most citedFine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark

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

collaborators

6 papers

cs.CV20261 cited

Fine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark

Ying Liu, Yijing Hua, Haojiang Chai +2

Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary data used for open-vocabulary learn…

cs.CV2026

Object-Centric Vision Token Pruning for Vision Language Models

Guangyuan Li, Rongzhen Zhao, Jinhong Deng +2

In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too much unnecessary computation. Pruning r…

cs.RO2026

Manual2Skill++: Connector-Aware General Robotic Assembly from Instruction Manuals via Vision-Language Models

Chenrui Tie, Shengxiang Sun, Yudi Lin +9

Assembly hinges on reliably forming connections between parts; yet most robotic approaches plan assembly sequences and part poses while treating connectors as an afterthought. Conn…

cs.CV2025

InfSplign: Inference-Time Spatial Alignment of Text-to-Image Diffusion Models

Sarah Rastegar, Violeta Chatalbasheva, Sieger Falkena +5

Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced to two fa…

cs.CV2025

Compositional Scene Understanding through Inverse Generative Modeling

Yanbo Wang, Justin Dauwels, Yilun Du

Generative models have demonstrated remarkable abilities in generating high-fidelity visual content. In this work, we explore how generative models can further be used not only to…

cs.AR2025

NLS: Natural-Level Synthesis for Hardware Implementation Through GenAI

Kaiyuan Yang, Huang Ouyang, Xinyi Wang +6

This paper introduces Natural-Level Synthesis, an innovative approach for generating hardware using generative artificial intelligence on both the system level and component-level.…