Grouped Discrete Representation Guides Object-Centric Learning

• URL: http://arxiv.org/abs/2407.01726v3
• Date: Thu, 19 Dec 2024 19:01:41 GMT
• Title: Grouped Discrete Representation Guides Object-Centric Learning
• Authors: Rongzhen Zhao, Vivienne Wang, Juho Kannala, Joni Pajarinen,
• Abstract summary: Transformer-based Object-Centric Discrete Learning can abstract dense images or textures into sparse object-level features. We propose textitGrouped Representation (GDR) to address these issues by grouping features into attributes and indexing them with numbers.
• Score: 18.44580501357929
• License:
• Abstract: Similar to humans perceiving visual scenes as objects, Object-Centric Learning (OCL) can abstract dense images or videos into sparse object-level features. Transformer-based OCL handles complex textures well due to the decoding guidance of discrete representation, obtained by discretizing noisy features in image or video feature maps using template features from a codebook. However, treating features as minimal units overlooks their composing attributes, thus impeding model generalization; indexing features with natural numbers loses attribute-level commonalities and characteristics, thus diminishing heuristics for model convergence. We propose \textit{Grouped Discrete Representation} (GDR) to address these issues by grouping features into attributes and indexing them with tuple numbers. In extensive experiments across different query initializations, dataset modalities, and model architectures, GDR consistently improves convergence and generalizability. Visualizations show that our method effectively captures attribute-level information in features. The source code will be available upon acceptance.

Related papers

• Real Classification by Description: Extending CLIP's Limits of Part Attributes Recognition [1.2499537119440243]
  We tackle zero shot "real" classification by description, a novel task that evaluates the ability of Vision-Language Models (VLMs) to classify objects based solely on descriptive attributes, excluding object class names. We release description data for six popular fine-grained benchmarks, which omit object names to encourage genuine zero-shot learning. We introduce a modified CLIP architecture that leverages multiple resolutions to improve the detection of fine-grained part attributes.
  arXiv  Detail & Related papers  (2024-12-18T15:28:08Z)
• LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation [16.864086165056698]
  Existing open-vocabulary approaches leverage vision-language models, such as CLIP, to align visual features with rich semantic features acquired through pre-training on large-scale vision-language datasets. We propose to alleviate the issues by leveraging multiple large-scale models to enhance the alignment between fine-grained visual features and enriched linguistic features. Our method achieves state-of-the-art performance across all major open-vocabulary segmentation benchmarks.
  arXiv  Detail & Related papers  (2024-11-30T05:49:42Z)
• Grouped Discrete Representation for Object-Centric Learning [18.44580501357929]
  We propose textitGroup Discrete Representation (GDR) for Object-Centric Learning. GDR decomposes features into attributes via organized channel grouping, and composes these attributes into discrete representation via indexes.
  arXiv  Detail & Related papers  (2024-11-04T17:25:10Z)
• Organized Grouped Discrete Representation for Object-Centric Learning [18.44580501357929]
  Representative methods suppress pixel-level information redundancy and guide object-level feature aggregation. The most recent advancement, Grouped Discrete Representation (GDR), further decomposes these template features into attributes. We propose Organized GDR (OGDR) to organize channels belonging to the same attributes together for correct decomposition from features into attributes.
  arXiv  Detail & Related papers  (2024-09-05T14:13:05Z)
• Cross-composition Feature Disentanglement for Compositional Zero-shot Learning [49.919635694894204]
  Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL) We propose the solution of cross-composition feature disentanglement, which takes multiple primitive-sharing compositions as inputs and constrains the disentangled primitive features to be general across these compositions.
  arXiv  Detail & Related papers  (2024-08-19T08:23:09Z)
• Attribute-Aware Deep Hashing with Self-Consistency for Large-Scale Fine-Grained Image Retrieval [65.43522019468976]
  We propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes. We develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors. Our models are equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities.
  arXiv  Detail & Related papers  (2023-11-21T08:20:38Z)
• Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification [78.52704557647438]
  We propose a novel FIne-grained Representation and Recomposition (FIRe$2$) framework to tackle both limitations without any auxiliary annotation or data. Experiments demonstrate that FIRe$2$ can achieve state-of-the-art performance on five widely-used cloth-changing person Re-ID benchmarks.
  arXiv  Detail & Related papers  (2023-08-21T12:59:48Z)
• Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning [52.506434446439776]
  Compositional zero-shot learning (CZSL) aims to recognize compositions with prior knowledge of known primitives (attribute and object) We propose a simple and scalable framework called Composition Transformer (CoT) to address these issues. Our method achieves SoTA performance on several benchmarks, including MIT-States, C-GQA, and VAW-CZSL.
  arXiv  Detail & Related papers  (2023-08-08T03:24:21Z)
• Triplet Contrastive Learning for Unsupervised Vehicle Re-identification [55.445358749042384]
  Part feature learning is a critical technology for fine semantic understanding in vehicle re-identification. We propose a novel Triplet Contrastive Learning framework (TCL) which leverages cluster features to bridge the part features and global features.
  arXiv  Detail & Related papers  (2023-01-23T15:52:12Z)
• Learning Invariant Visual Representations for Compositional Zero-Shot Learning [30.472541551048508]
  Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions using knowledge learned from seen-object compositions in the training set. We propose an invariant feature learning framework to align different domains at the representation and gradient levels. Experiments on two CZSL benchmarks demonstrate that the proposed method significantly outperforms the previous state-of-the-art.
  arXiv  Detail & Related papers  (2022-06-01T11:33:33Z)
• Semantic Disentangling Generalized Zero-Shot Learning [50.259058462272435]
  Generalized Zero-Shot Learning (GZSL) aims to recognize images from both seen and unseen categories. In this paper, we propose a novel feature disentangling approach based on an encoder-decoder architecture. The proposed model aims to distill quality semantic-consistent representations that capture intrinsic features of seen images.
  arXiv  Detail & Related papers  (2021-01-20T05:46:21Z)

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.