Xu Liu

SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation

Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals into one context, although they receive different supervision and fail differently...

0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation

Recent advances in vision foundation models (VFMs) have shown remarkable capabilities across diverse unimodal visual tasks. However, adapting VFMs to referring image segmentation (RIS) typically necessitates precise vision-language alignment via full fine-tuning, incurring substantial computational overhead and risking...

DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

Multi-task visual grounding requires models to jointly understand linguistic semantics and perform accurate visual localization and segmentation. Despite the success of multimodal large language models, effectively adapting them to multiple grounding objectives remains challenging. Existing methods commonly enforce tas...

The PID-Aware Geometric Semantic Representation Learning for Infrared-Visible Image Fusion

To relieve the problems of the lack of semantic alignment in the geometric representation space and insufficient modality-specific instruction calibration in infrared-visible image fusion (IVIF), we first propose a dynamic instruction-aware geometric representation (DIGR) for IVIF, which achieves semantic alignment bet...

RefZVC: Refinable Zero-Shot Video Captioning by Test-Time Reinforcement Polishing

Recently, the zero-shot image captioning (zero-shot IC) method based on pre-trained visual language models (VLMs) and large language models (LLMs) has made significant progress. However, how to adapt it to the zero-shot video captioning (zero-shot VC) scenario (without video-text paired supervision) has not been well e...

Privacy-preserving video anomaly detection via federated learning