Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction

• URL: http://arxiv.org/abs/2411.10513v2
• Date: Mon, 25 Nov 2024 17:04:20 GMT
• Title: Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction
• Authors: Po-han Li, Yunhao Yang, Mohammad Omama, Sandeep Chinchali, Ufuk Topcu,
• Abstract summary: We propose Any2Any, a novel retrieval framework that addresses scenarios where both query and reference instances have incomplete modalities.<n>It calculates pairwise similarities with cross-modal encoders and employs a two-stage calibration process with conformal prediction to align the similarities.<n>It achieves a Recall@5 of 35% on the KITTI dataset, which is on par with baseline models with complete modalities.
• Score: 17.607392214470295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous agents perceive and interpret their surroundings by integrating multimodal inputs, such as vision, audio, and LiDAR. These perceptual modalities support retrieval tasks, such as place recognition in robotics. However, current multimodal retrieval systems encounter difficulties when parts of the data are missing due to sensor failures or inaccessibility, such as silent videos or LiDAR scans lacking RGB information. We propose Any2Any-a novel retrieval framework that addresses scenarios where both query and reference instances have incomplete modalities. Unlike previous methods limited to the imputation of two modalities, Any2Any handles any number of modalities without training generative models. It calculates pairwise similarities with cross-modal encoders and employs a two-stage calibration process with conformal prediction to align the similarities. Any2Any enables effective retrieval across multimodal datasets, e.g., text-LiDAR and text-time series. It achieves a Recall@5 of 35% on the KITTI dataset, which is on par with baseline models with complete modalities.

Related papers

• Composed Multi-modal Retrieval: A Survey of Approaches and Applications [81.54640206021757]
  Composed Multi-modal Retrieval (CMR) emerges as a pivotal next-generation technology.<n>CMR enables users to query images or videos by integrating a reference visual input with textual modifications.<n>This paper provides a comprehensive survey of CMR, covering its fundamental challenges, technical advancements, and applications.
  arXiv  Detail & Related papers  (2025-03-03T09:18:43Z)
• MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching [54.740256498985026]
  Keypoint detection and description methods often struggle with multimodal data. We propose a modality-invariant feature learning network (MIFNet) to compute modality-invariant features for keypoint descriptions in multimodal image matching.
  arXiv  Detail & Related papers  (2025-01-20T06:56:30Z)
• Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning [27.867369806400834]
  We propose RAGPT, a novel Retrieval-AuGmented dynamic Prompt Tuning framework. RAGPT comprises three modules: (I) the multi-channel retriever, (II) the missing modality generator, and (III) the context-aware prompter. Experiments conducted on three real-world datasets show that RAGPT consistently outperforms all competitive baselines in handling incomplete modality problems.
  arXiv  Detail & Related papers  (2025-01-02T07:39:48Z)
• Analyzing Multimodal Integration in the Variational Autoencoder from an Information-Theoretic Perspective [0.0]
  We analyze how important the integration of the different modalities are for the reconstruction of the input data. We train networks with four different schedules and analyze them with respect to their capabilities for multimodal integration.
  arXiv  Detail & Related papers  (2024-11-01T11:43:17Z)
• All in One Framework for Multimodal Re-identification in the Wild [58.380708329455466]
  multimodal learning paradigm for ReID introduced, referred to as All-in-One (AIO) AIO harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal retrieval without additional fine-tuning. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts.
  arXiv  Detail & Related papers  (2024-05-08T01:04:36Z)
• Bi-directional Adapter for Multi-modal Tracking [67.01179868400229]
  We propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another. Our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods.
  arXiv  Detail & Related papers  (2023-12-17T05:27:31Z)
• Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios [23.43319138048058]
  Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data. Traditional methods have often involved discarding data or substituting data segments with zero vectors to approximate these incompletenesses. We introduce a novel noise-robust MER model that effectively learns robust multimodal joint representations from noisy data.
  arXiv  Detail & Related papers  (2023-09-21T10:49:02Z)
• Read, Look or Listen? What's Needed for Solving a Multimodal Dataset [7.0430001782867]
  We propose a two-step method to analyze multimodal datasets, which leverages a small seed of human annotation to map each multimodal instance to the modalities required to process it. We apply our approach to TVQA, a video question-answering dataset, and discover that most questions can be answered using a single modality, without a substantial bias towards any specific modality. We analyze the MERLOT Reserve, finding that it struggles with image-based questions compared to text and audio, but also with auditory speaker identification.
  arXiv  Detail & Related papers  (2023-07-06T08:02:45Z)
• Align and Attend: Multimodal Summarization with Dual Contrastive Losses [57.83012574678091]
  The goal of multimodal summarization is to extract the most important information from different modalities to form output summaries. Existing methods fail to leverage the temporal correspondence between different modalities and ignore the intrinsic correlation between different samples. We introduce Align and Attend Multimodal Summarization (A2Summ), a unified multimodal transformer-based model which can effectively align and attend the multimodal input.
  arXiv  Detail & Related papers  (2023-03-13T17:01:42Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text [58.655375327681774]
  We propose the first Multimodal Retrieval-Augmented Transformer (MuRAG) MuRAG accesses an external non-parametric multimodal memory to augment language generation. Our results show that MuRAG achieves state-of-the-art accuracy, outperforming existing models by 10-20% absolute on both datasets.
  arXiv  Detail & Related papers  (2022-10-06T13:58:03Z)
• Unsupervised Multimodal Language Representations using Convolutional Autoencoders [5.464072883537924]
  We propose extracting unsupervised Multimodal Language representations that are universal and can be applied to different tasks. We map the word-level aligned multimodal sequences to 2-D matrices and then use Convolutional Autoencoders to learn embeddings by combining multiple datasets. It is also shown that our method is extremely lightweight and can be easily generalized to other tasks and unseen data with small performance drop and almost the same number of parameters.
  arXiv  Detail & Related papers  (2021-10-06T18:28:07Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.