https://github.com/perquisite/rshnl
공식 코드Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
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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.
Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching Models
Align before Search: Aligning Ads Image to Text for Accurate Cross-Modal Sponsored Search
Disentangling and Generating Modalities for Recommendation in Missing Modality Scenarios
VinVL: Revisiting Visual Representations in Vision-Language Models
Cross-modal Active Complementary Learning with Self-refining Correspondence
Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
Efficient Cross-Modal Retrieval via Deep Binary Hashing and Quantization
DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Position-guided Text Prompt for Vision-Language Pre-training
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model
Harmonized Multimodal Learning with Gaussian Process Latent Variable Models
FDDH: Fast Discriminative Discrete Hashing for Large-Scale Cross-Modal Retrieval
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
The Curious Layperson: Fine-Grained Image Recognition without Expert Labels
Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
Deep Sketched Output Kernel Regression for Structured Prediction
Finding beans in burgers: Deep semantic-visual embedding with localization
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
ColorSwap: A Color and Word Order Dataset for Multimodal Evaluation
AToMiC: An Image/Text Retrieval Test Collection to Support Multimedia Content Creation
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
Vision-Language Pre-Training with Triple Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.