Resumen de: EP4811306A2
0001 Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection. In one aspect, a method comprises: obtaining: (i) an image, and (ii) a set of one or more query embeddings, wherein each query embedding represents a respective category of object; processing the image and the set of query embeddings using an object detection neural network to generate object detection data for the image, comprising: processing the image using an image encoding subnetwork of the object detection neural network to generate a set of object embeddings; processing each object embedding using a localization subnetwork to generate localization data defining a corresponding region of the image; and processing: (i) the set of object embeddings, and (ii) the set of query embeddings, using a classification subnetwork to generate, for each object embedding, a respective classification score distribution over the set of query embeddings.
Resumen de: EP4811192A1
0001 The present invention relates to a method for securing against adversarial and backdoor attacks a computer system including a memory storing a plurality of benign images (501) and using at least one neural network comprising an input layer, hidden layers, and an output layer generating an embedding or a classification result into a set of output classes for an input image presented at the input layer, said method being performed by said computer system and comprising, before or during the inference phase: - splitting at least one benign image (501) among said stored benign images into a plurality of zones according to at least one predefined method; and at the inference phase: - acquiring an image (502); - splitting the acquired image into a plurality of zones according to said at least one predefined method; -generating at least one composite image (503, 504, 505, 506) by replacing, in at least one split benign image among said stored benign images a zone of the split benign image corresponding to a zone of the split acquired image with said zone of the split acquired image; -applying said at least one neural network to said at least one generated composite image for generating a result from said output layer of said at least one neural network; -comparing said generated result and a result generated by applying said at least one neural network to said acquired image; - based on said comparison, detecting an adversarial or backdoor attack and performing a predetermi
Nº publicación: EP4811314A2 23/09/2026
Solicitante:
CLO VIRTUAL FASHION INC [KR]
CLO Virtual Fashion Inc.
Resumen de: EP4811314A2
0001 Estimating a material property parameter of fabric involves receiving information including a three-dimensional (3D) contour shape of fabric placed over a 3D geometric object, estimating a material property parameter of the fabric used for representing drape shapes of 3D clothes made by the fabric by applying the information to a trained artificial neural network, and providing the material property parameter of the fabric.