Latest Articles for CLIP

New method improves learning new classes with less data.

ProText enhances vision-language models using text-only data for better task handling.

A look into the MacCap framework and its impact on image captioning.

SpLiCE helps clarify the dense data from CLIP for better understanding.

Leveraging CLIP's visual and text components improves deepfake detection methods.

A new method helps robots interpret human commands more effectively.

PosSAM improves image segmentation with open-vocabulary capabilities and innovative techniques.

SNAP-PROTACs enhance protein study and targeted degradation techniques.

SaLIP combines SAM and CLIP for efficient medical image segmentation.

A method to enhance image generation using Large Language Models.

A new approach aligns language models with video content using textual simulations.

A framework to link image processing and text interpretation in vision models.

A method to enhance the identification of fake news using social media interactions.

WeCLIP improves weakly supervised segmentation using CLIP with minimal labeling effort.

A novel approach enhancing UDA performance using CLIP and language guidance.

New methods improve the speed and quality of text-to-image generation.

CLIP-CITE enhances CLIP models for specialized tasks while retaining flexibility.

FALIP enhances CLIP's image and text understanding without altering originals.

New technology helps patients express thoughts through EEG signals.

NOVIC introduces open vocabulary capabilities for identifying unseen objects in images.

A new method improves anomaly detection by tackling text clustering in models.

A new method improves book matching for library catalogs using advanced techniques.

A new system improves robots' ability to follow language commands effectively.

MAFT+ framework enhances object segmentation using collaborative optimization of vision and text.

A new network improves point cloud classification through image translation.

HOIGen introduces a new method for recognizing unseen human-object interactions.

CLIP-CID improves data efficiency in vision-language models.

A new framework boosts medical image analysis using visual symptoms and advanced prompting techniques.

This study assesses VLMs for traffic congestion, crack detection, and helmet compliance.

A new method enhances the understanding of museum exhibits using CLIP technology.

Study compares human and AI abilities in recognizing 3D shapes from different views.

This article reveals methods to interpret CLIP-like models in AI.

This work enhances CLIP's accuracy by addressing intra-modal overlap using lightweight adapters.

Researchers present Blind-VaLM, enhancing language models with visual knowledge efficiently.

A new method for assessing T2I model performance across diverse text prompts.

PiVOT enhances object tracking using visual prompting and CLIP for improved accuracy.

SuperClass simplifies image and text recognition for easier research access.

An overview of the strengths and flaws in today's Vision-Language Models.

This article examines zero-shot techniques for detecting anomalies in medical images.

Trident combines models to enhance image segmentation and detail recognition.