Search Results for author:
Found 53 papers, 17 papers with code
Physics to the Rescue: Deep Non-line-of-sight Reconstruction for High-speed Imaging
Computational approach to imaging around the corner, or non-line-of-sight (NLOS) imaging, is becoming a reality thanks to major advances in imaging hardware and reconstruction algorithms.
ActionFormer: Localizing Moments of Actions with Transformers
Self-attention based Transformer models have demonstrated impressive results for image classification and object detection, and more recently for video understanding.
Ranked #1 on
Temporal Action Localization
on THUMOS’14
The CAMELS project: public data release
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning.
Virtuoso: Video-based Intelligence for real-time tuning on SOCs
First, the system does not consider energy consumption of the models while making a decision on which model to run.
RegionCLIP: Region-based Language-Image Pretraining
However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans.
3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image
Our key intuition is that style transfer and view synthesis have to be jointly modeled for this task.
Super-resolving Dark Matter Halos using Generative Deep Learning
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology.
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2, 000 distinct simulated universes at several cosmic times.
Robust marginalization of baryonic effects for cosmological inference at the field level
We train neural networks to perform likelihood-free inference from $(25\, h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project.
Multifield Cosmology with Artificial Intelligence
Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields.
Towards Non-Line-of-Sight Photography
Non-line-of-sight (NLOS) imaging is based on capturing the multi-bounce indirect reflections from the hidden objects.
Learning to Generate Scene Graph from Natural Language Supervision
To bridge the gap between images and texts, we leverage an off-the-shelf object detector to identify and localize object instances, match labels of detected regions to concepts parsed from captions, and thus create "pseudo" labels for learning scene graph.
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection
Modern deep learning models require large amounts of accurately annotated data, which is often difficult to satisfy.
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution
Using a multi-scale convolution algorithm, the input dimensionality reduction features were mined to obtain shallow features, which then served as inputs into a multi-scale graph convolution algorithm to construct the internal relationships between eigenvalues at different scales.
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter.
Egocentric Activity Recognition and Localization on a 3D Map
Our model takes the inputs of a Hierarchical Volumetric Representation (HVR) of the environment and an egocentric video, infers the 3D action location as a latent variable, and recognizes the action based on the video and contextual cues surrounding its potential locations.
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.
Prism: Private Verifiable Set Computation over Multi-Owner Outsourced Databases
This paper proposes Prism, a secret sharing based approach to compute private set operations (i. e., intersection and union), as well as aggregates over outsourced databases belonging to multiple owners.
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
The scalability of Nystr\"{o}mformer enables application to longer sequences with thousands of tokens.
Ranked #11 on
Semantic Textual Similarity
on MRPC
(F1 metric)
A Simple Baseline for Weakly-Supervised Scene Graph Generation
We investigate the weakly-supervised scene graph generation, which is a challenging task since no correspondence of label and object is provided.
Learning the Evolution of the Universe in N-body Simulations
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe.
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning
We propose a MIL-based method for WSI classification and tumor detection that does not require localized annotations.
ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles
In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios.
AI-assisted super-resolution cosmological simulations
Cosmological simulations of galaxy formation are limited by finite computational resources.
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Comprehensive Image Captioning via Scene Graph Decomposition
We address the challenging problem of image captioning by revisiting the representation of image scene graph.
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
Our core innovation is the learning of a region-phrase score function, based on which an image-sentence score function is further constructed.
In the Eye of the Beholder: Gaze and Actions in First Person Video
Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.
Interpretable and Accurate Fine-grained Recognition via Region Grouping
Our results compare favorably to state-of-the-art methods on classification tasks, and our method outperforms previous approaches on the localization of object parts.
Obscure: Information-Theoretically Secure, Oblivious, and Verifiable Aggregation Queries on Secret-Shared Outsourced Data -- Full Version
Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge.
Gradients as Features for Deep Representation Learning
We address the challenging problem of deep representation learning--the efficient adaption of a pre-trained deep network to different tasks.
Rotation method for accelerating multiple-spherical Bessel function integrals against a numerical source function
In analysing these datasets recomputation of these integrals a substantial number of times, for instance to update perturbation theory predictions or covariance matrices as the input linear power spectrum is changed, will be one piece in a Monte Carlo Markov Chain cosmological parameter search: thus the overall savings from our method should be significant.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video
Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.
The Quijote simulations
The Quijote simulations are a set of 44, 100 full N-body simulations spanning more than 7, 000 cosmological models in the $\{\Omega_{\rm m}, \Omega_{\rm b}, h, n_s, \sigma_8, M_\nu, w \}$ hyperplane.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework
To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data.
Attention Distillation for Learning Video Representations
To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.
Ranked #35 on
Action Recognition
on UCF101
Beyond Grids: Learning Graph Representations for Visual Recognition
Our method further learns to propagate information across all vertices on the graph, and is able to project the learned graph representation back into 2D grids.
Learning to Predict the Cosmological Structure Formation
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
Disconnected Covariance of 2-point Functions in Large-Scale Structure
We verify the analytic covariance against the sample covariance from the galaxy mock simulations in two test cases: (1) the power spectrum multipole covariance, and (2) the joint covariance of the projected correlation function and the correlation function multipoles.
Cosmology and Nongalactic Astrophysics
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video
We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera.
Sense-Aware Neural Models for Pun Location in Texts
In this paper, we focus on the task of pun location, which aims to identify the pun word in a given short text.
3D-RCNN: Instance-Level 3D Object Reconstruction via Render-and-Compare
Our method produces a compact 3D representation of the scene, which can be readily used for applications like autonomous driving.
Ranked #3 on
Vehicle Pose Estimation
on KITTI Cars Hard
(using extra training data)
Learning to Grasp Without Seeing
We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge.
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks
These ConvNet based edge detectors have approached human level performance on standard benchmarks.
nbodykit: an open-source, massively parallel toolkit for large-scale structure
The package is extensively documented at http://nbodykit. readthedocs. io, which also includes an interactive set of example recipes for new users to explore.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Image-language matching tasks have recently attracted a lot of attention in the computer vision field.
An optimal FFT-based anisotropic power spectrum estimator
Here, we present a faster, optimal means of using FFTs for this measurement.
Cosmology and Nongalactic Astrophysics
Learning Deep Structure-Preserving Image-Text Embeddings
This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities.
Ranked #14 on
Image Retrieval
on Flickr30K 1K test
Unsupervised Learning of Edges
In this work we present a simple yet effective approach for training edge detectors without human supervision.
Delving Into Egocentric Actions
We propose to utilize these mid-level egocentric cues for egocentric action recognition.
Gaze-Enabled Egocentric Video Summarization via Constrained Submodular Maximization
Motivated by these applications, this paper focuses on the problem of egocentric video summarization.
The Secrets of Salient Object Segmentation
The dataset design bias does not only create the discomforting disconnection between fixations and salient object segmentation, but also misleads the algorithm designing.
