5 accepted papers
Pretrained vision encoders are widely used as frozen, black-box feature extractors, yet they often inherit spurious correlations that disproportionately harm underrepresented groups. We introduce PLD-Debias, a fully black-box debiasing framework that requires neither access to backbone parameters no
Deep models often exploit spurious correlations (e.g., backgrounds or dataset artifacts), hurting worst-group performance. We propose \textbf{Evidence-Gated Suppression (EGS)}, a lightweight, plug-in regularizer that intervenes inside the network during training. EGS tracks a class-conditional, conf…
Change detection has long been used for various tasks. With advancements in robotic systems and computer vision, change detection techniques can be further explored for diverse applications. Current state-of-the-art methods primarily use either satellite images or street-level images to detect chang
Bias mitigation in machine learning models is imperative, yet challenging. While several approaches have been proposed, one view towards mitigating bias is through adversarial learning. A discriminator is used to identify the bias attributes such as gender, age or race in question. This discriminato…
There have been a number of techniques that have demonstrated the generation of multimedia data for one modality at a time using GANs, such as the ability to generate images, videos, and audio. However, so far, the task of multi-modal generation of data, specifically for audio and videos both, has n…