5 accepted papers
Image customization, a crucial technique for industrial media production, aims to generate content that is consistent with reference images. However, current approaches conventionally separate image customization into position-aware and position-free customization paradigms and lack a universal fram…
Recent years have witnessed substantial progress on monocular depth estimation, particularly as measured by the success of large models on standard benchmarks. However, performance on standard benchmarks does not offer a complete assessment, because most evaluate accuracy but not robustness. In thi…
Large Language Models (LLMs) have achieved outstanding performance across various NLP tasks. Grammatical Error Correction (GEC) is a task aiming at automatically correcting grammatical errors in text, but it encounters a severe shortage of annotated data. Researchers have tried to make full use of t…
This paper presents a novel multi-modal deep learning framework for industrial robot joint health assessment and prediction, leveraging non-invasive signal fusion and Bayesian optimization. The proposed method addresses the challenges of comprehensive joint state monitoring in complex industrial env…
For long-tailed image recognition tasks, re-weighting is effective to alleviate data imbalance by assigning higher weights to tail categories. However, existing re-weighting methods typically adopt a static weighting scheme, which usually hurts the accuracy of head categories. To deal with this issu…