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20242026
most citedClassifier-guided Gradient Modulation for Enhanced Multimodal Learning

1 citations · 2 across the 10 of their papers we have counts for

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cs.CV2026

Bridging the Pose-Semantic Gap: A Cascade Framework for Text-Based Person Anomaly Search

Zequn Xie, Guijin Luo, Chuxin Wang +4

Text-based person anomaly search retrieves specific behavioral events from surveillance archives using natural-language queries. Although recent pose-aware methods align geometric…

cs.CV2026

Diffusion Model as a Generalist Segmentation Learner

Haoxiao Wang, Antao Xiang, Haiyang Sun +8

Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper, we demonstrate that these…

cs.CV2026

WorldEdit: Towards Open-World Image Editing with a Knowledge-Informed Benchmark

Wang Lin, Feng Wang, Majun Zhang +7

Recent advances in image editing models have demonstrated remarkable capabilities in executing explicit instructions, such as attribute manipulation, style transfer, and pose synth…

cs.CV2025

IRBridge: Solving Image Restoration Bridge with Pre-trained Generative Diffusion Models

Hanting Wang, Tao Jin, Wang Lin +4

Bridge models in image restoration construct a diffusion process from degraded to clear images. However, existing methods typically require training a bridge model from scratch for…

cs.CV2025

Towards Transformer-Based Aligned Generation with Self-Coherence Guidance

Shulei Wang, Wang Lin, Hai Huang +8

We introduce a novel, training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs). Existing TGDMs often struggle to generate semantical…

cs.CV2024

Semantic Alignment for Multimodal Large Language Models

Tao Wu, Mengze Li, Jingyuan Chen +6

Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly…

cs.CV2024★ 1 cited

Non-confusing Generation of Customized Concepts in Diffusion Models

Wang Lin, Jingyuan Chen, Jiaxin Shi +8

We tackle the common challenge of inter-concept visual confusion in compositional concept generation using text-guided diffusion models (TGDMs). It becomes even more pronounced in…