most citedMoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing

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cs.CV2026

Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm

Yaofang Liu, Kangning Cui, Meng Chu +7

Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generators still ask users to seria…

cs.CV2026

Learning Where to Embed: Noise-Aware Positional Embedding for Query Retrieval in Small-Object Detection

Yangchen Zeng, Zhenyu Yu, Dongming Jiang +5

Transformer-based detectors have advanced small-object detection, but they often remain inefficient and vulnerable to background-induced query noise, which motivates deep decoders…

cs.CV2026

StoryState: Agent-Based State Control for Consistent and Editable Storybooks

Ayushman Sarkar, Zhenyu Yu, Wei Tang +3

Large multimodal models have enabled one-click storybook generation, where users provide a short description and receive a multi-page illustrated story. However, the underlying sto…

cs.CV2026

ReDiStory: Region-Disentangled Diffusion for Consistent Visual Story Generation

Ayushman Sarkar, Zhenyu Yu, Chu Chen +3

Generating coherent visual stories requires maintaining subject identity across multiple images while preserving frame-specific semantics. Recent training-free methods concatenate…

cs.CV20251 cited

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing

Debashis Gupta, Aditi Golder, Rongkhun Zhu +6

Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrin…

cs.CV2025

Center-guided Classifier for Semantic Segmentation of Remote Sensing Images

Wei Zhang, Mengting Ma, Yizhen Jiang +4

Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing im…