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Found 10 papers, 1 papers with code
Conditional Image Repainting via Semantic Bridge and Piecewise Value Function
We study conditional image repainting where a model is trained to generate visual content conditioned on user inputs, and composite the generated content seamlessly onto a user provided image while preserving the semantics of users' inputs.
Colorizing Monochromatic Radiance Fields
Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided.
Language-guided Image Reflection Separation
This paper studies the problem of language-guided reflection separation, which aims at addressing the ill-posed reflection separation problem by introducing language descriptions to provide layer content.
Affective Image Filter: Reflecting Emotions from Text to Images
In this work, we propose Affective Image Filter (AIF), a novel model that is able to understand the visually-abstract emotions from the text and reflect them to visually-concrete images with appropriate colors and textures.
L-CoIns: Language-Based Colorization With Instance Awareness
Language-based colorization produces plausible colors consistent with the language description provided by the user.
CT2: Colorization Transformer via Color Tokens
Automatic image colorization is an ill-posed problem with multi-modal uncertainty, and there remains two main challenges with previous methods: incorrect semantic colors and under-saturation.
UniCoRN: A Unified Conditional Image Repainting Network
Conditional image repainting (CIR) is an advanced image editing task, which requires the model to generate visual content in user-specified regions conditioned on multiple cross-modality constraints, and composite the visual content with the provided background seamlessly.
MISC: Multi-Condition Injection and Spatially-Adaptive Compositing for Conditional Person Image Synthesis
In this paper, we explore synthesizing person images with multiple conditions for various backgrounds.
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition
To facilitate end-to-end training, we further develop a scenario context information extraction branch to extract context information from raw RGB video directly.
Ranked #83 on
Skeleton Based Action Recognition
on NTU RGB+D
STS Classification with Dual-stream CNN
Inspired by the dual-stream hypothesis in neural science, we propose a novel dual-stream framework for modeling the interweaved spatiotemporal dependency, and develop a convolutional neural network within this framework that aims to achieve high adaptability and flexibility in STS configurations from various diagonals, i. e., sequential order, dependency range and features.
