arXiv Computer Vision
Sep 24

VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

VIVAS is a new Vision‑Language Model pre‑training framework that addresses the lack of fine‑grained visual perception in existing VLMs. It introduces a unified token space and a dense‑structural‑semantic vision tokenizer that expands the textual vocabulary with visual tokens, enabling vision‑language unified autoregressive supervision over both visual details and linguistic content. Trained on 12.4 T tokens, VIVAS achieves state‑of‑the‑art results on 7 tasks and 39 multimodal benchmarks.

By Zhehan Kan, Yubo Zhu, Xinghua Jiang, Zhixiang Wei, Shifeng Liu, Wei Tong, Sheng Zhong, Qingmin Liao, Wenming Yang, Xin Li, Yinsong Liu, Deqiang Jiang, Xing Sun
arXiv Computer Vision
6d ago

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

ProCAP introduces a probabilistic cross-attentive prompt learning framework for vision-language models like CLIP, enabling improved cross-modal interaction without updating the backbone. It jointly learns visual and textual prompt tokens, linking them via stacked bidirectional multi-head cross-attention to refine each branch across prompt depth. The method incorporates Gaussian parameterization of prompt tokens, lightweight KL and L2 regularization, and a compact symmetric InfoNCE head to align image features with class-level text representations, achieving strong few-shot base-to-novel performance and competitive transfer results across multiple datasets and benchmarks.

By Hiwa Azeez Abbas, Fatemeh Daneshfar, Moloud Abdar
arXiv Computer Vision
Sep 18

Efficient Unified Multimodal Understanding (EUMU): Winning Solution for the MUMU Track at the 8th LSVOS Challenge

Efficient Unified Multimodal Understanding (EUMU) is the winning solution for the MUMU Track of the 8th LSVOS Challenge, addressing multi‑concept image tagging, open‑vocabulary object detection, and image captioning with a single efficient model. It leverages a shared pretrained multimodal backbone and lightweight heads, while applying task‑aware inference refinement that uses detection cues to improve captioning, caption cues to recover missed detections, and image statistics to refine tagging. With 239.169 M parameters, 23.947 GFLOPs, and 4.5 GB peak memory, EUMU achieves a challenge score of 17.3409 and is publicly available on GitHub.

By Dayoung Kil, Seong-heum Kim