Finlay G. C. Hudson

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 gallery-gallery feature information to establish semantic links between query and gallery images. This enables the retrieval of relevant images even when they do not exhibit similar visual features but share underlying semantic concepts. This can be combined with any pre-existing cross-domain feature extraction backbone to improve retrieval performance. However, when combined with a carefully chosen Vision Transformer backbone and combination of zero-shot retrieval losses, our approach yields state-of-the-art results on the Sketchy, TU-Berlin and QuickDraw sketch-based retrieval benchmarks. We show that our re-ranking also improves performance with other backbones and outperforms other re-ranking methods applied with our backbone. Importantly, unlike many previous methods, none of the components in our approach are engineered specifically towards the sketch-based image retrieval task - it can be generally applied to any cross-domain, zero-shot retrieval task. We therefore also present new results on zero-shot cartoon-to-photo and art-to-product retrieval using the Office-Home dataset. Project page: finlay-hudson.github.io/icfrr, code available at: github.com/finlay-hudson/ICFRR

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 artificial vision systems struggle with persistent, panoramic understanding, often processing scenes egocentrically on a frame-by-frame basis. This limitation is pronounced in the Track Any Point (TAP) task, where existing methods fail to track 2D points outside the field of view. To address this, we introduce TAPVid-360, a novel task that requires predicting the 3D direction to queried scene points across a video sequence, even when far outside the narrow field of view of the observed video. This task fosters learning allocentric scene representations without needing dynamic 4D ground truth scene models for training. Instead, we exploit 360 videos as a source of supervision, resampling them into narrow field-of-view perspectives while computing ground truth directions by tracking points across the full panorama using a 2D pipeline. We introduce a new dataset and benchmark, TAPVid360-10k comprising 10k perspective videos with ground truth directional point tracking. Our baseline adapts CoTracker v3 to predict per-point rotations for direction updates, outperforming existing TAP and TAPVid 3D methods. Project page: https://finlay-hudson.github.io/tapvid360

Finlay G. C. Hudson, William A. P. Smith

We present Track Anything Behind Everything (TABE), a novel dataset, pipeline, and evaluation framework for zero-shot amodal completion from visible masks. Unlike existing methods that require pretrained class labels, our approach uses a single query mask from the first frame where the object is visible, enabling flexible, zero-shot inference. Our dataset, TABE-51 provides highly accurate ground truth amodal segmentation masks without the need for human estimation or 3D reconstruction. Our TABE pipeline is specifically designed to handle amodal completion, even in scenarios where objects are completely occluded. We also introduce a specialised evaluation framework that isolates amodal completion performance, free from the influence of traditional visual segmentation metrics.