https://github.com/wangzhecheng/skyscript
공식 코드SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Cross-Modal Retrieval (CMR) is a task of retrieving items across different modalities, such as image, text, video, and audio. The core challenge of CMR is the heterogeneity gap, which arises because data from different modalities have distinct representations, making direct comparison difficult. To address this, most CMR methods focus on learning a shared latent embedding space. In this space, concepts from different modalities are projected, allowing their similarity to be measured using a distance metric.
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.
SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
Universal Weighting Metric Learning for Cross-Modal Matching
Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
Enhancing Image-Text Matching with Adaptive Feature Aggregation
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
Learning to Evaluate Performance of Multi-modal Semantic Localization
Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images
3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion Map
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
Improving Medical Multi-modal Contrastive Learning with Expert Annotations
LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition
Language Models Can See: Plugging Visual Controls in Text Generation
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
Image-text Retrieval via Preserving Main Semantics of Vision
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.