Understanding the reliability of model explanations remains a critical challenge in deep learning. Prior work has shown that saliency maps can be manipulated by optimizing the input using gradient-based methods, where gradients of the loss with respect to the input are computed to generate dense perturbations that alte...
Khoa Tran, Thai Huy Nguyen, Quan Minh Phan et al.· International Conference on...· 0 citations
Weakly supervised semantic segmentation (WSSS) aims to train dense prediction models from inexpensive supervision such as image-level labels. Recent foundation models provide complementary capabilities: promptable segmentation models can produce high-coverage object masks, while self-supervised vision transformers prov...
Duc-Hien Nguyen, L. Nguyễn, X. Nguyễn et al.· International Conference on...· 0 citations
Amodal appearance completion in open-world scenarios requires reconstructing the hidden regions of occluded objects, a task that demands both geometric extrapolation and high-level semantic understanding. Conventional methods predominantly rely on geometric priors, often failing to maintain semantic coherence and struc...