We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.

We propose a framework that synthesizes artistic landscape sketches using a diffusion model-based approach. Furthermore, we suggest a three-channel perspective map (3CPM) that mimics the artistic skill used by real artists. We employ Stable Diffusion, which leads us to use ControlNet to process 3CPM in Stable Diffusion. Additionally, we adopt the Low Rank Adaptation (LoRA) method to fine-tune our framework, thereby enhancing the quality of sketch and resolving the color-remaining problem, which is a frequently observed artifact in the sketch images using diffusion models. We implement a bimodal sketch generation interface: text to sketch and image to sketch. In producing a sketch, a guide token is used so that our method synthesizes an artistic sketch in both cases. Finally, we evaluate our framework using quantitative and quantitative schemes. Various sketch images synthesized by our framework demonstrate the excellence of our study.

Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

We present a diffusion model-based approach that applies the artistic style of an artist or an art movement to a portrait photograph. Learning the style from the artworks of an artist or an art movement requires a training dataset composed of a lot of samples. We resolve this limitation by combining Contrastive Language Image Pretraining (CLIP) encoder and diffusion model, since the CLIP encoder extracts the features from an input portrait in a very effective way. Our framework includes three independent CLIP encoders that extract the text features, color features and Canny edge features from an input portrait, respectively. These features are incorporated to the style information extracted through a diffusion model to complete the stylization on an input portrait. The diffusion model extracts the style information from the sample images in the training dataset using an image encoder. The denoising steps in the diffusion model applies the style information from the training dataset to the CLIP-based features from an input portrait. Finally, our framework produces an artistic portrait that presents both the identity of the input portrait and the artistic style from the training dataset. The most important contribution of our framework is that our framework requires less than a hundred sample images for an artistic style. Therefore, our framework can successfully extract styles from an artist who has drawn less than a hundred artworks. We sample three artists and three art movements and apply these styles to the portraits of various identities and produce visually pleasing results. We evaluate our results using various metrics, including Frechet Inception Distance (FID), ArtFID and Language-Image Quality Evaluator (LIQE) to prove the excellence of our results.

We showed that any reversible Finsler metric with positive constant flag curvature must be Riemannian.