ProCLIP introduces a curriculum‑learning framework that progressively aligns a CLIP image encoder with an LLM‑based text embedder. It first distills CLIP’s text encoder knowledge into the LLM embedder, then fine‑tunes the image encoder via contrastive learning with self‑distillation, instance semantic alignment, and embedding structure alignment losses. The approach addresses CLIP’s token limit, lack of multilingual support, and preserves pre‑trained vision‑language alignment.
By Xiaoxing Hu, Kaicheng Yang, Ziyang Gong, Qi Ming, Zonghao Guo, Yu Tian, Xiang An, Ziyong Feng, Xue Yang
arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.
By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim
DEPICT is a new training‑free metric for evaluating text‑to‑image alignment. It replaces fixed reference answers with an agreement rule that compares image‑based and caption‑only responses, weighting questions by how decisively the caption determines them. By merging this agreement score with a holistic score, DEPICT improves negation accuracy dramatically and outperforms existing training‑free metrics while matching or exceeding fine‑tuned evaluators on several benchmarks.
By Vasco Ramos, Sandra Godinho Silva, Joao Magalhaes, Ricardo Rei, Pedro Henrique Martins
Flow-CLIP introduces a method to improve compositional image-text matching by refining global image-text alignment with image-conditioned changes in token–patch evidence. It combines Global Compatibility Calibration (GCC) for sentence-level similarity, Image-Conditioned Evidence Refinement (ICER) to incorporate visual patch context, and Cross-Image Margin Ranking (CIMR) to enhance ranking margins when images support correct descriptions. Experiments demonstrate that Flow-CLIP outperforms pretrained CLIP and other global alignment approaches on binding accuracy and reduces reliance on language priors.
By Minghong Wei, Enguang Zhu, Pu Cao, Lu Yang, Qing Song
The paper introduces a framework that aligns multimodal embeddings with language by using captioning functions on interpretable auxiliary data, a soft contrastive loss, and a rank-based concept retrieval task. Applied to Earth observation embeddings, the method improves species distribution modelling, achieving state‑of‑the‑art performance on most metrics and better concept retrieval than prior vision‑language models. The aligned concepts enable recovery of species‑habitat associations and allow domain experts to interpret model predictions in natural language.
By Gabriel\.e Tij\=unaityt\.e, Thijs L. van der Plas, Mohamed-Bachir Belaid, Rob Knapen, Maciej J. Soja, Ioannis N. Athanasiadis
arXiv:2607. 22919v1 Announce Type: cross Abstract: Multimodal embedding spaces in models like CLIP enable powerful capabilities such as semantic similarity retrieval and cross-modal zero-shot classification.
By Joseph Fioresi, Fabian Caba Heilbron, Pankaj Nathani, Mubarak Shah, Kushal Kafle
arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.
By Peiyuan Zhu, Shaoan Xie, Zijian Li, Yifan Shen, Namrata Deka, Harsh Shrivastava, Guangyi Chen, Kun Zhang
arXiv:2610.07689v1 Announce Type: new
Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense p...
By Juntong Li, Lingwei Dang, Haomin Wu, Ziyan Qiu, Qingxin Xiao, Qingyao Wu
Contrastive Language-Image Pre-training (CLIP) has been shown to have limitations in its fine-grained dense feature representation, due to its pre-training focusing on matching the whole image to a text description. Considering the large data and computational burden in pre-training a vision-language model from scratch, a series of works aim to enhance the fine-grained ability of CLIP through a fine-tuning scheme.
arXiv:2511. 01390v2 Announce Type: replace-cross Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between vision and language, forming a cornerstone for visual question answering and related multimodal applications.
By Xinyu Mao, Junsi Li, Haoji Zhang, Yu Liang, Ming Sun
arXiv:2609.24564v1 Announce Type: new
Abstract: CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing CLIP's text encode...
By Zelin Peng, Zhengqin Xu, Changsong Wen, Yu Huang, Yaoming Wang, Xiaokang Yang, Wei Shen
arXiv:2606. 17950v1 Announce Type: cross Abstract: Visual information helps resolve ambiguity in coreference resolution, leading to notable performance gains.
By Jinghan Wu, Jing Li, Ivor W. Tsang, Xuetao Zhang