Weakly supervised concept Bottleneck Learning for Robust Two stage Object centric visual reasoning

• URL: http://arxiv.org/abs/2608.22584v1
• Date: Sun, 23 Aug 2026 20:39:49 GMT
• Title: Weakly supervised concept Bottleneck Learning for Robust Two stage Object centric visual reasoning
• Abstract summary: Two-stage neuro-symbolic architectures provide an elegant paradigm for visual problem solving.<n>We introduce the Dynamic Orthogonal Concept Bottleneck (D-OCB), an object-centric slot- VAE framework.
• Score: 3.6959117433332724
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Two-stage neuro-symbolic architectures provide an elegant paradigm for visual problem solving by cleanly separating connectionist perception of predefined symbols from possibly later defined relational reasoning thereon. However, anchoring high-level predicates into visual frames typically necessitates annotations that are expensive to acquire. In this work, we introduce the Dynamic Orthogonal Concept Bottleneck (D-OCB), an object-centric slot- VAE framework designed to extract human-aligned symbolic predicates under extremely weak supervision. D-OCB eliminates the arduous manual tuning of loss-balancing coef- ficients by dynamically learning optimal hyperparameter allocations during training. To infuse prior knowledge on independence of concept categories, in addition to standard re- construction self-supervision we penalize correlation across concept subspaces. Crucially, to combat the instability of very low supervision regimes, D-OCB incorporates a dynamic di- mensionality allocation mechanism; this adaptive formulation allows well-represented con- cepts to yield latent dimensions to underperforming concepts that are lagging behind, effectively preventing representation collapse and significantly improving overall concept accuracy. Through an extensive empirical evaluation, we demonstrate that our framework achieves high concept alignment and downstream visual reasoning accuracy using minimal label budgets, matching or outperforming end-to-end paradigms.

Related papers

• Interpreting Object-Dependent Concept Brittleness in Text-to-Image Diffusion Models [105.53190946111586]
  We term this phenomenon object-dependent concept brittleness.<n>We present an interpretability-oriented framework to audit and minimally correct these failures.<n>We evaluate the proposed framework on style and attribute failure cases across multiple diffusion backbones.
  arXiv  Detail & Related papers  (2026-09-09T09:07:40Z)
• Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model [73.59026525350808]
  We propose a dual adversarial fine-tuning framework that jointly optimize visual and semantic supervision signals from two modalities.<n>Our approach outperforms the state-of-the-art method in adversarial robustness evaluation across zero-shot classification, image captioning, and visual question answering tasks.
  arXiv  Detail & Related papers  (2026-07-21T10:49:29Z)
• When Interpretability Becomes a Liability: Adversarial Attacks on CBM Concept Layers [0.0]
  Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning.<n>We present a comprehensive, systematic study of concept-level adversarial vulnerabilities in CBMs.<n>We introduce SPECTRA, a stability principled regularization defense.
  arXiv  Detail & Related papers  (2026-05-25T00:03:47Z)
• Hyperbolic Concept Bottleneck Models [17.962222104908648]
  Concept Bottleneck Models (CBMs) have become a popular approach to enable interpretability in neural networks by constraining inputs to a set of human-understandable concepts.<n>We propose Hyperbolic Concept Bottleneck Models (HypCBM), a post-hoc framework that grounds the bottleneck in this structure by reformulating concept activation as asymmetric geometric containment in hyperbolic space.<n>We show they encode a natural test-time activation signal: the margin of inclusion within a concept's entailment cone yields sparse, hierarchy-aware activations without any additional supervision or learned modules.
  arXiv  Detail & Related papers  (2026-05-07T15:41:22Z)
• Rethinking Concept Bottleneck Models: From Pitfalls to Solutions [53.84388497227224]
  Concept Bottleneck Models (CBMs) ground predictions in human-understandable concepts.<n>CBM-Suite is a methodological framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-05T19:37:49Z)
• Understanding Degradation with Vision Language Model [56.09241449206817]
  Understanding visual degradations is a critical yet challenging problem in computer vision.<n>We introduce DU-VLM, a multimodal chain-of-thought model trained with supervised fine-tuning and reinforcement learning.<n>We also introduce textbfDU-110k, a large-scale dataset comprising 110,000 clean-degraded pairs with grounded physical annotations.
  arXiv  Detail & Related papers  (2026-02-04T13:51:15Z)
• Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding [54.05243949024302]
  Existing robust MLLMs rely on implicit training/adaptation that focuses solely on visual encoder generalization.<n>We propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains.<n>Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity.
  arXiv  Detail & Related papers  (2025-12-19T12:56:17Z)
• Towards more holistic interpretability: A lightweight disentangled Concept Bottleneck Model [5.700536552863068]
  Concept Bottleneck Models (CBMs) enhance interpretability by predicting human-understandable concepts as intermediate representations.<n>We propose a lightweight Disentangled Concept Bottleneck Model (LDCBM) that automatically groups visual features into semantically meaningful components.<n> Experiments on three diverse datasets demonstrate that LDCBM achieves higher concept and class accuracy, outperforming previous CBMs in both interpretability and classification performance.
  arXiv  Detail & Related papers  (2025-10-17T15:59:30Z)
• Interpretable Few-Shot Image Classification via Prototypical Concept-Guided Mixture of LoRA Experts [79.18608192761512]
  Self-Explainable Models (SEMs) rely on Prototypical Concept Learning (PCL) to enable their visual recognition processes more interpretable.<n>We propose a Few-Shot Prototypical Concept Classification framework that mitigates two key challenges under low-data regimes: parametric imbalance and representation misalignment.<n>Our approach consistently outperforms existing SEMs by a notable margin, with 4.2%-8.7% relative gains in 5-way 5-shot classification.
  arXiv  Detail & Related papers  (2025-06-05T06:39:43Z)
• "Principal Components" Enable A New Language of Images [79.45806370905775]
  We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space.<n>Our approach achieves state-of-the-art reconstruction performance and enables better interpretability to align with the human vision system.
  arXiv  Detail & Related papers  (2025-03-11T17:59:41Z)
• Neural Concept Binder [22.074896812195437]
  We introduce the Neural Concept Binder (NCB), a framework for deriving both discrete and continuous concept representations. The structured nature of NCB's concept representations allows for intuitive inspection and the straightforward integration of external knowledge. We validate the effectiveness of NCB through evaluations on our newly introduced CLEVR-Sudoku dataset.
  arXiv  Detail & Related papers  (2024-06-14T11:52:09Z)

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.