Image de couverture de livre

Fine-tuning CLIP Text Encoders with Two-step Paraphrasing

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kim, Hyunjae, Yoon, Seunghyun, Bui, Trung, Zhao, Handong, Tran, Quan, Dernoncourt, Franck, Kang, Jaewoo
Format:	Preprint
Publié:	2024
Sujets:	Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

ViT-AdaLA: Adapting Vision Transformers with Linear Attention
par: Li, Yifan, et autres
Publié: (2026)

MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos
par: Zang, Yuan, et autres
Publié: (2025)

Scaling Up Video Summarization Pretraining with Large Language Models
par: Argaw, Dawit Mureja, et autres
Publié: (2024)

FIFA: Unified Faithfulness Evaluation Framework for Text-to-Video and Video-to-Text Generation
par: Jing, Liqiang, et autres
Publié: (2025)

Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
par: Lee, Saehyung, et autres
Publié: (2024)

StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
par: Lee, Daeun, et autres
Publié: (2025)

Semantic-aware Adversarial Fine-tuning for CLIP
par: Zhang, Jiacheng, et autres
Publié: (2026)

Supercharged One-step Text-to-Image Diffusion Models with Negative Prompts
par: Nguyen, Viet, et autres
Publié: (2024)

The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers
par: Qi, Daiqing, et autres
Publié: (2025)

Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications
par: Nguyen, Nghia, et autres
Publié: (2024)

LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts
par: Cao, Anh-Quan, et autres
Publié: (2024)

Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
par: Liu, Mushui, et autres
Publié: (2024)

Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models
par: Morelli, Fabian, et autres
Publié: (2026)

Benchmarking Direct Preference Optimization for Medical Large Vision-Language Models
par: Kim, Dain, et autres
Publié: (2026)

Extending CLIP's Image-Text Alignment to Referring Image Segmentation
par: Kim, Seoyeon, et autres
Publié: (2023)

Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
par: Zhou, Ziqi, et autres
Publié: (2024)

SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning
par: Kim, Minjun, et autres
Publié: (2026)

Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
par: Li, Jiachen, et autres
Publié: (2023)

Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image Generation
par: Dao, Quan, et autres
Publié: (2024)

Understanding Generative AI Capabilities in Everyday Image Editing Tasks
par: Taesiri, Mohammad Reza, et autres
Publié: (2025)

Breaking the Encoder Barrier for Seamless Video-Language Understanding
par: Li, Handong, et autres
Publié: (2025)

Not Just What's There: Enabling CLIP to Comprehend Negated Visual Descriptions Without Fine-tuning
par: Xiao, Junhao, et autres
Publié: (2026)

Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
par: Liang, Jian, et autres
Publié: (2023)

Breaking the Limits of Open-Weight CLIP: An Optimization Framework for Self-supervised Fine-tuning of CLIP
par: Mehta, Anant, et autres
Publié: (2026)

PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
par: Pham, Thang M., et autres
Publié: (2024)

Leveraging CLIP Encoder for Multimodal Emotion Recognition
par: Song, Yehun, et autres
Publié: (2025)

CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model
par: Zhao, Shuai, et autres
Publié: (2023)

CORG: Generating Answers from Complex, Interrelated Contexts
par: Lee, Hyunji, et autres
Publié: (2025)

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP
par: Kim, Eunji, et autres
Publié: (2024)

TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation
par: Park, NaHyeon, et autres
Publié: (2024)

DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models
par: Ram, Shwetha, et autres
Publié: (2024)

TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection
par: Yoon, Jiae, et autres
Publié: (2026)

Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates
par: Ahn, Jaewoo, et autres
Publié: (2025)

CalibCLIP: Contextual Calibration of Dominant Semantics for Text-Driven Image Retrieval
par: Kang, Bin, et autres
Publié: (2025)

Enhancing Fine-grained Image Classification through Attentive Batch Training
par: Le, Duy M., et autres
Publié: (2024)

CLIP-VQDiffusion : Langauge Free Training of Text To Image generation using CLIP and vector quantized diffusion model
par: Han, Seungdae, et autres
Publié: (2024)

Robustness in Both Domains: CLIP Needs a Robust Text Encoder
par: Rocamora, Elias Abad, et autres
Publié: (2025)

Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
par: Singh, Naman Deep, et autres
Publié: (2024)

Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching ability
par: Yoon, Yejun, et autres
Publié: (2024)

DetailCLIP: Detail-Oriented CLIP for Fine-Grained Tasks
par: Monsefi, Amin Karimi, et autres
Publié: (2024)