guided Registration for Cross-Domain Image Alignment](https://mlsanity.com/paper/2602.13304/) (2026)
- [Deformation-Free Cross-Domain Image Registration via Position-Encoded Temporal Attention](https://mlsanity.com/paper/2602.15959/) (2026)

## Abstract (arXiv)

In the realm of multimodal data integration, feature alignment plays a pivotal role. This paper introduces an innovative approach to feature alignment that revolutionizes the fusion of multimodal information. Our method employs a novel iterative process of telescopic displacement and expansion of feature representations across different modalities, culminating in a coherent unified representation within a shared feature space. This sophisticated technique demonstrates a remarkable ability to capture and leverage complex crossmodal interactions at the highest levels of abstraction. As a result, we observe significant enhancements in the performance of multimodal learning tasks. Through rigorous comparative analysis, we establish the superiority of our approach over existing multimodal fusion paradigms across a diverse array of applications. Comprehensive empirical evaluations conducted on multifaceted datasets encompassing temporal sequences, visual data, and textual information provide compelling evidence that our method achieves unprecedented benchmarks in the field. This work not only advances the state of the art in multimodal learning but also opens new avenues for exploring the synergies between disparate data modalities in complex analytical scenarios.