6 citations · 8 across the 16 of their papers we have counts for
7 papers · 1 filter
DS-BiCA-Net: Pair-Preserving Cross-Temporal Modeling for Remote Sensing Change Captioning
Licheng Zhang, Naveed Akhtar, Siew-Kei Lam
Remote sensing image change captioning (RSICC) describes in natural language the semantic changes between two images of the same scene captured at different times. Previous methods…
On the Limitations of Cross-Lingual Consistency in Multilingual Text-to-image Generation
Sicheng Zhang, Zhonghao Yan, Binzhu Xie +4
Text-to-image (T2I) generation has achieved remarkable progress in recent years. However, existing research has largely focused on English-only settings, leaving cross-lingual perf…
Introspective Attention Modulation for Safe Text-to-Image Generation
Basim Azam, Hossein Rahmani, Naveed Akhtar
State-of-the-art flow based text-to-image (T2I) models exhibit remarkable generative abilities but remain vulnerable to producing unsafe content. Prior safety efforts range from co…
LBTCap: A Lightweight Bilateral Transformer for Real-Time Remote Sensing Image Change Captioning
Licheng Zhang, Siew-Kei Lam, Naveed Akhtar
Remote sensing image change captioning (RSICC) generates natural-language descriptions of semantic changes between paired remote sensing images (RSIs), supporting applications such…
Beyond Pixel Diffs: Benchmarking Image Change Captioning for Web UI Visual Regression Testing
Licheng Zhang, Bach Le, Pengtao Zhao +1
Visual regression testing (VRT) is a standard quality assurance step in modern software release pipelines. On every change, it re-renders user interface (UI) screenshots, compares…
Intermediate Text Representation Guided Text-to-Image Generation for Enhancing One-and-Only Alignment
Soyoun Won, Aryan Yazdan Parast, Basim Azam +2
Text-to-image (T2I) diffusion models often fail to faithfully render explicit textual descriptions, instead defaulting to strongly learned visual priors due to a phenomenon referre…
HSFM: Hard-Set-Guided Feature-Space Meta-Learning for Robust Classification under Spurious Correlations
Aryan Yazdan Parast, Khawar Islam, Soyoun Won +2
Deep neural networks often rely on spurious features to make predictions, which makes them brittle under distribution shift and on samples where the spurious correlation does not h…