VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization

Seul-Ki Yeom; Marcel Simon; Eunbin Lee; Tae-Ho Kim

cs.CVMar 7, 2026

VINO: Video-driven Invariance for Non-contextual Objects via Structural Prior Guided De-contextualization

Authors: Seul-Ki Yeom, Marcel Simon, Eunbin Lee, Tae-Ho Kim

Organizations: Nota AI GmbH

Abstract

Self-supervised learning (SSL) has made rapid progress, yet learned features often over-rely on contextual shortcuts-background textures and co-occurrence statistics. While video provides rich temporal variation, dense in-the-wild streams with strong ego-motion create a co-occurrence trap: foreground objects and background context move coherently, encouraging representations to collapse into scene encoders. To address this, we propose VINO (Video-driven Invariance for Non-Contextual Objects), a teacher-student framework that learns robust image encoders from dense video by imposing a structural information bottleneck. Using a class-agnostic structural prior solely to generate views-not as semantic pseudo-labels-VINO forms an asymmetric distillation problem. The teacher predicts from a foreground-union view with the background suppressed, while the student observes object-conditioned scene views that retain surrounding context but remove competing instances. Matching these targets via masked distillation makes background cues unreliable, pushing the representation toward object-centric invariances. We further enforce temporal object permanence via teacher-anchored cross-time distillation over track-matched objects, and stabilize part-to-whole consistency with mask-guided local views. Through attention visualization and unsupervised object discovery on PASCAL VOC, we demonstrate that VINO effectively disentangles foreground from background. Pretrained on the dense Walking Tours Venice video, VINO achieves 34.8 CorLoc, yielding highly focused, shape-biased representations that substantially outperform prior dense-video and motion-guided SSL baselines.

Figures & tables

Figure 1: Attention maps from ViT-S/16 encoders. We visualize attention maps by ViT-S/16 encoders for the same inputs, comparing DINO trained on ImageNet, DINO trained on WT-Venice, DoRA, and VINO. (a) shows results on single static natural images from PASCAL VOC 2012 [ 12 ] . (b) shows results on Physical AI video sequences from Mobile ALOHA [ 14 ] dataset, where attention is visualized across multiple frames within each sequence.

Figure 2: Our framework learns object-centric representations from dense video by enforcing a structural information bottleneck . (A) The Teacher observes a foreground-union global view where background is suppressed, providing a de-contextualized target. (B) The Student receives object-conditioned views that retain background but remove co-occurring objects using a structural prior. (C) This asymmetric distillation makes background and co-occurrence shortcuts non-predictive, pushing representations toward object-intrinsic cues while retaining robustness to natural context. The total objective $\mathcal{L}_{\text{total}}$ ensures spatial de-contextualization ( $\mathcal{L}_{\text{mask}}$ ), temporal object permanence ( $\mathcal{L}_{\text{temp}}$ ), and part-to-whole consistency ( $\mathcal{L}_{\text{local}}$ ).

Dataset	Modality	Curation	Spatial Diversity	FG Sparsity	Ego- motion	Temporal Coupling	Shortcut Risk
ImageNet [ 8 ]	Image	Curated	Medium	Low	None	None	Dataset bias
LVD-142M [ 32 ]	Image	Curated (Web)	Very High	Low– Med	None	None	Statistical shortcut
LVD-1689M [ 36 ]	Image	Curated (Billion)	Extremely High	Low– Med	None	None	Statistical shortcut
Kinetics-400 [ 22 ]	Video	Semi- curated	Medium	Medium	Moderate	Medium	Scene bias
EPIC-Kitchens [ 7 ]	Video	Egocentric	Medium	Medium	High	High	Motion ambiguity
BDD-100K [ 49 ]	Video	In-the-wild	Low	High	High	Very High	Structural entanglement

Table 1: Structural properties of pretraining corpora. Static curated image corpora ( e.g . , DINOv2 / DINOv3 ) can dilute object–context correlations statistically via large spatial diversity, whereas dense ego-motion videos exhibit strong temporal coupling and foreground sparsity, turning temporal predictability into contextual shortcuts.

Figure 3: Unsupervised object discovery on PASCAL VOC 2012. We visualize the predicted object bounding boxes obtained from attention based foreground masks following the default LOST [ 35 ] . We compared results on ViT-S/16 encoders for the same inputs, comparing DINO trained on ImageNet, DINO trained on WT-Venice, DoRA, and VINO. Compared to baselines, VINO produces tighter boxes that better align with the principal object extent and is less prone to drifting toward large background regions, highlighting improved figure–ground separation under dense ego-motion pretraining.

Method	Epochs	Pre-train	Object Discovery
Method	Epochs	Pre-train	$\mathrm{CorLoc}\uparrow$
DoRA [ 39 ]	100	WT-All	33.7
PooDLe [ 40 ]	100	WT-Venice	22.6
DINO [ 4 ]	100	WT-Venice	24.8
DINOv2 [ 32 ]	100	WT-Venice	27.5
iBOT [ 52 ]	100	WT-Venice	33.9

Table 2: Object discovery. We report unsupervised object discovery performance on PASCAL VOC 2012 [ 12 ] measured by CorLoc. All methods use ViT-S/16 pretrained on the Walking Tours dataset.

Self-Supervised Visual Representation Learning

Self-supervised visual representation learning trains image, video, and 3D encoders on unlabeled data to produce transferable visual features.

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Selective Synergistic Learning for Video Object-Centric Learning

WonJun Moon, Jae-Pil Heo

KAIST, South Korea · † Work done at Sungkyunkwan University. · Sungkyunkwan University, South Korea

Object-Centric Representation Learning Video Object-Centric Learning Video Representation Learning

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Typical video object-centric learning (VOCL) approaches employ slot-based frameworks that rely on reconstruction-driven encoder-decoder architectures, where learning is mediated by two spatial maps: attention maps from the encoder and object maps from the decoder. As these two distinct maps exhibit different properties, a recent dense alignment strategy attempted to reconcile this discrepancy by enforcing agreement across all spatio-temporal patches via contrastive learning. However, this indiscriminate alignment inadvertently propagates the inherent weaknesses of each module, such as noisy encoder predictions and blurred decoder boundaries. Moreover, computing dense similarities across all pairs incurs a computational cost quadratic in the total number of spatio-temporal patches, severely limiting scalability. Motivated by this, we propose Selective Synergistic Learning (SSync). Instead of exhaustive patch-to-patch alignment, SSync prevents error propagation by selectively distilling only the most reliable cues: leveraging the encoder strictly for boundary refinement and the decoder for interior denoising. This is realized via a pseudo-labeling with linear complexity, eliminating the need for quadratic spatial comparisons. Also, to prevent the reinforcement of architectural biases like slot redundancy, we introduce a transitive pseudo-label merging that consolidates overlapping slots based on spatio-temporal activation consistency. Extensive studies demonstrate that SSync improves decomposition quality and serves as a versatile, plug-and-play module while also exhibiting exceptional robustness to slot configurations. Code is available at github.com/wjun0830/SSync.

Abstract

Self-Supervised Visual Representation Learning

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