https://github.com/zhjohnchan/sk-vg
공식 코드Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
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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.
Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
Evaluating Attribute Comprehension in Large Vision-Language Models
Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval
Increasing Textual Context Size Boosts Medical Image-Text Matching
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Deep Visual-Semantic Alignments for Generating Image Descriptions
VinVL: Revisiting Visual Representations in Vision-Language Models
VinVL: Revisiting Visual Representations in Vision-Language Models
Dual Attention Networks for Multimodal Reasoning and Matching
Impression-CLIP: Contrastive Shape-Impression Embedding for Fonts
Deep Visual-Semantic Alignments for Generating Image Descriptions
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Universal Weighting Metric Learning for Cross-Modal Matching
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.