mlsanityData to 7 Oct 2026

CoCoG-2: Controllable generation of visual stimuli for understanding human concept representation

arXiv 2407.14949 ↗First posted 20 Jul 2024q-bio.NC cs.CV cs.HCcited by 3

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Abstract (arXiv)

Humans interpret complex visual stimuli using abstract concepts that facilitate decision-making tasks such as food selection and risk avoidance. Similarity judgment tasks are effective for exploring these concepts. However, methods for controllable image generation in concept space are underdeveloped. In this study, we present a novel framework called CoCoG-2, which integrates generated visual stimuli into similarity judgment tasks. CoCoG-2 utilizes a training-free guidance algorithm to enhance generation flexibility. CoCoG-2 framework is versatile for creating experimental stimuli based on human concepts, supporting various strategies for guiding visual stimuli generation, and demonstrating how these stimuli can validate various experimental hypotheses. CoCoG-2 will advance our understanding of the causal relationship between concept representations and behaviors by generating visual stimuli. The code is available at urlhttps://github.com/ncclab-sustech/CoCoG-2.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 28 of its 36 references that are in the map. First posted on arXiv 20 Jul 2024.

Builds on

28 of 36 references.

References · 28

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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16 more
  1. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
  2. 2023Adversarial Diffusion Distillationcited by 741
  3. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  4. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  5. 2023DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Modelscited by 431
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  12. 2024Guidance with Spherical Gaussian Constraint for Conditional Diffusioncited by 69
  13. 2023Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking Oraclescited by 34
  14. 2020Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approachcited by 26
  15. 2023FABRIC: Personalizing Diffusion Models with Iterative Feedbackcited by 19
  16. 2024CoCoG: Controllable Visual Stimuli Generation based on Human Concept Representationscited by 5

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Temporal Alignment Guidance: On-Manifold Sampling in Diffusion Modelscited by 6
  2. 2025Synthesizing Images on Perceptual Boundaries of ANNs for Uncovering Human Perceptual Variability on Facial Expressionscited by 0
  3. 2025Dimensions of Vulnerability in Visual Working Memory: An AI-Driven Approach to Perceptual Comparisoncited by 0