Deep Learning

Deep learning has achieved remarkable success in various fields, including computer vision, natural language processing, speech recognition, and many others. Some popular deep learning architectures include convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequential data, and transformer models for natural language processing tasks.

Our team members are researching on various aspects of Deep Learning,

Computer Vision

Our team is dedicated to pioneering advancements in visual perception technology. Through innovative algorithms and cutting-edge research, we're unlocking new possibilities in fields such as object discovery, medical imaging, etc.,. From identifying objects in images to analyzing complex scenes, computer vision empowers us to understand and interact with the visual world in transformative ways.

Data Centric Deep Learning

Our team is at the forefront of this cutting-edge approach, prioritizing data quality and diversity to drive groundbreaking advancements in artificial intelligence. By harnessing innovative algorithms and advanced data management strategies, we're pushing the boundaries of what's possible in fields ranging from healthcare to finance.

Learning with Noisy Labels

We're tackling the challenge of training machine learning models in the presence of label noise, which is prevalent in many real-world datasets. Our team is developing innovative algorithms and methodologies to effectively learn from noisy or mislabeled data, improving the robustness and generalization of AI systems. Through our work, we aim to overcome the limitations of noisy labels and drive advancements in machine learning that are more resilient to imperfect data.

Self-Supervised Learning

In traditional supervised learning, models are trained using labeled data. However, in self-supervised learning, models learn from the inherent structure of the data itself, without requiring explicit labels. Our team is at the forefront of developing innovative self-supervised learning techniques that leverage unlabeled data to train robust and versatile AI models. Through our research, we're unlocking new possibilities in machine learning and pushing the boundaries of what's possible in autonomous learning.

Dataset Generation

We specialize in creating high-quality datasets tailored to specific machine learning tasks and applications. Leveraging advanced algorithms and domain knowledge, our team generates datasets with diverse and representative samples, ensuring optimal model performance and generalization. Whether it's in computer vision, natural language processing, or other domains, we're dedicated to providing the data necessary to fuel AI innovation and research.

Image Generation

We specialize in creating realistic and diverse images using advanced generative models and algorithms. Whether it's generating high-resolution images from scratch or synthesizing novel visual content, our team is at the forefront of image generation research. From artistic creations to data augmentation for machine learning, we're dedicated to pushing the boundaries of what's possible in visual synthesis.

Curation

We specialize in meticulously selecting, organizing, and refining datasets to meet the specific needs of our clients. With a keen eye for quality and relevance, our team ensures that curated datasets are comprehensive, accurate, and tailored to your requirements. Whether it's for machine learning, research, or business applications, we're committed to providing curated datasets that drive actionable insights and fuel innovation.

Representation Learning

We specialize in extracting meaningful and informative representations from raw data using advanced machine learning techniques. Our team is dedicated to developing algorithms that can automatically learn hierarchical and abstract features, enabling better understanding and manipulation of complex data. From image and text data to audio and video, we're at the forefront of representation learning research, driving forward advancements in AI and data-driven decision-making.

Structure from Motion (SfM)

We're passionate about leveraging this powerful technique to reconstruct 3D scenes from 2D images. With applications spanning from archaeology to urban planning, SfM enables us to unlock valuable insights and understand spatial relationships in unprecedented detail.

Depth from Motion (DfM)

We're on a mission to unlock the depth of visual scenes by leveraging motion cues. Through innovative algorithms and cutting-edge research, we're able to infer depth information from dynamic scenes, enabling us to create immersive 3D representations. With applications spanning from robotics to virtual reality, DfM holds the key to unlocking new possibilities in spatial understanding and interaction.

Neural Radiance Fields (NeRF)

NeRF is a cutting-edge technique in computer graphics and computer vision that enables us to create highly detailed 3D reconstructions of scenes from 2D images. By leveraging neural networks and advanced algorithms, NeRF allows us to capture rich scene information, including lighting, texture, and geometry, with unprecedented accuracy. With applications ranging from virtual reality and augmented reality to digital content creation and visual effects, NeRF is revolutionizing how we perceive and interact with digital worlds.

Mask Tracking

We're dedicated to advancing this essential technique, enabling precise and reliable tracking of objects within video streams. With applications spanning from augmented reality to surveillance and beyond, mask tracking plays a crucial role in understanding and interacting with dynamic visual environments.

Object Discovery

Our team specializes in developing algorithms and techniques to automatically discover objects within images and videos. From identifying objects in cluttered scenes to localizing objects with varying scales and orientations, we aim to provide accurate and efficient solutions for object discovery tasks. Through our research and collaboration with industry partners, we strive to address challenges in computer vision and enhance the capabilities of object detection systems.

Few-Short Learning

Few-shot learning is a machine learning paradigm that focuses on training models with only a few examples per class. Our team specializes in developing innovative algorithms and methodologies to address the challenges of learning from limited data. By leveraging transfer learning, meta-learning, and other techniques, we aim to empower models to generalize effectively and adapt to new tasks with minimal training data. Through our research, we strive to push the boundaries of what's possible in few-shot learning and unlock new opportunities for AI applications in various domains.

Fine-Grained Learning

Fine-grained learning focuses on distinguishing between highly similar classes within a larger category. Our team specializes in developing advanced algorithms and techniques to tackle this challenging task, particularly in domains such as image recognition and object classification. By leveraging deep learning and feature extraction methods, we aim to achieve superior accuracy and granularity in classification tasks, enabling applications in areas such as biometrics, wildlife conservation, and manufacturing.