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20242026
most citedParameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding

2 citations · 3 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

Enhancing Video Physical Consistency via Role-aware Joint Training and Modality-decoupled Denoising

Guangting Zheng, Haojing Chen, Hao Li +6

While modern video diffusion models excel in visual fidelity, maintaining long-range physical consistency remains a formidable challenge. Conventional pixel-reconstruction objectiv…

cs.CV2026

SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation

Shanwen Tan, Hao Li, Jingtao Zhang +4

Streaming long-video generation faces a central challenge in continuous semantic switching, requiring adaptive memory to preserve coherent visual evolution. Current approaches rely…

cs.CV2025

Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models

Gen Luo, Wenhan Dou, Wenhao Li +9

This paper focuses on monolithic Multimodal Large Language Models (MLLMs), which integrate visual encoding and language decoding into a single model. Existing structures and pre-tr…

cs.CV20251 cited

A Simple Aerial Detection Baseline of Multimodal Language Models

Qingyun Li, Yushi Chen, Xinya Shu +4

The multimodal language models (MLMs) based on generative pre-trained Transformer are considered powerful candidates for unifying various domains and tasks. MLMs developed for remo…

cs.CV2025

Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding

Zhaokai Wang, Xizhou Zhu, Xue Yang +8

Image pyramids are widely adopted in top-performing methods to obtain multi-scale features for precise visual perception and understanding. However, current image pyramids use the…

cs.CV2024

Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

Gen Luo, Xue Yang, Wenhan Dou +5

In this paper, we focus on monolithic Multimodal Large Language Models (MLLMs) that integrate visual encoding and language decoding into a single LLM. In particular, we identify th…