They manage to escape with a clue that leads to Charleston.ĭuring Annabeth’s last meeting with her mother, Athena, the goddess had seemed confused and angry and insisted that Annabeth avenge Athena against the Romans. In Atlanta, Percy and Frank learn that Phorcys is working with Gaea and almost become trapped in his aquarium. When he relays the dream to the others, they understand that this image is connected to the prophecy, and the must find Nico before his time runs out. Back on the ship, Piper realizes the eidolons are still present and extracts an oath from them promising never again to possess the demigods.Īs the ship heads for Atlanta, Percy has a dream in which Nico is trapped in a bronze jar. After Bacchus leaves, eidolons at Gaea’s command possess Jason and Percy, and they almost kill each other. The ship heads to Kansas, where Bacchus turns down their request for help but suggests they visit the old sea god Phorcys in Atlanta. Hazel and Leo receive help from Echo to wrest a sheet of celestial bronze from Narcissus, who is using it as a mirror. If the demigods accept her help, they may be able to avenge an old wrong. In their search, he and Hazel meet the goddess, Nemesis. To repair damage the ship sustained, the demigods stop at a lake that promises to have the celestial bronze Leo needs. Furthermore, the input image can be styled using a variety of color-style presets after being normalized with nDNCM.Ī comparison of the proposed approach with the state-of-the-art techniques is presented below.This study guide refers to the 2012 edition published by Hyperion Books.Īnnabeth and her counterparts from Camp Jupiter convince Hazel, Frank, and Percy to join them on the quest to Rome and Greece on the warship Argo II, but spirits possess Leo’s body and use him to fire on the city. This design guarantees that the parameters of sDNCM can be saved as color style presets and utilized by different input images. The second stage builds an sDNCM from the style image, which stylizes the normalized image to the desired target color style.

The first stage creates an nDNCM from the input image, normalizing the colors and mapping the image to a color-style space representing the content. The underlying structure relies on the encoder E, which predicts parameters employed in the normalization and stylization stages. Neural Preset works in two distinct stages, allowing for quick switching between different styles. Unlike conventional 3D filters that rely on the regression of tens of thousands of parameters, DNCM can model arbitrary color mappings using only a few hundred learnable parameters. Additionally, DNCM functions independently on each pixel, requiring a small memory footprint and supporting high-resolution inputs. DNCM utilizes an image-adaptive color mapping matrix that multiplies the pixels of the same color to produce a specific color and effectively eliminates unrealistic artifacts. The proposed method differs from the current state-of-the-art techniques, employing Deterministic Neural Color Mapping (DNCM) instead of convolutional models for color mapping. To address this problem, color style transfer techniques have been introduced to automatically map the color style from a well-retouched image (i.e., the style image) to another (i.e., the input image).

Therefore, careful adjustments by the users are still necessary. AI-powered video filters can automatically adjust lighting, color balance, and other elements of a video, allowing creators to achieve a professional-quality look without the need for extensive technical knowledge.Īlthough very powerful, these filters are designed with pre-defined parameters, so they cannot generate consistent color styles for images with diverse appearances. Moreover, with the rise of AI, these filters have become even more sophisticated, allowing us to manipulate video content in previously impossible ways with just some clicks. These filters have revolutionized the way we communicate and share experiences, providing us with the ability to create visually appealing and engaging content that captures our audience’s attention. With millions of images and video content posted daily, visual filters have become an essential feature of social media platforms, allowing users to enhance and customize their video content with various effects and adjustments.

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