SigLIP 2: A better multilingual vision language encoder

jina-vlm: Small Multilingual Vision Language Model

arXiv:2512. 04032v4 Announce Type: replace-cross Abstract: We present jina-vlm, a token-efficient 2.

Vision Language Models (Better, faster, stronger)

Fine-tuning Florence-2 - Microsoft's Cutting-edge Vision Language Models

Accelerating Vision-Language Models: BridgeTower on Habana Gaudi2

A Dive into Vision-Language Models

Vision Language Models Explained

MAViE: A Multi-scale Adaptive Vision Encoder for Fine-grained Visual Perception and Efficient Multimodal Reasoning

Vision-language models commonly project all tokens produced by a pretrained vision encoder into a large language model. However, final-layer features can discard text, local attributes, and spatial relationships, while high-resolution inputs substantially increase context length and inference latency.

A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality

Introducing Idefics2: A Powerful 8B Vision-Language Model for the community

EuroBERT: Scaling Multilingual Encoders for European Languages

arXiv:2503. 05500v3 Announce Type: replace-cross Abstract: General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirectional encoder models.

Vision-Language Models are Fragile Multilingual Associators

arXiv:2608. 12333v1 Announce Type: cross Abstract: Vision-language models must associate visual entities with textual attributes.

Projector Is All You Train

arXiv:2608. 19726v1 Announce Type: cross Abstract: The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder.