7 papers · 1 filter
Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval
Zelong Sun, Jun Wang, Kaicheng Yang +3
Unified multimodal retrieval aims to identify candidates that satisfy complex user intent expressed through heterogeneous inputs. Although Large Vision-Language Model (LVLM)-based…
DanQing: An Up-to-Date Large-Scale Chinese Vision-Language Pre-training Dataset
Hengyu Shen, Tiancheng Gu, Bin Qin +10
Vision-Language Pre-training (VLP) models have achieved remarkable success by leveraging large-scale image-text pairs. While English-centric models like CLIP and SigLIP benefit fro…
Can Synthetic Images Serve as Effective and Efficient Class Prototypes?
Dianxing Shi, Dingjie Fu, Yuqiao Liu +1
Vision-Language Models (VLMs) have shown strong performance in zero-shot image classification tasks. However, existing methods, including Contrastive Language-Image Pre-training (C…
Dynamic Pattern Alignment Learning for Pretraining Lightweight Human-Centric Vision Models
Xuanhan Wang, Huimin Deng, Ke Liu +3
Human-centric vision models (HVMs) have achieved remarkable generalization due to large-scale pretraining on massive person images. However, their dependence on large neural archit…
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
BLIP3-KALE: Knowledge Augmented Large-Scale Dense Captions
Anas Awadalla, Le Xue, Manli Shu +13
We introduce BLIP3-KALE, a dataset of 218 million image-text pairs that bridges the gap between descriptive synthetic captions and factual web-scale alt-text. KALE augments synthet…