5 citations · 11 across the 25 of their papers we have counts for
10 papers · 1 filter
ProCLIP: Progressive Vision-Language Alignment via LLM-based Embedder
Xiaoxing Hu, Kaicheng Yang, Ziyang Gong +7
The original CLIP text encoder is limited by a maximum input length of 77 tokens, which hampers its ability to effectively process long texts and perform fine-grained semantic unde…
UniME-V2: MLLM-as-a-Judge for Universal Multimodal Embedding Learning
Tiancheng Gu, Kaicheng Yang, Kaichen Zhang +6
Universal multimodal embedding models are foundational to various tasks. Existing approaches typically employ in-batch negative mining by measuring the similarity of query-candidat…
Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval
Tianlu Zheng, Yifan Zhang, Xiang An +3
Although Contrastive Language-Image Pre-training (CLIP) exhibits strong performance across diverse vision tasks, its application to person representation learning faces two critica…
LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training
Xiang An, Yin Xie, Kaicheng Yang +20
We present LLaVA-OneVision-1.5, a novel family of Large Multimodal Models (LMMs) that achieve state-of-the-art performance with significantly reduced computational and financial co…
PaCo-FR: Patch-Pixel Aligned End-to-End Codebook Learning for Facial Representation Pre-training
Yin Xie, Zhichao Chen, Zeyu Xiao +7
Facial representation pre-training is crucial for tasks like facial recognition, expression analysis, and virtual reality. However, existing methods face three key challenges: (1)…
Region-based Cluster Discrimination for Visual Representation Learning
Yin Xie, Kaicheng Yang, Xiang An +9
Learning visual representations is foundational for a broad spectrum of downstream tasks. Although recent vision-language contrastive models, such as CLIP and SigLIP, have achieved…
ForCenNet: Foreground-Centric Network for Document Image Rectification
Peng Cai, Qiang Li, Kaicheng Yang +6
Document image rectification aims to eliminate geometric deformation in photographed documents to facilitate text recognition. However, existing methods often neglect the significa…
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs
Tiancheng Gu, Kaicheng Yang, Ziyong Feng +6
The Contrastive Language-Image Pre-training (CLIP) framework has become a widely used approach for multimodal representation learning, particularly in image-text retrieval and clus…
Decoupled Global-Local Alignment for Improving Compositional Understanding
Xiaoxing Hu, Kaicheng Yang, Jun Wang +3
Contrastive Language-Image Pre-training (CLIP) has achieved success on multiple downstream tasks by aligning image and text modalities. However, the nature of global contrastive le…
RealSyn: An Effective and Scalable Multimodal Interleaved Document Transformation Paradigm
Tiancheng Gu, Kaicheng Yang, Chaoyi Zhang +6
After pre-training on extensive image-text pairs, Contrastive Language-Image Pre-training (CLIP) demonstrates promising performance on a wide variety of benchmarks. However, a subs…