5 papers · 1 filter
Real-VAS: a Real-World Video Amodal Segmentation Dataset
Finlay G. C. Hudson, William A. P. Smith
Amodal video object segmentation is fundamentally limited by the absence of datasets that combine real-world complexity with precise ground-truth annotations. To address this, we p…
TAP-Out: Tracking Any Point in 360° via Outpainting
Finlay G. C. Hudson, James A. D. Gardner, William A. P. Smith
Tracking points that leave the camera's field of view (FoV) requires reasoning about unobserved scene dynamics, a fundamentally different problem from tracking visible corresponden…
TAPVid-360: Tracking Any Point in 360 from Narrow Field of View Video
Finlay G. C. Hudson, James A. D. Gardner, William A. P. Smith
Humans excel at constructing panoramic mental models of their surroundings, maintaining object permanence and inferring scene structure beyond visible regions. In contrast, current…
Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation
Finlay G. C. Hudson, William A. P. Smith
We present Track Anything Behind Everything (TABE), a novel pipeline for zero-shot amodal video object segmentation. Unlike existing methods that require pretrained class labels, o…
If At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval
Finlay G. C. Hudson, William A. P. Smith
In this paper, we introduce a novel method for zero-shot, cross-domain image retrieval. Our key contribution is a test-time Iterative Cluster-free Re-ranking process that leverages…