Cross-Modal Retrieval
169 papers with code • 12 benchmarks • 21 datasets
Cross-Modal Retrieval is used for implementing a retrieval task across different modalities. such as image-text, video-text, and audio-text Cross-Modal Retrieval. The main challenge of Cross-Modal Retrieval is the modality gap and the key solution of Cross-Modal Retrieval is to generate new representations from different modalities in the shared subspace, such that new generated features can be applied in the computation of distance metrics, such as cosine distance and Euclidean distance.
References:
[1] Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
[2] Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-modal Retrieval
Benchmarks
These leaderboards are used to track progress in Cross-Modal Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Cross-Modal Retrieval models and implementationsDatasets
COCO
Flickr30k
PASCAL VOC
NUS-WIDE
PASCAL VOC 2007
CUHK-PEDES
Recipe1M+
RSICD
SemArt
ChEBI-20
Most implemented papers
Stacked Capsule Autoencoders
google-research/google-research
•
•
NeurIPS 2019
In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
fartashf/vsepp
•
•
We present a new technique for learning visual-semantic embeddings for cross-modal retrieval.
Rescaling Egocentric Vision
This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS.
Stacked Cross Attention for Image-Text Matching
Prior work either simply aggregates the similarity of all possible pairs of regions and words without attending differentially to more and less important words or regions, or uses a multi-step attentional process to capture limited number of semantic alignments which is less interpretable.
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
salesforce/lavis
•
•
NeurIPS 2021
Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens.
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
To improve fine-grained video-text retrieval, we propose a Hierarchical Graph Reasoning (HGR) model, which decomposes video-text matching into global-to-local levels.
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks.
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
dandelin/vilt
•
•
Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks.
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
