https://github.com/ubc-tea/backdoor_multimodal_foundation_model
공식 코드Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image-Text Matching is a subtask within Cross-Modal Retrieval (CMR) that involves establishing associations between images and corresponding textual descriptions. The goal is to retrieve an image given a textual query or, conversely, retrieve a textual description given an image query. This task is challenging due to the heterogeneity gap between image and text data representations. Image-text matching is used in applications such as content-based image search, visual question answering, and multimodal summarization.
Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Backdoor Attack on Unpaired Medical Image-Text Foundation Models: A Pilot Study on MedCLIP
Composing Object Relations and Attributes for Image-Text Matching
Universal Weighting Metric Learning for Cross-Modal Matching
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding
Enhancing Image-Text Matching with Adaptive Feature Aggregation
LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
Language Models Can See: Plugging Visual Controls in Text Generation
Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
Evaluating Attribute Comprehension in Large Vision-Language Models
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
Increasing Textual Context Size Boosts Medical Image-Text Matching
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
VinVL: Revisiting Visual Representations in Vision-Language Models
VinVL: Revisiting Visual Representations in Vision-Language Models
Dual Attention Networks for Multimodal Reasoning and Matching
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Universal Weighting Metric Learning for Cross-Modal Matching
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.