OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad

• URL: http://arxiv.org/abs/2503.18695v1
• Date: Mon, 24 Mar 2025 14:04:17 GMT
• Title: OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
• Abstract summary: We propose a novel framework, the Object-Concept-Relation Triad (OCRT), that enables FMs to extract sparse, high-level concepts and intricate relational structures from raw visual inputs.<n>To be specific, we project the object-centric representations onto a semantic concept space that the model can readily interpret and estimate their importance to filter out irrelevant elements.<n>To be specific, OCRT can substantially boost the generalizability and robustness of SAM and CLIP across multiple downstream tasks.
• Score: 22.358084538002327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious attacks in the open world. On the other hand, most domain generalization or adversarial fine-tuning methods are task-related or model-specific, ignoring the universality in practical applications and the transferability between FMs. This paper delves into the problem of generalizing FMs to the out-of-domain data. We propose a novel framework, the Object-Concept-Relation Triad (OCRT), that enables FMs to extract sparse, high-level concepts and intricate relational structures from raw visual inputs. The key idea is to bind objects in visual scenes and a set of object-centric representations through unsupervised decoupling and iterative refinement. To be specific, we project the object-centric representations onto a semantic concept space that the model can readily interpret and estimate their importance to filter out irrelevant elements. Then, a concept-based graph, which has a flexible degree, is constructed to incorporate the set of concepts and their corresponding importance, enabling the extraction of high-order factors from informative concepts and facilitating relational reasoning among these concepts. Extensive experiments demonstrate that OCRT can substantially boost the generalizability and robustness of SAM and CLIP across multiple downstream tasks.

Related papers

• CDFM: Towards a General-Purpose Causal Discovery Foundation Model [62.32138962508216]
  Causal discovery is the process of recovering underlying causal structures from observational data.<n>We formulate the Causal Discovery Foundation Model (CDFM) as a unified, general-purpose framework for zero-shot structural inference.<n>CDFM consistently outperforms traditional algorithms, driving a paradigm shift toward a general-purpose causal discovery foundation model.
  arXiv  Detail & Related papers  (2026-07-13T12:58:28Z)
• Large Vision-Language Models Get Lost in Attention [51.851592109135716]
  We propose a unified framework grounded in information theory and geometry to quantify the geometric and entropic nature of residual updates.<n>Applying this unified framework reveals a fundamental functional decoupling: Attention acts as a subspace-language operator focused on reconfiguration, whereas FFNs serve as subspace-language operators driving semantic innovation.
  arXiv  Detail & Related papers  (2026-05-07T04:45:52Z)
• From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models [29.908678230814015]
  We introduce an iERF-centric framework that unifies local, global, and mechanistic interpretability around a single analysis unit.<n>On the local side, Sharing Ratio Decomposition (SRD) expresses each PFV as a mixture of upstream PFVs via sharing ratios and propagates iERFs to construct class-discriminative saliency maps.<n>For the global view, we introduce Concept-Anchored Feature Explanation (CAFE), which utilizes the iERF as a semantic label.
  arXiv  Detail & Related papers  (2026-05-01T07:25:49Z)
• Perception-Aware Multimodal Spatial Reasoning from Monocular Images [57.42071289037214]
  spatial reasoning from monocular images is essential for autonomous driving.<n>Current Vision-Language Models (VLMs) still struggle with fine-grained geometric perception.<n>We propose a perception-aware multimodal reasoning framework that equipsVLMs with explicit object-centric grounding ability.
  arXiv  Detail & Related papers  (2026-03-07T02:05:12Z)
• Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimality [52.57416398859353]
  We show that causal minimality can endow latent representations of diffusion vision and autoregressive language models with clear causal interpretation and robust, component-wise identifiable control.<n>We introduce a novel theoretical framework for hierarchical selection models, where higher-level concepts emerge from the constrained composition of lower-level variables.<n>These causally grounded concepts serve as levers for fine-grained model steering, paving the way for transparent, reliable systems.
  arXiv  Detail & Related papers  (2025-12-11T14:59:14Z)
• Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation [27.62798534410331]
  We introduce Fine-grained Cross-modal Causal Tracing (FCCT) framework, which systematically quantifies the causal effects on visual object perception.<n>FCCT conducts fine-grained analysis covering the full range of visual and textual tokens, three core model components including multi-head self-attention (MHSA), feed-forward networks (FFNs), and hidden states, across all decoder layers.<n>Our analysis is the first to demonstrate that MHSAs of the last token in middle layers play a critical role in aggregating cross-modal information, while FFNs exhibit a three-stage hierarchical progression for the storage
  arXiv  Detail & Related papers  (2025-11-08T08:37:26Z)
• CIR-CoT: Towards Interpretable Composed Image Retrieval via End-to-End Chain-of-Thought Reasoning [93.05917922306196]
  Composed Image Retrieval (CIR) aims to find a target image from a reference image and a modification text.<n>CIR-CoT is the first end-to-end retrieval-oriented MLLM designed to integrate explicit Chain-of-Thought (CoT) reasoning.
  arXiv  Detail & Related papers  (2025-10-09T09:41:45Z)
• From Segments to Concepts: Interpretable Image Classification via Concept-Guided Segmentation [21.421373394306837]
  Concept Bottleneck Models (CBMs) aim to improve transparency by reasoning through human-interpretable concepts.<n>We propose SEG-MIL-CBM, a novel framework that integrates concept-guided image segmentation into an attention-based multiple instance learning framework.<n>By reasoning over semantically meaningful regions aligned with high-level concepts, our model highlights task-relevant evidence, down-weights irrelevant cues, and produces spatially grounded, concept-level explanations.
  arXiv  Detail & Related papers  (2025-10-05T12:48:43Z)
• Object Centric Concept Bottlenecks [22.074896812195437]
  We introduce Object-Centric Concept Bottlenecks (OCB), a framework that combines the strengths of CBMs and pre-trained object-centric foundation models.<n>We evaluate OCB on complex image datasets and conduct a comprehensive ablation study to analyze key components of the framework.<n>The results show that OCB outperforms traditional CBMs and allows one to make interpretable decisions for complex visual tasks.
  arXiv  Detail & Related papers  (2025-05-30T11:45:05Z)
• Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation [5.326302374594885]
  Foreground segmentation is crucial for scene understanding, yet parameter-efficient fine-tuning (PEFT) of vision foundation models (VFMs) often fails in complex scenarios. We propose Ladder Shape-bias Representation Side-tuning (LSR-ST), a lightweight PEFT framework that enhances model robustness by introducing shape-biased inductive priors.
  arXiv  Detail & Related papers  (2025-04-20T04:12:38Z)
• ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for Enhanced Alignment in Vision Transformers [29.932706137805713]
  ASCENT-ViT is an attention-based, concept learning framework for Vision Transformers (ViTs)<n>It composes scale and position-aware representations from multiscale feature pyramids and ViT patch representations, respectively.<n>It can be utilized as a classification head on top of standard ViT backbones for improved predictive performance and accurate and robust concept explanations.
  arXiv  Detail & Related papers  (2025-01-16T00:45:05Z)
• Coding for Intelligence from the Perspective of Category [66.14012258680992]
  Coding targets compressing and reconstructing data, and intelligence. Recent trends demonstrate the potential homogeneity of these two fields. We propose a novel problem of Coding for Intelligence from the category theory view.
  arXiv  Detail & Related papers  (2024-07-01T07:05:44Z)
• Cantor: Inspiring Multimodal Chain-of-Thought of MLLM [83.6663322930814]
  We argue that converging visual context acquisition and logical reasoning is pivotal for tackling visual reasoning tasks. We propose an innovative multimodal CoT framework, termed Cantor, characterized by a perception-decision architecture. Our experiments demonstrate the efficacy of the proposed framework, showing significant improvements in multimodal CoT performance.
  arXiv  Detail & Related papers  (2024-04-24T17:59:48Z)
• Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement [58.9768112704998]
  Disentangled representation learning strives to extract the intrinsic factors within observed data. We introduce a new perspective and framework, demonstrating that diffusion models with cross-attention can serve as a powerful inductive bias. This is the first work to reveal the potent disentanglement capability of diffusion models with cross-attention, requiring no complex designs.
  arXiv  Detail & Related papers  (2024-02-15T05:07:54Z)
• Concept-Centric Transformers: Enhancing Model Interpretability through Object-Centric Concept Learning within a Shared Global Workspace [1.6574413179773757]
  Concept-Centric Transformers is a simple yet effective configuration of the shared global workspace for interpretability. We show that our model achieves better classification accuracy than all baselines across all problems.
  arXiv  Detail & Related papers  (2023-05-25T06:37:39Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• Robust and Controllable Object-Centric Learning through Energy-based Models [95.68748828339059]
  ours is a conceptually simple and general approach to learning object-centric representations through an energy-based model. We show that ours can be easily integrated into existing architectures and can effectively extract high-quality object-centric representations.
  arXiv  Detail & Related papers  (2022-10-11T15:11:15Z)
• From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation [16.783836191022445]
  The field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models. While local XAI methods explain individual predictions in form of attribution maps, global explanation techniques visualize what concepts a model has generally learned to encode.
  arXiv  Detail & Related papers  (2022-06-07T12:05:58Z)
• Unsupervised Learning of Compositional Energy Concepts [70.11673173291426]
  We propose COMET, which discovers and represents concepts as separate energy functions. Comet represents both global concepts as well as objects under a unified framework.
  arXiv  Detail & Related papers  (2021-11-04T17:46:12Z)
• Entity Concept-enhanced Few-shot Relation Extraction [35.10974511223129]
  Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem. Most existing FSRE algorithms fail to accurately classify the relations merely based on the information of the sentences together with the recognized entity pairs. We propose a novel entity-enhanced FEw-shot Relation Extraction scheme (ConceptFERE), which introduces the inherent concepts of entities to provide clues for relation prediction.
  arXiv  Detail & Related papers  (2021-06-04T10:36:49Z)

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.