Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning

• URL: http://arxiv.org/abs/2603.22758v1
• Date: Tue, 24 Mar 2026 03:34:51 GMT
• Title: Reconstruction-Guided Slot Curriculum: Addressing Object Over-Fragmentation in Video Object-Centric Learning
• Abstract summary: Video Object-Centric Learning seeks to decompose raw videos into a small set of object slots.<n>Existing slot models often suffer from severe overfragmentation.<n>We tackle this limitation with a reconstruction-guided slot curriculum.
• Score: 45.1920794546889
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Video Object-Centric Learning seeks to decompose raw videos into a small set of object slots, but existing slot-attention models often suffer from severe over-fragmentation. This is because the model is implicitly encouraged to occupy all slots to minimize the reconstruction objective, thereby representing a single object with multiple redundant slots. We tackle this limitation with a reconstruction-guided slot curriculum (SlotCurri). Training starts with only a few coarse slots and progressively allocates new slots where reconstruction error remains high, thus expanding capacity only where it is needed and preventing fragmentation from the outset. Yet, during slot expansion, meaningful sub-parts can emerge only if coarse-level semantics are already well separated; however, with a small initial slot budget and an MSE objective, semantic boundaries remain blurry. Therefore, we augment MSE with a structure-aware loss that preserves local contrast and edge information to encourage each slot to sharpen its semantic boundaries. Lastly, we propose a cyclic inference that rolls slots forward and then backward through the frame sequence, producing temporally consistent object representations even in the earliest frames. All combined, SlotCurri addresses object over-fragmentation by allocating representational capacity where reconstruction fails, further enhanced by structural cues and cyclic inference. Notable FG-ARI gains of +6.8 on YouTube-VIS and +8.3 on MOVi-C validate the effectiveness of SlotCurri. Our code is available at github.com/wjun0830/SlotCurri.

Related papers

• Semantic Slots for Video Object-Centric Learning [8.62968609670716]
  We show that traditional decoders force slots to be spatially anchored, hindering their ability to adapt to motion.<n>We propose SemanticSlots, which uses a Transformer-based decoder that leverages image context.<n>Results on YouTube-VIS show that SemanticSlots improves upon VideoSAUR by 31 points in mBO and outperforms current state-of-the-art methods by 21 points.
  arXiv  Detail & Related papers  (2026-08-21T21:12:50Z)
• MeshLoom: Feed-Forward Non-Rigid Registration of Mesh Sequences [71.45325481648995]
  MeshLoom is a feed-forward registration network that reconstructs deformations across mesh sequences.<n>We show that MeshLoom achieves state-of-the-art results on non-rigid registration.
  arXiv  Detail & Related papers  (2026-06-15T17:51:38Z)
• Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning [17.16289725621027]
  Unsupervised video object-centric learning aims to decompose dynamic scenes into persistent, object-level representations without supervision.<n>Existing slot-based methods struggle to maintain stable object identity in challenging settings such as rapid motion and partial occlusion.<n>We propose Dual-State Slot Attention (DSSA), a fully self-supervised framework that separates appearance from identity and reduces spurious updates from weakly matching slots.
  arXiv  Detail & Related papers  (2026-06-10T19:00:47Z)
• Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization [34.98521123729049]
  Slot-Slot Contrastive (SSC) loss has become the cornerstone for state-of-the-art (SOTA) video Object-Centric Learning (OCL) methods.<n>We propose a paradigm shift: temporal consistency is better enforced as an implicit model design rather than an explicit loss.
  arXiv  Detail & Related papers  (2026-05-29T16:28:21Z)
• SoLAR: Error-Resilient Streamable Long-Horizon Free-Viewpoint Video Reconstruction with Anchor Activation and Latent Recalibration [57.159190580279585]
  Motivated by bit allocation theory, we analyze dynamic-anchor-based volumetric video representation within a rate-distortion optimization framework.<n>We propose textbfSoLAR, which is the first error-resilient streamable FVV framework that maintains stable reconstruction quality on long sequences.
  arXiv  Detail & Related papers  (2026-05-08T06:48:59Z)
• Improved Object-Centric Diffusion Learning with Registers and Contrastive Alignment [83.56510119503265]
  Slot Attention (SA) with pretrained diffusion models has recently shown promise for object-centric learning (OCL), but suffers from slot entanglement and weak alignment between object slots and image content.<n>We propose Contrastive Object-centric Diffusion Alignment (CODA), a simple extension that (i) employs register slots to absorb residual attention and reduce interference between object slots, and (ii) applies a contrastive alignment loss to explicitly encourage slot-image correspondence.
  arXiv  Detail & Related papers  (2026-01-03T16:10:18Z)
• OnlineSplatter: Pose-Free Online 3D Reconstruction for Free-Moving Objects [58.38338242973447]
  OnlineSplatter is a novel framework generating high-quality, object-centric 3D Gaussians directly from RGB frames.<n>Our approach anchors reconstruction using the first frame and progressively refines the object representation through a dense Gaussian primitive field.<n>Our core contribution is a dual-key memory module combining latent appearance-geometry keys with explicit directional keys.
  arXiv  Detail & Related papers  (2025-10-23T14:37:25Z)
• Slot Attention with Re-Initialization and Self-Distillation [33.38373596185185]
  We propose Slot Attention with re-Initialization and self-Distillation (DIAS) for object discovery and recognition.<n>DIAS achieves state-of-the-art on OCL tasks like object discovery and recognition, while also improving advanced visual prediction and reasoning.
  arXiv  Detail & Related papers  (2025-07-31T17:41:18Z)
• Adaptive Slot Attention: Object Discovery with Dynamic Slot Number [64.45419820717754]
  A major drawback of most object-centric models, including slot attention, is their reliance on predefining the number of slots. Within this framework, we introduce an adaptive slot attention (AdaSlot) mechanism that dynamically determines the optimal number of slots. Our framework, tested extensively on object discovery tasks with various datasets, shows performance matching or exceeding top fixed-slot models.
  arXiv  Detail & Related papers  (2024-06-13T14:55:11Z)
• Towards Improving the Generation Quality of Autoregressive Slot VAEs [16.079315268039934]
  We propose two improvements that strengthen object correlation learning. First, we condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects.
  arXiv  Detail & Related papers  (2022-06-03T02:41:59Z)
• Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging [117.73967303377381]
  We propose a novel self-supervised Video Object (VOS) approach that strives to achieve better object-background discriminability. Our approach is based on a discriminative learning loss formulation that takes into account both object and background information. Our proposed approach, CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS.
  arXiv  Detail & Related papers  (2022-04-22T17:53:27Z)
• Test-time Adaptation with Slot-Centric Models [63.981055778098444]
  Slot-TTA is a semi-supervised scene decomposition model that at test time is adapted per scene through gradient descent on reconstruction or cross-view synthesis objectives. We show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors, and alternative test-time adaptation methods.
  arXiv  Detail & Related papers  (2022-03-21T17:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.