https://github.com/mesnico/aladin
공식 코드ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
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Cross-Modal Information Retrieval (CMIR) is the task of finding relevant items across different modalities. For example, given an image, find a text or vice versa. The main challenge in CMIR is known as the heterogeneity gap: since items from different modalities have different data types, the similarity between them cannot be measured directly. Therefore, the majority of CMIR methods published to date attempt to bridge this gap by learning a latent representation space, where the similarity between items from different modalities can be measured.
Source: Scene-centric vs. Object-centric Image-Text Cross-modal Retrieval: A Reproducibility Study
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and Retrieval
Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
Fusion and Orthogonal Projection for Improved Face-Voice Association
Effective and Efficient Indexing in Cross-Modal Hashing-Based Datasets
Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval
Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval
Nearest Neighbor Normalization Improves Multimodal Retrieval
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
Learning Tri-modal Embeddings for Zero-Shot Soundscape Mapping
TaxaBind: A Unified Embedding Space for Ecological Applications
MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement Learning
Bridging Vision and Language Spaces with Assignment Prediction
Cross-modal Retrieval for Knowledge-based Visual Question Answering
Self-supervised vision-language pretraining for Medical visual question answering
Deep Reversible Consistency Learning for Cross-modal Retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.