15 citations · 23 across the 6 of their papers we have counts for
6 papers
InvSeg: Test-Time Prompt Inversion for Semantic Segmentation
Jiayi Lin, Jiabo Huang, Jian Hu +1
Visual-textual correlations in the attention maps derived from text-to-image diffusion models are proven beneficial to dense visual prediction tasks, e.g., semantic segmentation. H…
Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation
Jian Hu, Jiayi Lin, Junchi Yan +1
Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segme…
Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects
Jian Hu, Jiayi Lin, Weitong Cai +1
Camouflaged object detection (COD) approaches heavily rely on pixel-level annotated datasets. Weakly-supervised COD (WSCOD) approaches use sparse annotations like scribbles or poin…
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
Jian Hu, Haowen Zhong, Junchi Yan +3
Domain adaptation (DA) aims to transfer knowledge learned from a labeled source domain to an unlabeled or a less labeled but related target domain. Ideally, the source and target d…
Self-Adaptive Partial Domain Adaptation
Jian Hu, Hongya Tuo, Shizhao Zhang +6
Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space. However, the mismatc…
Domain Adaptive YOLO for One-Stage Cross-Domain Detection
Shizhao Zhang, Hongya Tuo, Jian Hu +1
Domain shift is a major challenge for object detectors to generalize well to real world applications. Emerging techniques of domain adaptation for two-stage detectors help to tackl…