International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2026, pp.1-6
Publisher
IEEE
Language
English
Type
Conference Paper
Abstract
Generative concept bottleneck models aim to enable interpretable generation by routing synthesis through explicit, user-facing concepts. In practice, prior approaches often rely on non-explicit bottleneck representations (e.g., vision cues or opaque concept embeddings) or black-box decoders to preserve image quality, which weakens the transparency. We propose CoBELa (Concept Bottlenecks on Energy Landscapes), a decoder-free, energy-based framework that eliminates non-explicit bottleneck representations by conditioning generation entirely through per-concept energy functions over the latent space of a frozen pretrained generator—requiring no generator retraining and enabling post-hoc interpretation. Because these concept energies compose additively, CoBELa naturally supports compositional concept interventions: concept conjunction and negation are realized by summing or subtracting per-concept energy terms without additional training. A diffusion-scheduled energy guidance scheme further replaces expensive MCMC chains with more stable, scheduled denoising for efficient concept-steered sampling. Experiments on CelebA-HQ and CUB-200-2011 demonstrate improvements over prior concept bottleneck generative models, achieving 75.70%/82.42% concept accuracy and 6.47/5.37 FID, respectively, while enabling reliable multi-concept interventions.
Black box, Energy Function, Energy-based models, Image Quality, Image synthesis, Latent space, energy landscape, generative model, multi-concept
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