Titlebook: Computational Visual Media; 12th International C Fang-Lue Zhang,Andrei Sharf Conference proceedings 2024 The Editor(s) (if applicable) and

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Multi-level Patch Transformer for?Style Transfer with?Single Reference Imageof large volumes of style image data. In this work, we present a deep model called . to optimize the mapping between a content image and a single style image by leveraging the strengths of transformer encoders and generative adversarial networks, where we advocate for patch-level operations. Our pro

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Palette-Based Content-Aware Image Recoloringpulating a small set of representative colors. Many approaches have been proposed for palette extraction and palette-based image recoloring. However, existing methods primarily leverage low-level visual information to extract color palettes, so that different objects with similar colors will share t

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FreeStyler: A Free-Form Stylization Method via?Multimodal Vector Quantizationowever, most existing works only support single-modal guidance, which is not ideal for real-world applications. To tackle this limitation, we propose FreeStyler, a flexible framework for image stylization that is capable of handling various input scenarios. Our approach goes beyond the traditional a

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Denoised Dual-Level Contrastive Network for?Weakly-Supervised Temporal Sentence Grounding labeling the temporal boundaries, weakly-supervised methods have drawn increasing attention. Most of the weakly-supervised methods heavily rely on aligning the visual and textual modalities, ignoring modeling the confusing snippets within a video and non-discriminative snippets across different vid

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13 B,H,S 1-Thia-,-decaborane(9),egy (NRS) modules. BBMG leverages the inherent characteristics of boundary blur to simulate a comprehensive range of tampering techniques, enabling a more realistic representation of real-world scenarios. In conjunction with BBMG, the NRS module effectively mitigates the influence of noise samples.

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14 B,H,S 1-Thia-,-dodecaborane(11),lity prediction function from data. We evaluate the proposed method for different powerful FR models on two classical video-based (or template-based) benchmarks IJB-B and YTF. Extensive experiments show that, although the tinyFQnet is much smaller than the others, the proposed method outperforms sta

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,6 Ar3H2S Hydrogen sulfide – argon (3/1),, they do not consider the arousal degree of an expression. We propose a neutral-expression-aware expression feature similarity loss based on the traditional anchor loss, which can further guide the network to learn better features from an input image. Extensive experiments demonstrate the advantage

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12 BH4Na Sodium tetrahydroborate,y when the datasets contain a small number of samples. We also demonstrate that the structured layout space constructed by our method enables structure blending between structured layouts. We will release our code upon the acceptance of the paper.

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