#01Nov 12, 2026
cs.CV
MuViRL: Multimodal View-Invariant Visual Speech Representation Learning via Calibrated View Aggregation
Fei Su, Kai Qiu, Juan Liu
Multimodal representation learning combines complementary signals to capture shared semantic information. For dynamic scenes, these representations need to remain consistent across views while preserving distinctions between temporal states. In visual speech representation learning, view variation changes the visibility and reliability of articulation cues, making it difficult to learn such representations from synchronized audio and video. To address this challenge, we propose MuViRL, a self-supervised framework for multimodal view-invariant visual speech representation learning via calibrated view aggregation. The framework combines synchronized 3D Gaussian view synthesis with multimodal self-supervision to learn consistent representations from real and synthesized data. A multimodal decision model learns relative view utility from teacher prediction losses, and its calibrated scores guide the aggregation of complementary information. Aggregation-to-Single Distillation transfers the aggregated predictions to a single branch. Real-synthetic alignment matches synchronized windows across views while preserving their temporal relations. Experiments on real and synthesized multiview data demonstrate that MuViRL improves multimodal representation consistency and visual speech recognition performance across views.
#02Nov 12, 2026
cs.CV
When Chekhov's Gun Fires Anyway: Over-Realization in Text-to-Image Models
Jiahao Huang, Kon Woo Kim, Song-Duo Ma and 12 more
In text-to-image generation, a target that the prompt mentions often appears in the generated image in a way the prompt does not call for. We call this failure over-realization. Mental-spaces theory points to the situations where it can happen: under a negation, inside what a character believes or imagines, as the vehicle of a figurative comparison, or behind a viewpoint that hides the target. Prior work has studied only negation in depth, and the conventional alignment and preference metrics do not penalize the failure. We build OverReal to measure it: OverReal-Gen, 698 prompts covering the four situations, on which we measure each generator's over-realization rate; and OverReal-Det, 2,841 generated images human-labeled for whether and how the target is over-realized, on which we verify the automatic judge that produces this rate. Our evaluation of eight text-to-image systems reveals that all of them over-realize, and that conventional metrics such as CLIPScore reward the failure rather than penalize it. Prompt expansion only partly mitigates the failure. Inside the generator, the diffusion backbone attends to a mentioned target almost as much as to a requested one. The cue words that should hold the target back receive less attention than ordinary words.
#03Nov 12, 2026
cs.CV
Learning the Missing Derivative: Neural Multiple-Scattering Operators for Inverse Volume Rendering
Sipeng Chen, Shibo Li
Reconstructing optically thick media from photographs requires modeling multiple scattering and its response to changes in extinction. Frozen-source tomography discards this derivative; differentiable path tracing estimates it through expensive, noisy sampling. We instead predict the multiple-scattering field with a neural operator and differentiate through it during inverse rendering, while computing transmittance and single scattering directly. A further challenge arises during optimization: an operator that predicts the field accurately on held-out media can still produce poor derivatives at the density states visited by the optimizer. We address this by fine-tuning on states generated during inversions driven by the operator, using Monte Carlo scattering-field supervision. On held-out optically thick clouds, our method improves relighting over frozen-source tomography at equal time and outperforms differentiable path tracing given substantially more time. Training on inversion trajectories also improves derivative alignment at visited states and reduces optimization drift.
#04Nov 12, 2026
cs.CY
From State of THE ART to Human Prediction Algorithms
Conghao Wong, Ziqian Zou
Human curiosity and longing for the future have driven human progress from ancient times to the present. With the advancement of AI technology, human prediction algorithms have also drawn increasing attention from researchers. However, as AI technology continues to advance, we observe a counterintuitive phenomenon. Take human trajectory prediction algorithms as an example. Although models' quantitative prediction performance has improved, yielding one state-of-the-art model after another, the consideration of human behaviors, future thoughts, and social interactions themselves presents a gradually diminishing trend in the construction of these prediction algorithms, buried beneath numbers and metrics. In this manuscript, taking interactive art and human trajectory prediction algorithms as examples, we attempt to adopt a different analytical perspective to analyze the structural and developmental commonalities between these two fields. By examining specific interactive artworks and prediction algorithms, we aim to explore how human intuitions and art are associated with AI algorithms through three aspects, including perception, interaction, and timing, thereby providing new insights for the understanding and further research in this domain. We also observe an interesting phenomenon that certain art-inspired prediction algorithms can actually deduce intriguing patterns of human behaviors from the data, offering new potential avenues for understanding the relationship between human activities, state of The Art, and AI algorithms.
#05Nov 12, 2026
cs.CV
Improved CNN-Transformer Network for Neutron Image Super-Resolution
Yuheng Chu, Hongli Li, Tian Zhang
The spatial resolution of neutron images is of great importance for material defect diagnosis, structural characterization, and nondestructive testing. However, owing to limitations in neutron beam quality, scintillator conversion efficiency and light scattering, detector pixel size, and noise levels, conventional neutron images often suffer from speckle noise, blurred edges, and missing details, making them inadequate for high-precision analysis. To address these issues, an improved super-resolution model combining convolutional neural networks (CNNs) and Transformers is proposed. First, to overcome the difficulty of obtaining high-quality and clear training images for neutron imaging, a pretrained image restoration model is fine-tuned using real neutron images and then employed to enhance existing neutron images, thereby providing high-quality reference images for subsequent super-resolution model training. Second, an improved CRAFT-based super-resolution network is developed by incorporating a hybrid prior-guided channel fusion module (HPCF), a mean-standard deviation dual-statistics squeeze-and-excitation block (MS-SEBlock), and a partial convolution feed-forward network (PCFN), with the aim of improving the reconstruction of important information, such as textures and fine details, in neutron images. Simulation results demonstrate that, compared with bicubic interpolation, SRCNN, SRGAN-VGG, and other methods, the proposed network achieves superior performance in both full-reference metrics, including PSNR and SSIM, and no-reference metrics, including AGM and SMD. The visual quality and structural fidelity of the reconstructed images are also significantly improved. This study provides a new approach to improving neutron image quality.