Search Results for author:
Found 56 papers, 18 papers with code
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization.
Scale Normalized Image Pyramids with AutoFocus for Object Detection
The resulting algorithm is referred to as AutoFocus and results in a 2. 5-5 times speed-up during inference when used with SNIP.
Learning Visual Representations for Transfer Learning by Suppressing Texture
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information.
Ranked #19 on
Object Detection
on PASCAL VOC 2007
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D.
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes.
Unsupervised Deep Learning for Structured Shape Matching
We present a novel method for computing correspondences across 3D shapes using unsupervised learning.
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis.
Graphics
Weakly Supervised Deep Functional Map for Shape Matching
We show empirically minimum components for obtaining state of the art results with different loss functions, supervised as well as unsupervised.
Weakly Supervised Deep Functional Maps for Shape Matching
We show empirically the minimum components for obtaining state-of-the-art results with different loss functions, supervised as well as unsupervised.
Reinforcement learning with a network of spiking agents
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions.
Object-Aware Cropping for Self-Supervised Learning
This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image.
Learning 3D Human Pose from Structure and Motion
3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #24 on
Monocular 3D Human Pose Estimation
on Human3.6M
Monocular 3D Human Pose Estimation
Weakly-supervised Learning
VConv-DAE: Deep Volumetric Shape Learning Without Object Labels
Prior work has shown encouraging results on problems ranging from shape completion to recognition.
Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns
On studying recent state-of-the-art models on the ShARCQA task, we found indications that the models learn spurious clues/patterns in the dataset.
Geometric Matrix Completion: A Functional View
We propose a totally functional view of geometric matrix completion problem.
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons.
DYAN: A Dynamical Atoms-Based Network for Video Prediction
The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning.
Rate of Change Analysis for Interestingness Measures
We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties.
One Shot Joint Colocalization and Cosegmentation
This paper presents a novel framework in which image cosegmentation and colocalization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner.
Controlling Search in Very large Commonsense Knowledge Bases: A Machine Learning Approach
Results show that these methods lead to an order of magnitude reduction in inference time.
Deep Hierarchical Parsing for Semantic Segmentation
We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function.
Locally Scale-Invariant Convolutional Neural Networks
We show on a modified MNIST dataset that when faced with scale variation, building in scale-invariance allows ConvNets to learn more discriminative features with reduced chances of over-fitting.
Foreground Clustering for Joint Segmentation and Localization in Videos and Images
This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner.
Recursive Context Propagation Network for Semantic Scene Labeling
Then a top-down propagation of the aggregated information takes place that enhances the contextual information of each local feature.
A Sentence Is Worth a Thousand Pixels
We are interested in holistic scene understanding where images are accompanied with text in the form of complex sentential descriptions.
Exploration of Self-Propelling Droplets Using a Curiosity Driven Robotic Assistant
We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit.
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation
The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire.
On the Robustness of Human Pose Estimation
This paper provides a comprehensive and exhaustive study of adversarial attacks on human pose estimation models and the evaluation of their robustness.
Multi-Person 3D Human Pose Estimation from Monocular Images
Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #8 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Pose Estimation
3D Multi-Person Human Pose Estimation
Listwise Learning to Rank with Deep Q-Networks
Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query.
Towards Automatic Generation of Questions from Long Answers
Therefore, we propose a novel evaluation benchmark to assess the performance of existing AQG systems for long-text answers.
OpenEDS2020: Open Eyes Dataset
We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.
Price Optimization in Fashion E-commerce
Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
Neural Conversational QA: Learning to Reason vs Exploiting Patterns
Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage.
Matrix Decomposition on Graphs: A Functional View
We propose a functional view of matrix decomposition problems on graphs such as geometric matrix completion and graph regularized dimensionality reduction.
Communication and networking technologies for UAVs: A survey
With the advancement in drone technology, in just a few years, drones will be assisting humans in every domain.
Ensemble Attention Distillation for Privacy-Preserving Federated Learning
Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.
Learning Canonical Embedding for Non-rigid Shape Matching
This paper provides a novel framework that learns canonical embeddings for non-rigid shape matching.
On Learning Prediction-Focused Mixtures
Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks.
Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud
Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously.
Adapting Neural Machine Translation for Automatic Post-Editing
Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns.
Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care
In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time.
A Joint Learning Approach for Semi-supervised Neural Topic Modeling
Topic models are some of the most popular ways to represent textual data in an interpret-able manner.
Surya Namaskar: real-time advanced yoga pose recognition and correction for smart healthcare
Nowadays, yoga has gained worldwide attention because of increasing levels of stress in the modern way of life, and there are many ways or resources to learn yoga.
Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized.
Interactive Post-Editing for Verbosity Controlled Translation
We explore Interactive Post-Editing (IPE) models for human-in-loop translation to help correct translation errors and rephrase it with a desired style variation.
Federated Learning with Privacy-Preserving Ensemble Attention Distillation
We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work.
iPAL: A Machine Learning Based Smart Healthcare Framework For Automatic Diagnosis Of Attention Deficit/Hyperactivity Disorder (ADHD)
In this work, multiclass classification is performed on phenotypic data using an SVM model.
Determining Molecular Complexity using Assembly Theory and Spectroscopy
Determining the complexity of molecules has important applications from molecular design to understanding the history of the process that led to the formation of the molecule.
Decision-Focused Model-based Reinforcement Learning for Reward Transfer
Decision-focused (DF) model-based reinforcement learning has recently been introduced as a powerful algorithm that can focus on learning the MDP dynamics that are most relevant for obtaining high returns.
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
PBADet: A One-Stage Anchor-Free Approach for Part-Body Association
The detection of human parts (e. g., hands, face) and their correct association with individuals is an essential task, e. g., for ubiquitous human-machine interfaces and action recognition.
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation
Tumor segmentation from multi-modal brain MRI images is a challenging task due to the limited samples, high variance in shapes and uneven distribution of tumor morphology.
