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20242026
most citedSCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

1 citations · 1 across the 1 of their papers we have counts for

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10 papers

cs.CV20261 cited

SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

Qiangang Du, Jinlong Peng, Xu Chen +4

In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets based on paired data. However,…

cs.CV2025

Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models

Yuansen Liu, Haiming Tang, Jinlong Peng +12

Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks. However, their capacity to comprehend human-centric scenes has rarely…

cs.CV2025

DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning

Jiawei Zhan, Jun Liu, Jinlong Peng +4

With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However,…

cs.CV2025

DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation

Qingdong He, Jinlong Peng, Pengcheng Xu +8

To enhance the controllability of text-to-image diffusion models, current ControlNet-like models have explored various control signals to dictate image attributes. However, existin…

cs.CV2025

PiT: Progressive Diffusion Transformer

Jiafu Wu, Yabiao Wang, Jian Li +4

Diffusion Transformers (DiTs) achieve remarkable performance within image generation via the transformer architecture. Conventionally, DiTs are constructed by stacking serial isotr…

cs.CV2025

PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models

Qingdong He, Jinlong Peng, Zhengkai Jiang +2

Recent success of vision foundation models have shown promising performance for the 2D perception tasks. However, it is difficult to train a 3D foundation network directly due to t…