Lingling Li

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...

Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection

Bridging the simulation-to-reality gap in roadside LiDAR requires addressing several coupled discrepancies, including scene geometry, sampling density, return patterns, and pedestrian scale. This report presents a multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection. The method...

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...

Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems

A five-dimensional analytical framework is introduced that clarifies how EI transforms isolated search trajectories into cumulative scientific insight, and identifies critical bottlenecks regarding evaluation, process traceability, and shared infrastructure.

Privacy-preserving video anomaly detection via federated learning