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Found 9 papers, 4 papers with code
Unsupervised Audio-Visual Segmentation with Modality Alignment
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound.
Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation
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.
WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images
In particular, our model outperforms SAM by 4. 1 and 2. 5 percent points on a ductal carcinoma in situ (DCIS) segmentation tasks and breast cancer metastasis segmentation task (CAMELYON16 dataset).
Recognize Any Regions
Understanding the semantics of individual regions or patches within unconstrained images, such as in open-world object detection, represents a critical yet challenging task in computer vision.
Leveraging Foundation models for Unsupervised Audio-Visual Segmentation
Particularly, in situations where existing supervised AVS methods struggle with overlapping foreground objects, our models still excel in accurately segmenting overlapped auditory objects.
Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
As a result, our model can extract effectively both static appearance and dynamic motion spontaneously, leading to superior spatiotemporal representation learning capability.
NSNet: Non-saliency Suppression Sampler for Efficient Video Recognition
On the video level, a temporal attention module is learned under dual video-level supervisions on both the salient and the non-salient representations.
Ranked #4 on
Action Recognition
on ActivityNet
Temporal Action Proposal Generation with Background Constraint
To evaluate the confidence of proposals, the existing works typically predict action score of proposals that are supervised by the temporal Intersection-over-Union (tIoU) between proposal and the ground-truth.
Temporal Action Proposal Generation with Transformers
Conventionally, the temporal action proposal generation (TAPG) task is divided into two main sub-tasks: boundary prediction and proposal confidence prediction, which rely on the frame-level dependencies and proposal-level relationships separately.
