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[2411. 16828] CLIPS: An Enhanced CLIP Framework for Learning with . . . Abstract page for arXiv paper 2411 16828: CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions Previous works show that noisy, web-crawled image-text pairs may limit vision-language pretraining like CLIP and propose learning with synthetic captions as a promising alternative
[2411. 04997] LLM2CLIP: Powerful Language Model Unlocks Richer Visual . . . We propose an efficient post-training strategy that integrates LLMs into pretrained CLIP To address the challenge posed by the autoregressive nature of LLMs, we introduce a caption-to-caption contrastive fine-tuning framework, significantly enhancing the discriminative quality of LLM outputs
Learning Transferable Visual Models From Natural Language Supervision ConVIRT trained from scratch, which we call CLIP, for Con-trastive Language-Image Pre-training, is an efficient method of learning from natural language supervision We study the scalability of CLIP by training a series of eight models spanning almost 2 orders of magnitude of compute and ob-serve that transfer performance is a smoothly predictable
Title: Alpha-CLIP: A CLIP Model Focusing on Wherever You Want - arXiv. org Abstract: Contrastive Language-Image Pre-training (CLIP) plays an essential role in extracting valuable content information from images across diverse tasks It aligns textual and visual modalities to comprehend the entire image, including all the details, even those irrelevant to specific tasks
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator Specifically, we propose a CLIP-based discriminator
Title: Long-CLIP: Unlocking the Long-Text Capability of CLIP - arXiv. org To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks
[2404. 08197] Scaling (Down) CLIP: A Comprehensive Analysis of Data . . . Abstract: This paper investigates the performance of the Contrastive Language-Image Pre-training (CLIP) when scaled down to limited computation budgets We explore CLIP along three dimensions: data, architecture, and training strategies
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP AA-CLIP is achieved through a straightforward yet effective two-stage approach: it first creates anomaly-aware text anchors to differentiate normal and abnormal semantics clearly, then aligns patch-level visual features with these anchors for precise anomaly localization
CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World . . . To take a step toward open-world 3D vision understanding, we propose Contrastive Language-Image-Point Cloud Pretraining (CLIP$^2$) to directly learn the transferable 3D point cloud representation in realistic scenarios with a novel proxy alignment mechanism