The Prism Hypothesis: Harmonizing Semantic and Pixel Representations via Unified Autoencoding
This work addresses the challenge of unifying abstract meaning and fine-grained details in deep representations for computer vision applications, offering a novel perspective but with incremental methodological contributions.
The paper tackled the problem of harmonizing semantic and pixel representations by analyzing encoder spectral characteristics, proposing the Prism Hypothesis and Unified Autoencoding (UAE) model, which achieved state-of-the-art performance on ImageNet and MS-COCO benchmarks.
Deep representations across modalities are inherently intertwined. In this paper, we systematically analyze the spectral characteristics of various semantic and pixel encoders. Interestingly, our study uncovers a highly inspiring and rarely explored correspondence between an encoder's feature spectrum and its functional role: semantic encoders primarily capture low-frequency components that encode abstract meaning, whereas pixel encoders additionally retain high-frequency information that conveys fine-grained detail. This heuristic finding offers a unifying perspective that ties encoder behavior to its underlying spectral structure. We define it as the Prism Hypothesis, where each data modality can be viewed as a projection of the natural world onto a shared feature spectrum, just like the prism. Building on this insight, we propose Unified Autoencoding (UAE), a model that harmonizes semantic structure and pixel details via an innovative frequency-band modulator, enabling their seamless coexistence. Extensive experiments on ImageNet and MS-COCO benchmarks validate that our UAE effectively unifies semantic abstraction and pixel-level fidelity into a single latent space with state-of-the-art performance.