Towards Improving the Generation Quality of Autoregressive Slot VAEs

• URL: http://arxiv.org/abs/2206.01370v3
• Date: Tue, 28 Nov 2023 01:01:54 GMT
• Title: Towards Improving the Generation Quality of Autoregressive Slot VAEs
• Abstract summary: 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.
• Score: 16.079315268039934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (''slots'') from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multi-object relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to 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. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multi-object environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

Related papers

• Prompting Diffusion Models for Zero-Shot Instance Segmentation [74.37083707535778]
  We present Prompt2Seg, a spatial conditioning framework for diffusion-based segmentation.<n>Our approach takes spatial prompts, represented as 2D Gaussians or confidence maps, as explicit input signals, training the model to respond directly to user intent.<n>We evaluate on seven datasets ranging from standard benchmarks to more challenging domains, including paintings, egocentric views, and X-ray data.
  arXiv  Detail & Related papers  (2026-06-21T20:30:41Z)
• PLACID: Identity-Preserving Multi-Object Compositing via Video Diffusion with Synthetic Trajectories [22.63777279327245]
  PLACID is a framework that transforms a collection of object images into an appealing multi-object composite.<n>First, we leverage a pretrained image-to-video (I2V) diffusion model with text control to preserve objects consistency, identities, and background details.<n>Second, we propose a novel data curation strategy that generates synthetic sequences where randomly placed objects smoothly move to their target positions.
  arXiv  Detail & Related papers  (2026-01-30T19:42:54Z)
• Intent-aware Diffusion with Contrastive Learning for Sequential Recommendation [0.552480439325792]
  We propose Intent-aware Diffusion with contrastive learning for sequential Recommendation (InDiRec) InDiRec first performs intent clustering on sequence representations using K-means to build intent-guided signals. It retrieves the intent representation of the target interaction sequence to guide a conditional diffusion model, generating positive views that share the same underlying intent.
  arXiv  Detail & Related papers  (2025-04-22T17:55:56Z)
• Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion [57.232688209606515]
  We present HTCL, a novel Temporal Temporal Context Learning paradigm for improving camera-based semantic scene completion. Our method ranks $1st$ on the Semantic KITTI benchmark and even surpasses LiDAR-based methods in terms of mIoU.
  arXiv  Detail & Related papers  (2024-07-02T09:11:17Z)
• 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)
• Diffusion-based Contrastive Learning for Sequential Recommendation [6.3482831836623355]
  We propose a Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. CaDiRec employs a context-aware diffusion model to generate alternative items for the given positions within a sequence. We train the entire framework in an end-to-end manner, with shared item embeddings between the diffusion model and the recommendation model.
  arXiv  Detail & Related papers  (2024-05-15T14:20:37Z)
• Enhancing Interpretable Object Abstraction via Clustering-based Slot Initialization [17.25953277219166]
  We present a new method for object-centric representations using slots. Our method outperforms prior works consistently. We evaluate our method on object discovery and novel view synthesis tasks with various datasets.
  arXiv  Detail & Related papers  (2023-08-22T11:48:43Z)
• Contrastive Training of Complex-Valued Autoencoders for Object Discovery [55.280789409319716]
  We introduce architectural modifications and a novel contrastive learning method that greatly improve the state-of-the-art synchrony-based model. For the first time, we obtain a class of synchrony-based models capable of discovering objects in an unsupervised manner in multi-object color datasets.
  arXiv  Detail & Related papers  (2023-05-24T10:37:43Z)
• ReVersion: Diffusion-Based Relation Inversion from Images [31.61407278439991]
  We propose ReVersion for the Relation Inversion task, which aims to learn a specific relation from exemplar images. We learn a relation prompt from a frozen pre-trained text-to-image diffusion model. The learned relation prompt can then be applied to generate relation-specific images with new objects, backgrounds, and styles.
  arXiv  Detail & Related papers  (2023-03-23T17:56:10Z)
• Unsupervised Object Learning via Common Fate [61.14802390241075]
  Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos.
  arXiv  Detail & Related papers  (2021-10-13T08:22:04Z)
• Object-aware Contrastive Learning for Debiased Scene Representation [74.30741492814327]
  We develop a novel object-aware contrastive learning framework that localizes objects in a self-supervised manner. We also introduce two data augmentations based on ContraCAM, object-aware random crop and background mixup, which reduce contextual and background biases during contrastive self-supervised learning.
  arXiv  Detail & Related papers  (2021-07-30T19:24:07Z)
• Dependent Multi-Task Learning with Causal Intervention for Image Captioning [10.6405791176668]
  In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI) Firstly, we involve an intermediate task, bag-of-categories generation, before the final task, image captioning. Secondly, we apply Pearl's do-calculus on the model, cutting off the link between the visual features and possible confounders. Finally, we use a multi-agent reinforcement learning strategy to enable end-to-end training and reduce the inter-task error accumulations.
  arXiv  Detail & Related papers  (2021-05-18T14:57:33Z)
• Object-Centric Image Generation from Layouts [93.10217725729468]
  We develop a layout-to-image-generation method to generate complex scenes with multiple objects. Our method learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We introduce SceneFID, an object-centric adaptation of the popular Fr'echet Inception Distance metric, that is better suited for multi-object images.
  arXiv  Detail & Related papers  (2020-03-16T21:40:09Z)

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.