Patents by Inventor Debasmit DAS

Debasmit DAS has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20250259283
    Abstract: Systems and techniques are described herein for detecting objects. For instance, a method for detecting objects is provided. The method may include generating first image features based on a first image; generating second image features based on a second image; aligning the first image features and the second image features to generate aligned features; estimating motion parameters based on the aligned features; adjusting the aligned features based on the motion parameters to generate adjusted aligned image features; and detecting an object based on the adjusted aligned image features.
    Type: Application
    Filed: February 14, 2024
    Publication date: August 14, 2025
    Inventors: Varun RAVI KUMAR, Debasmit DAS, Senthil Kumar YOGAMANI
  • Publication number: 20250252627
    Abstract: A processor-implemented method performed for text-based video editing includes receiving a video input and a text prompt. The video input includes a sequence of video frames. Features of the video input are extracted to generate a latent representation of the video input. Noise is injected to the latent representation of the video input to generate a noise injected latent. The noise is conditioned on the video input. An artificial neural network (ANN) model processes the noise injected latent based on the text prompt to adapt the video input according to the text prompt.
    Type: Application
    Filed: February 6, 2024
    Publication date: August 7, 2025
    Inventors: Hyojin PARK, Debasmit DAS, Munawar HAYAT, Fatih Murat PORIKLI
  • Publication number: 20250245883
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a first latent tensor generated during a first iteration of processing data using a denoising backbone of a diffusion machine learning model is accessed. A guidance scale is generated based on processing the first latent tensor using a guidance machine learning model. A second latent tensor is generated during a second iteration of processing data using the denoising backbone based on the first latent tensor and the first guidance scale, and an output from the diffusion machine learning model is generated based at least in part on the second latent tensor.
    Type: Application
    Filed: January 25, 2024
    Publication date: July 31, 2025
    Inventors: Samuel SHOWALTER, Risheek GARREPALLI, Debasmit DAS, Munawar HAYAT, Fatih Murat PORIKLI
  • Publication number: 20250245427
    Abstract: A processor-implemented method for selective parameter efficient fine-tuning (PEFT) includes receiving a large language model (LLM). The LLM has multiple layers with each layer having a set of parameters. A subset of the parameters are identified to fine-tune for a downstream task based on a score function. An adapter is applied to the identified subset of the parameters to fine-tune. Only the identified subset of the parameters is fine-tuned.
    Type: Application
    Filed: January 26, 2024
    Publication date: July 31, 2025
    Inventors: Sungha CHOI, Jungsoo LEE, Jaeseong YOU, Debasmit DAS, Munawar HAYAT
  • Publication number: 20250200429
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, a reference latent tensor generated based on a reference input to a diffusion machine learning model is accessed. A first latent tensor generated during a first iteration of processing data using a denoising backbone of the diffusion machine learning model is accessed, and a first intermediate tensor is generated based on processing the reference latent tensor and the first latent tensor using an auxiliary machine learning model. A second latent tensor is generated, during a second iteration of processing data using the denoising backbone, based on the first latent tensor and at least in part on the first intermediate tensor.
    Type: Application
    Filed: December 18, 2023
    Publication date: June 19, 2025
    Inventors: Samuel SHOWALTER, Risheek GARREPALLI, Debasmit DAS, Hyojin PARK, Munawar HAYAT, Fatih Murat PORIKLI
  • Publication number: 20250166236
    Abstract: Certain aspects of the present disclosure provide techniques for generating an output image based on a text prompt. A method may include receiving the text prompt; providing a user interface comprising one or more input elements associated with one or more words of the text prompt; receiving input corresponding to at least one of the one or more input elements, the input indicating a semantic importance for each of at least one of the one or more words associated with the at least one of the one or more input elements; and generating the output image based on the text prompt and the input.
    Type: Application
    Filed: November 16, 2023
    Publication date: May 22, 2025
    Inventors: Kambiz AZARIAN YAZDI, Fatih Murat PORIKLI, Qiqi HOU, Debasmit DAS
  • Publication number: 20250148752
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input image is accessed, and the input image is processed using an image encoder to generate an image embedding tensor. The image embedding tensor is processed using a mask decoder machine learning model to generate a set of mask embedding tensors. A textual input is processed using a text encoder to generate a text embedding tensor. A set of augmented masks is generated based on aggregating the text embedding tensor with the set of mask embedding tensors.
    Type: Application
    Filed: November 6, 2023
    Publication date: May 8, 2025
    Inventors: Vibashan VISHNUKUMAR SHARMINI, Shubhankar Mangesh BORSE, Hyojin PARK, Debasmit DAS, Munawar HAYAT, Fatih Murat PORIKLI
  • Publication number: 20240412493
    Abstract: Systems and techniques are provided for processing image data. According to some aspects, a computing device can generate a gradient (e.g., a classifier gradient using a trained classifier) associated with a current sample. The computing device can combine the gradient with an iterative model estimated score function or data associated with the current sample to generate a score function estimate. The computing device can predict, using the diffusion machine learning model and based on the score function estimate, a new sample.
    Type: Application
    Filed: December 12, 2023
    Publication date: December 12, 2024
    Inventors: Risheek GARREPALLI, Yunxiao SHI, Hong CAI, Yinhao ZHU, Shubhankar Mangesh BORSE, Jisoo JEONG, Debasmit DAS, Manish Kumar SINGH, Rajeev YASARLA, Shizhong Steve HAN, Fatih Murat PORIKLI
  • Publication number: 20240404003
    Abstract: Certain aspects of the present disclosure provide techniques for training and using an instance segmentation neural network to detect instances of a target object in an image. An example method generally includes generating, through an instance segmentation neural network, a first mask output from a first mask generation branch of the network. The method further includes generating, through the instance segmentation neural network, a second mask output from a second, parallel, mask generation branch of the network. The second mask output is typically of a lower resolution than the first mask output. The method further includes combining the first mask output and second mask output to generate a combined mask output. Based on the combined mask output, an output of the instance segmentation neural network is generated. One or more actions are taken based on the generated output.
    Type: Application
    Filed: May 31, 2023
    Publication date: December 5, 2024
    Inventors: Debasmit DAS, Hyojin PARK, Shubhankar Mangesh BORSE, Yu FU, Oleksandr BAILO, Mohsen GHAFOORIAN, Fatih Murat PORIKLI
  • Publication number: 20240303497
    Abstract: A processor-implemented method for adapting an artificial neural network (ANN) at test-time includes receiving by a first ANN model and a second ANN model, a test data set. The test data set includes unlabeled data samples. The first ANN model is pretrained using a training data set and the test data set. The first ANN model generates first estimated labels for the test data set. The second ANN model generates second estimated labels for the test data set. Samples of the test data set are selected based on a confidence difference between the first estimated labels and the second estimated labels. The second ANN model is retrained based on the selected samples.
    Type: Application
    Filed: July 27, 2023
    Publication date: September 12, 2024
    Inventors: Jungsoo LEE, Debasmit DAS, Sungha CHOI
  • Publication number: 20240273742
    Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for processing image data. For example, a process can include obtaining segmentation information associated with an image of a scene, the image including a plurality of pixels having a resolution, and obtaining depth information associated with one or more objects in the scene. A plurality of features can be generated corresponding to the plurality of pixels, wherein each feature of the plurality of features corresponds to a particular pixel of the plurality of pixels, and wherein each feature includes respective segmentation information of the particular pixel and respective depth information of the particular pixel. The plurality of features can be processed to generate a dense depth output corresponding to the image.
    Type: Application
    Filed: February 6, 2023
    Publication date: August 15, 2024
    Inventors: Debasmit DAS, Varun RAVI KUMAR, Shubhankar Mangesh BORSE, Senthil Kumar YOGAMANI
  • Publication number: 20240169542
    Abstract: Techniques and systems are provided for generating one or more segmentations masks. For instance, a process may include generating a delta image based on a difference between a current image and a prior image. The process may further include processing, using a transform operation, the delta image and features representing the prior image to generate a transformed feature representation of the prior image. The process may include combining the transformed feature representation of the prior image with features representing the current image to generate a combined feature representation of the current image. The process may further include generating, based on the combined feature representation of the current image, a segmentation mask for the current image.
    Type: Application
    Filed: July 3, 2023
    Publication date: May 23, 2024
    Inventors: Shubhankar Mangesh BORSE, Hyojin PARK, Risheek GARREPALLI, Debasmit DAS, Hong CAI, Fatih Murat PORIKLI
  • Publication number: 20240161368
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for regenerative learning to enhance dense predictions. In one example method, an input image is accessed. A dense prediction output is generated based on the input image using a dense prediction machine learning (ML) model, and a regenerated version of the input image is generated. A first loss is generated based on the input image and a corresponding ground truth dense prediction, and a second loss is generated based on the regenerated version of the input image. One or more parameters of the dense prediction ML model are updated based on the first and second losses.
    Type: Application
    Filed: September 5, 2023
    Publication date: May 16, 2024
    Inventors: Shubhankar Mangesh BORSE, Debasmit DAS, Hyojin PARK, Hong CAI, Risheek GARREPALLI, Fatih Murat PORIKLI
  • Publication number: 20240104367
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training a machine learning model. An example method generally includes partitioning a machine learning model into a plurality of partitions. A request to update a respective partition of the plurality of partitions in the machine learning model is transmitted to each respective participating device of a plurality of participating devices in a federated learning scheme, and the request may specify that the respective partition is to be updated based on unique data at the respective participating device. Updates to one or more partitions in the machine learning model are received from the plurality of participating devices, and the machine learning model is updated based on the received updates.
    Type: Application
    Filed: September 21, 2022
    Publication date: March 28, 2024
    Inventors: Jamie Menjay LIN, Debasmit DAS
  • Publication number: 20240095504
    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for feature masking. A feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. A masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.
    Type: Application
    Filed: September 16, 2022
    Publication date: March 21, 2024
    Inventors: Debasmit DAS, Jamie Menjay LIN
  • Publication number: 20240078800
    Abstract: A method receives first and second data generated from a first and second domains including first and second set of objects, receiving first class labels for each of the first set of objects, and receiving second class labels for each of the second set of objects. The method generates a training dataset by augmenting the first data and corresponding first class labels, and locally updating neural network parameters of a model based on the training dataset. The method generates a validation dataset by augmenting the second data and corresponding second class labels, and globally updating the neural network parameters of the model based on the validation dataset. The method also generates multiple target labels for target data generated from a target domain including a third set of objects after globally updating the neural network parameters of the model based on the validation dataset.
    Type: Application
    Filed: September 7, 2022
    Publication date: March 7, 2024
    Inventors: Saeed VAHIDIAN, Manoj BHAT, Debasmit DAS, Shizhong Steve HAN, Fatih Murat PORIKLI
  • Publication number: 20240078797
    Abstract: Techniques and systems are provided for performing online adaptation of machine learning model(s). For example, a process may include obtaining features extracted from a image by a machine learning model during inference and determining, by the machine learning model based on the features during inference, a plurality of keypoint estimates in the image and/or a bounding region estimate associated with an object in the image. The process may further include generating pseudo-label(s) based on the plurality of keypoint estimates and/or the bounding region estimate. The process may include determining at least one self-supervised loss based on the plurality of keypoint estimates and/or the bounding region estimate. The process may further include adapting, based on the at least one self-supervised loss, parameter(s) of the machine learning model. The process may include generating, using the machine learning model with the adapted parameter(s), a segmentation mask for the image (or another image).
    Type: Application
    Filed: August 3, 2023
    Publication date: March 7, 2024
    Inventors: Kambiz AZARIAN YAZDI, Debasmit DAS, Hyojin PARK, Fatih Murat PORIKLI
  • Publication number: 20240020848
    Abstract: Systems and techniques are provided for processing one or more images. For instance, according to some aspects of the disclosure, a method may include obtaining an unlabeled image and generating at least one transformed image based on the unlabeled image. The method may include processing the unlabeled image using a pre-trained semantic segmentation model to generate a first segmentation output. The method may further include processing the at least one transformed image using the pre-trained semantic segmentation model to generate at least a second segmentation output. The method may include fine-tuning, based on the first segmentation output and at least the second segmentation output, one or more parameters of the pre-trained semantic segmentation model.
    Type: Application
    Filed: July 10, 2023
    Publication date: January 18, 2024
    Inventors: Debasmit DAS, Shubhankar Mangesh BORSE, Hyojin PARK, Kambiz AZARIAN YAZDI, Hong CAI, Risheek GARREPALLI, Fatih Murat PORIKLI
  • Publication number: 20240020844
    Abstract: Systems and techniques are provided for processing data (e.g., image data). For instance, according to some aspects of the disclosure, a method may include receiving, at a transformer of a machine learning system, learnable queries, keys, and values obtained from a feature map of a segmentation model of the machine learning system. The method may further include learning, via the transformer, a mapping between an unsupervised output and a supervised output of the segmentation model based on the feature map.
    Type: Application
    Filed: July 10, 2023
    Publication date: January 18, 2024
    Inventors: Debasmit DAS, Shubhankar Mangesh BORSE, Hyojin PARK, Kambiz AZARIAN YAZDI, Hong CAI, Risheek GARREPALLI, Fatih Murat PORIKLI
  • Publication number: 20230376753
    Abstract: Systems and techniques are provided for training a neural network model or machine learning model. For example, a method of augmenting training data can include augmenting, based on a randomly initialized neural network, training data to generate augmented training data and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data. The method can further include applying semantic-aware style fusion to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training the neural network model or machine learning model.
    Type: Application
    Filed: January 20, 2023
    Publication date: November 23, 2023
    Inventors: Seokeon CHOI, Sungha CHOI, Seunghan YANG, Hyunsin PARK, Debasmit DAS, Sungrack YUN