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20242026
most citedDeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution

2 citations · 2 across the 12 of their papers we have counts for

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cs.CV2026

Bridging the RGB-IR Gap: Consensus and Discrepancy Modeling for Text-Guided Multispectral Detection

Jiaqi Wu, Zhen Wang, Enhao Huang +5

Text-guided multispectral object detection uses text semantics to guide semantic-aware cross-modal interaction between RGB and IR for more robust perception. However, notable limit…

cs.CV2026

AdaGen: Learning Adaptive Policy for Image Synthesis

Zanlin Ni, Yulin Wang, Yeguo Hua +5

Recent advances in image synthesis have been propelled by powerful generative models, such as Masked Generative Transformers (MaskGIT), autoregressive models, diffusion models, and…

cs.CV2025

IMG: Calibrating Diffusion Models via Implicit Multimodal Guidance

Jiayi Guo, Chuanhao Yan, Xingqian Xu +4

Ensuring precise multimodal alignment between diffusion-generated images and input prompts has been a long-standing challenge. Earlier works finetune diffusion weight using high-qu…

cs.CV2025

Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception

Yulin Wang, Yang Yue, Huanqian Wang +11

Human vision is highly adaptive, efficiently sampling intricate environments by sequentially fixating on task-relevant regions. In contrast, prevailing machine vision models passiv…

cs.CV2025

CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning

Yang Yue, Yulin Wang, Chenxin Tao +3

Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has eme…

cs.CV2025

EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance

Yang Yue, Yulin Wang, Haojun Jiang +3

Echocardiography is crucial for cardiovascular disease detection but relies heavily on experienced sonographers. Echocardiography probe guidance systems, which provide real-time mo…