25 citations · 34 across the 7 of their papers we have counts for
7 papers
FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation
Xi Chen, Haosen Yang, Sheng Jin +2
Open-vocabulary segmentation poses significant challenges, as it requires segmenting and recognizing objects across an open set of categories in unconstrained environments. Buildin…
Unsupervised Audio-Visual Segmentation with Modality Alignment
Swapnil Bhosale, Haosen Yang, Diptesh Kanojia +2
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained anno…
Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation
Xi Chen, Haosen Yang, Huicong Zhang +2
Source-free unsupervised domain adaptation (SFUDA) aims to enable the utilization of a pre-trained source model in an unlabeled target domain without access to source data. Self-tr…
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
Hong Liu, Haosen Yang, Paul J. van Diest +2
The Segment Anything Model (SAM) marks a significant advancement in segmentation models, offering robust zero-shot abilities and dynamic prompting. However, existing medical SAMs a…
Leveraging Foundation models for Unsupervised Audio-Visual Segmentation
Swapnil Bhosale, Haosen Yang, Diptesh Kanojia +1
Audio-Visual Segmentation (AVS) aims to precisely outline audible objects in a visual scene at the pixel level. Existing AVS methods require fine-grained annotations of audio-mask…
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
Boyang Xia, Wenhao Wu, Haoran Wang +5
It is challenging for artificial intelligence systems to achieve accurate video recognition under the scenario of low computation costs. Adaptive inference based efficient video re…