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Jin-Peng Lan

5 accepted papers

2025

MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis

ICLR 2025poster

MetaDesigner introduces a transformative framework for artistic typography synthesis, powered by Large Language Models (LLMs) and grounded in a user-centric design paradigm. Its foundation is a multi-agent system comprising the Pipeline, Glyph, and Texture agents, which collectively orchestrate the…

Cited by 2SourcePDFScholar
2024

Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception

CVPR 2024highlight

Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks. Recent efforts have been made to equip MLLMs with visual perceiving and grounding capabilities. However there still remains a gap in providing fine-grained pixel-level…

2023

Longshortnet: Exploring Temporal and Semantic Features Fusion In Streaming Perception

ICASSP 2023accepted

Streaming perception is a fundamental task in autonomous driving that requires a careful balance between the latency and accuracy of the autopilot system. However, current methods for streaming perception are limited as they rely only on the current and adjacent two frames to learn movement patterns…

Cited by 0SourceScholar
2023

Procontext: Exploring Progressive Context Transformer for Tracking

ICASSP 2023accepted

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it cannot account for changes in object appearance between frames. To this end, we revamped the tracking framework with P…

Cited by 0SourceScholar
2023

Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning

ICCV 2023oral

Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pursue unified frameworks to tackle this challenge but mostly still treat it as two individual learning tasks, which limits…

Cited by 12PDFcodeScholar