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Found 12 papers, 4 papers with code
Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation
Facial images have extensive practical applications.
HumanVLM: Foundation for Human-Scene Vision-Language Model
Specifically, (1) we create a large-scale human-scene multimodal image-text dataset (HumanCaption-10M) sourced from the Internet to facilitate domain-specific alignment; (2) develop a captioning approach for human-centered images, capturing human faces, bodies, and backgrounds, and construct a high-quality Human-Scene image-text dataset (HumanCaptionHQ, about 311k pairs) that contain as much detailed information as possible about human; (3) Using HumanCaption-10M and HumanCaptionHQ, we train a HumanVLM.
Granular-ball Representation Learning for Deep CNN on Learning with Label Noise
Specifically, considering the classification task: (1) in forward process, we split the input samples as $gb$ samples at feature-level, each of which can correspond to multiple samples with varying numbers and share one single label; (2) during the backpropagation process, we modify the gradient allocation strategy of the GBC module to enable it to propagate normally; and (3) we develop an experience replay policy to ensure the stability of the training process.
PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding
In this study, we developed a domain-specific large language-vision assistant (PA-LLaVA) for pathology image understanding.
15M Multimodal Facial Image-Text Dataset
FaceCaption-15M comprises over 15 million pairs of facial images and their corresponding natural language descriptions of facial features, making it the largest facial image-caption dataset to date.
Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
In specific scenarios, face sketch can be used to identify a person.
Sketch Less Face Image Retrieval: A New Challenge
In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was carried out at each stroke and aim to retrieve the target face photo using a partial sketch with as few strokes as possible (see Fig. 1).
A Study of Deep CNN Model with Labeling Noise Based on Granular-ball Computing
In this paper, we pioneered a granular ball neural network algorithm model, which adopts the idea of multi-granular to filter label noise samples during model training, solving the current problem of model instability caused by label noise in the field of deep learning, greatly reducing the proportion of label noise in training samples and improving the robustness of neural network models.
One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning
Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene.
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval
We consider that there is a significant correlation among these incomplete sketches in the sketch drawing episode of each photo.
Rethinking the Image Feature Biases Exhibited by Deep CNN Models
From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature.
Understanding the Feedforward Artificial Neural Network Model From the Perspective of Network Flow
Experiments for two types of ANN model including multi-layer MLP and CNN verify that the network flow based on class-pathway is a reasonable explanation for ANN models.
