Existing video object segmentation (VOS) benchmarks focus on short-term videos which just
last about 3-5 seconds and where objects are visible most of the time. These videos are …

Visual object tracking aims to localize the target object of each frame based on its initial
appearance in the first frame. Depending on the input modility tracking tasks can be divided …

Semi-supervised video object segmentation is the task of segmenting the target in
sequential frames given the ground truth mask in the first frame. The modern approaches …

Abstract Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which are
crafted by adding human-imperceptible perturbations to the benign inputs. Simultaneously …

Reference features from a template or historical frames are crucial for visual object tracking.
Prior works utilize all features from a fixed template or memory for visual object tracking …

Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect,
segment, and track arbitrary object categories in a video, without being constrained to …

Unsupervised video object segmentation (UVOS) aims at detecting the primary objects in a
given video sequence without any human interposing. Most existing methods rely on two …

Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This
enriched data representation improves performance across various tasks. Existing methods …

Video object segmentation (VOS) aims to distinguish and track target objects in a video.
Despite the excellent performance achieved by off-the-shell VOS models, existing VOS …

Recent studies indicate that sparse attacks threaten the security of deep learning models,
which modify only a small set of pixels in the input based on the l0 norm constraint. While …