Abstract: A method, apparatus, and product for video panoptic segmentation are disclosed. Such video panoptic segmentation includes generating, by a convolutional neural network encoder of a video panoptic segmentation system, multi-scale feature maps from a sequence of video frames, the system further including a pixel decoder, a transformer decoder, and an online tracker. The method further includes refining the multi-scale feature maps to produce mask feature representations and producing, by the transformer decoder, query embeddings and mask predictions from the refined mask feature representations. The method also includes matching the query embeddings for a current frame with query embeddings for a previous frame, refining the current-frame query embeddings based on the matched embeddings, and outputting panoptic-segmentation results based on the refined query embeddings that identify classes and instance associations across frames.

Abstract: A system and a method are disclosed for KD. A method may include performing knowledge distillation (KD) from a vision transformer (ViT) teacher network to a convolutional neural network (CNN) student network.

Abstract: A method of image processing includes: determining a first feature, wherein the first feature has a dimensionality D1; determining a second feature, wherein the second feature has a dimensionality D2 and is based on an output of a feature extraction network; generating a third feature by processing the first feature, the third feature having a dimensionality D3; generating a guidance by processing the second feature, the guidance having the dimensionality D3; generating a filter output by applying a deep guided filter (DGF) to the third feature using the guidance; generating a map based on the filter output; and outputting a processed image based on the map.

Abstract: A method of image processing includes: determining a first feature, wherein the first feature has a dimensionality D1; determining a second feature, wherein the second feature has a dimensionality D2 and is based on an output of a feature extraction network; generating a third feature by processing the first feature, the third feature having a dimensionality D3; generating a guidance by processing the second feature, the guidance having the dimensionality D3; generating a filter output by applying a deep guided filter (DGF) to the third feature using the guidance; generating a map based on the filter output; and outputting a processed image based on the map.

Abstract: The disclosure provides a method, apparatus, electronic device and storage medium for data processing. The method includes: obtaining a storage request for target data; obtaining first dimension information and second dimension information of the target data based on the storage request, the first dimension information representing information of a user to which the target data belongs, and the second dimension information representing information of a business to which the target data belongs; and storing the target data into the first storage region.

Abstract: A system and a method are disclosed for classifying features from an input image. The method includes generating, by a processing circuit, a segment feature from a first input image, the segment feature corresponding to a group of pixels associated with an object represented in the first input image, and being an out-of-vocabulary segment feature; performing, by the processing circuit, a retrieval of a first feature vector, corresponding to the segment feature, from a database of feature vectors, the first feature vector representing an object-specific segmentation mask; and generating, by the processing circuit, an output segmentation mask based on the first feature vector.

Abstract: A technique for quantization in diffusion transformers is disclosed. A weight quantizer is configured to quantize a weight matrix of a layer in a diffusion transformer block to generate a quantized weight matrix. An activation quantizer is configured to quantize an activation matrix of the layer to generate a quantized activation matrix. A time-step quantizer is configured to estimate a quantization parameter based on at least one of the quantized weight matrix or the quantized activation matrix for a time step based on a per-step calibration set.

Abstract: Disclosed is a method including receiving, in a semantic segmentation network, input data from a plurality of frames, computing a ground truth label on the plurality of frames, generating a ground truth temporal semantic boundary map from the ground truth label on the plurality of frames, generating a predicted temporal semantic boundary map based on an output of the input data, and determining a loss based on the ground truth temporal semantic boundary map and the predicted temporal semantic boundary map.

Abstract: The present disclosure relates to the technical field of chemical profile control and water shutoff in oilfields, and discloses a modified graphite-coated soft particle and a method for preparing the same, and a profile control and water shutoff agent and an application thereof. The modified graphite-coated soft particle includes a soft particle and first modified graphite coating a surface of the soft particle. The soft particle includes one or more of a rubber main agent, an antioxidant, an auxiliary antioxidant, a density regulator, and second modified graphite. The first modified graphite and the second modified graphite are the same or different and each are modified graphite with a surface rich in hydroxyl groups and carboxyl groups. The profile control and water shutoff agent can mutually agglomerate after aging for a period upon entering a deep formation zone, and can form effective sealing of a water channeling pathway.

Abstract: The present disclosure relates to the technical field of oilfield chemistry, and discloses a CO2 chemical reaction viscosity-increasing type polymer surfactant for gas channeling control during CO2 flooding in offshore heavy oil reservoirs, a preparation method therefor and use thereof, the CO2 reactive viscosity-increasing polymer surfactant includes structural units represented by formula (1), formula (2) and a formula (3) respectively; R1, R2, R3, R4 and R5 in formula (1) are respectively one or more of H, F, C1-C4 alkyl alcohol, and at least one group is F; R6, R7 and R8 in formula (2) are respectively one or more of H, C1-C4 alkyl; R in formula (3) is one or more of C10-C16 alkyl; the CO2 reactive viscosity-increasing polymer surfactant has a high CO2 gas channeling plugging strength, excellent chemical stability, and desirable heavy oil viscosity reduction performance;

Abstract: A computer vision system including: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: determine a semantic multi-scale context feature and an instance multi-scale context feature of an input scene; generate a joint attention map based on the semantic multi-scale context feature and the instance multi-scale context feature; refine the semantic multi-scale context feature and instance multi-scale context feature based on the joint attention map; and generate a panoptic segmentation image based on the refined semantic multi-scale context feature and the refined instance multi-scale context feature.

Abstract: A system and a method are disclosed for performing video segmentation, including obtaining a plurality of frames from an input video; extracting a plurality of features from the plurality of frames; obtaining query embeddings corresponding to the plurality of features; refining the query embeddings in a forward time order to generate forward embeddings, and in a backward time order to generate backward embeddings; fusing the forward embeddings and the backward embeddings to obtain fused embeddings; and generating a classification prediction corresponding to the input video based on the fused embeddings.

Abstract: A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

Abstract: Methods and systems for performing video prediction, including obtaining an input frame from among a plurality of frames included in an input video; extracting a first feature map by providing the input frame to a first plurality of feature extraction layers and a first strided convolutional layer included in an encoder; providing the first feature map and at least one neighboring first feature map corresponding to at least one neighboring frame to a first fusion module included in the encoder; fusing the first feature map with the at least one neighboring first feature map to generate a fused first feature map using the first fusion module; generating a prediction corresponding to the input frame based on the fused first feature map using a decoder; and performing a video prediction task using the prediction.

Abstract: A surge protection circuit includes a first surge protection device (SPD) including a first terminal coupled to a voltage potential and a second terminal. A second SPD includes a first terminal coupled to the voltage potential and a second terminal. A diode array includes a first plurality of diodes, each diode of the first plurality of diodes including an anode coupleable to a respective power line of an electronic circuit and a cathode coupled to the second terminal of the first SPD. The diode array also includes a second plurality of diodes, each diode of the second plurality of diodes including a cathode coupled to a respective power line of the electronic circuit and an anode coupled to the second terminal of the second SPD. The second terminal of the first SPD is further coupled to a common power bus of the electronic circuit.

Abstract: A system and a method are disclosed for tuning parameters of a large language model. The method comprises identifying first weights of a machine learning (ML) model. Second weights are received from a client device. The second weights may be based on updating, by the client device, the first weights. An update matrix may be generated based on the second weights. The update matrix may be decomposed into first decomposition matrices. Singular values that satisfy a criterion may be identified based on the first decomposition matrices. Singular vectors may be identified based on the singular values. Second decomposition matrices may be identified based on the singular vectors. Updates may be received from the client device of third weights associated with the second decomposition matrices. An updated ML model may be generated based on the updates of the third weights. An inference may be generated based on the updated ML model.

Abstract: Disclosed are an electrostatically driven comb structure of an MEMS (Micro Electro Mechanical System), a micro-mirror using the same, and a preparation method therefor.

Abstract: Disclosed are an electrostatically driven comb structure of an MEMS (Micro Electro Mechanical System), a micro-mirror using the same, and a preparation method therefor.

Abstract: The disclosure provides a method, apparatus, electronic device and storage medium for data processing. The method includes: obtaining a storage request for target data; obtaining first dimension information and second dimension information of the target data based on the storage request, the first dimension information representing information of a user to which the target data belongs, and the second dimension information representing information of a business to which the target data belongs; and storing the target data into the first storage region.

Abstract: A system and a method to train a neural network are disclosed. A first image is weakly and strongly augmented. The first image, the weakly and strongly augmented first images are input into a feature extractor to obtain augmented features. Each weakly augmented first image is input to a corresponding first expert head to determine a supervised loss for each weakly augmented first image. Each strongly augmented first image is input to a corresponding second expert head to determine a diversity loss for each strongly augmented first image. The feature extractor is trained to minimize the supervised loss on weakly augmented first images and to minimize a multi-expert consensus loss on strongly augmented first images. Each first expert head is trained to minimize the supervised loss for each weakly augmented first image, and each second expert head is trained to minimize the diversity loss for each strongly augmented first image.