Papers › Learning Object Permanence from Video

Learning Object Permanence from Video

23 Mar 2020ECCV 2020 8arXiv:2003.10469archive 2025-07-28

Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik

Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensive studies in developmental psychology suggest that object permanence is a challenging task that is learned through extensive experience. Here we introduce the setup of learning Object Permanence from data. We explain why this learning problem should be dissected into four components, where objects are (1) visible, (2) occluded, (3) contained by another object and (4) carried by a containing object. The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, and find that it outperforms previous localization methods and various baselines.

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ofrikleinfeld/ObjectPermanence mentioned on GitHubpytorchMIT report

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cal_map ofrikleinfeld/ObjectPermanence/baselines/analyze_iou_offline.py community (archive-listed) unverified MIT (permissive) · ab01d8783cda41bf · report
get_fast_rcnn_for_fine_tune ofrikleinfeld/ObjectPermanence/object_detection/models.py community (archive-listed) unverified MIT (permissive) · 88baabe6197d6747 · report
is_cone_object ofrikleinfeld/ObjectPermanence/object_indices.py community (archive-listed) unverified MIT (permissive) · 8a782e50e07f6f13 · report
transform_xyxy_to_w_h ofrikleinfeld/ObjectPermanence/baselines/cater_setup_inference.py community (archive-listed) unverified MIT (permissive) · 20ce79aa95b4b50c · report

Tasks

ObjectVideo Object Tracking

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Video Object Tracking CATER OPNet L1 0.54 #3 of 7 Archive leaderboard report
Video Object Tracking CATER OPNet Top 1 Accuracy 74.8 #3 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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