Research
 

Computer Vision

All Work

S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
NeurIPSComputer Vision
 
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens
Computer VisionNeurIPS
 
A simpler path to better computer vision
A simpler path to better computer vision
MIT News
↗
In machine learning, synthetic data can offer real performance improvements
In machine learning, synthetic data can offer real performance improvements
MIT News
↗
Using sound to model the world
Using sound to model the world
MIT News
↗
Student-powered machine learning
Student-powered machine learning
MIT News
↗
Artificial intelligence system learns concepts shared across video, audio, and text
Artificial intelligence system learns concepts shared across video, audio, and text
MIT News
↗
Can an Image Classifier Suffice For Action Recognition?
Can an Image Classifier Suffice For Action Recognition?
ICLR
 
Will transformers take over artificial intelligence?
Will transformers take over artificial intelligence?
Quanta Magazine
↗
Putting AI in IoT chips? It’s a question of memory
Putting AI in IoT chips? It’s a question of memory
Tech Monitor
↗
Unlocking new doors to artificial intelligence
Unlocking new doors to artificial intelligence
MIT News
↗
Seven from MIT named 2022 Sloan Research Fellows
Seven from MIT named 2022 Sloan Research Fellows
MIT News
↗
TinyML is bringing neural networks to small microcontrollers
TinyML is bringing neural networks to small microcontrollers
TechTalks
↗
AI Researchers Fight Noise by Turning to Biology
AI Researchers Fight Noise by Turning to Biology
Quanta Magazine
↗
Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices
Tiny machine learning design alleviates a bottleneck in memory usage on internet-of-things devices
MIT News
↗
Machines that see the world more like humans do
Machines that see the world more like humans do
MIT News
↗
These locations may look eerily familiar, but none actually exist
These locations may look eerily familiar, but none actually exist
Fast Company
↗
AI Learns to Predict Human Behavior from Videos
AI Learns to Predict Human Behavior from Videos
Columbia Engineering
↗
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
ECCVComputer Vision
 
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
ICLRComputer Vision
 
VA-RED^2: Video Adaptive Redundancy Reduction
VA-RED^2: Video Adaptive Redundancy Reduction
ICLRComputer Vision
 
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
AAAIComputer Vision
 
Non-Adversarial Video Synthesis with Learned Priors
Non-Adversarial Video Synthesis with Learned Priors
CVPRComputer Vision
 
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
CVPRComputer Vision
 
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
ECCVComputer Vision
 
Semi-Supervised Action Recognition with Temporal Contrastive Learning
Semi-Supervised Action Recognition with Temporal Contrastive Learning
CVPRComputer Vision
 
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
AAAIComputer Vision
 
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos
ECCVComputer Vision
 
Fashion IQ: A New Dataset towards Retrieving Images by Natural Language Feedback
Fashion IQ: A New Dataset towards Retrieving Images by Natural Language Feedback
CVPRComputer Vision
 
Wasserstein Style Transfer
Wasserstein Style Transfer
AISTATSMachine Learning
 
GAN Compression: Efficient Architectures for Interactive Conditional GANs
GAN Compression: Efficient Architectures for Interactive Conditional GANs
CVPRComputer Vision
 
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
AAAIMachine Learning
 
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
CVPRComputer Vision
 
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
ICLRMachine Learning
 
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using Capsules
CVPRComputer Vision
 
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
CVPRComputer Vision
 
Why Do These Match? Explaining the Behavior of Image Similarity Models
Why Do These Match? Explaining the Behavior of Image Similarity Models
ECCVComputer Vision
 
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
ECCVComputer Vision
 
Fine-grained Angular Contrastive Learning with Coarse Labels
Fine-grained Angular Contrastive Learning with Coarse Labels
CVPRComputer Vision
 
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
ICLRMachine Learning
 
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
ICLRComputer Vision
 
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning.
RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning.
AAAIComputer Vision
 
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
MVFNet: Multi-View Fusion Network for Efficient Video Recognition
AAAIComputer Vision
 
Black-box Explanation of Object Detectors via Saliency Maps
Black-box Explanation of Object Detectors via Saliency Maps
CVPRComputer Vision
 
Anycost GANs for Interactive Image Synthesis and Editing
Anycost GANs for Interactive Image Synthesis and Editing
CVPRComputer Vision
 
AGENT: A Benchmark for Core Psychological Reasoning
AGENT: A Benchmark for Core Psychological Reasoning
ICMLArtificial Intelligence
 
Facebook’s New AI Teaches Itself to See With Less Human Help
Facebook’s New AI Teaches Itself to See With Less Human Help
Wired
↗
Is neuroscience the key to protecting AI from adversarial attacks?
Is neuroscience the key to protecting AI from adversarial attacks?
TechTalks
↗
Neuroscientists find a way to make object-recognition models perform better
Neuroscientists find a way to make object-recognition models perform better
MIT News
↗
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Computer VisionMultimodal Learning
 
Adversarial T-shirt! Evading Person Detectors in A Physical World
Adversarial T-shirt! Evading Person Detectors in A Physical World
ECCVComputer Vision
 
A Broader Study of Cross-Domain Few-Shot Learning
A Broader Study of Cross-Domain Few-Shot Learning
ECCVComputer Vision
 
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
ECCVComputer Vision
 
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification
ECCVComputer Vision
 
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
ECCVComputer Vision
 
RetrieveGAN AI tool combines scene fragments to create new images
RetrieveGAN AI tool combines scene fragments to create new images
VentureBeat
↗
Research Highlights: ExBERT
Research Highlights: ExBERT
InsideBIGDATA
↗
Here’s what’s stopping AI from reaching human-like understanding
Here’s what’s stopping AI from reaching human-like understanding
TheNextWeb
↗
Identifying a melody by studying a musician’s body language
Identifying a melody by studying a musician’s body language
MIT News
↗
Undergraduates develop next-generation intelligence tools
Undergraduates develop next-generation intelligence tools
MIT News
↗
CLEVRER: The first video dataset for neuro-symbolic reasoning
CLEVRER: The first video dataset for neuro-symbolic reasoning
Neuro-Symbolic AIComputer Vision
 
Reasoning about Human-Object Interactions through Dual Attention Networks
Reasoning about Human-Object Interactions through Dual Attention Networks
Computer Vision
 
LaSO: Label-Set Operations networks for multi-label few-shot learning
LaSO: Label-Set Operations networks for multi-label few-shot learning
Computer VisionFew-shot Learning
 
SpotTune: Transfer Learning through Adaptive Fine-tuning
SpotTune: Transfer Learning through Adaptive Fine-tuning
Computer VisionTransfer Learning
 
RepMet: Representative-based metric learning for classification and one-shot object detection
RepMet: Representative-based metric learning for classification and one-shot object detection
Computer Vision
 
ObjectNet: A bias-controlled dataset object recognition
ObjectNet: A bias-controlled dataset object recognition
Computer VisionNeurIPS
 
Moments in Time Dataset: one million videos for event understanding
Moments in Time Dataset: one million videos for event understanding
Computer VisionMultimodal Learning
 
Self-supervised Moving Vehicle Tracking with Stereo Sound
Self-supervised Moving Vehicle Tracking with Stereo Sound
Multimodal LearningComputer Vision
 
The sound of motions
The sound of motions
Computer Vision
 
TSM: Temporal Shift Module for Efficient Video Understanding
TSM: Temporal Shift Module for Efficient Video Understanding
Efficient AIComputer Vision
 
Graph Convolutional Networks for Temporal Action Localization
Graph Convolutional Networks for Temporal Action Localization
Graph Deep LearningComputer Vision
 
Why computer vision algorithms need new benchmarks
Why computer vision algorithms need new benchmarks
Tech Talks
↗
The mind-bending confusion of ‘hammer on a bed’ shows computer vision is far from solved
The mind-bending confusion of ‘hammer on a bed’ shows computer vision is far from solved
The Verge
↗
Big-Little-Video-Net: Work smarter, not harder, for video understanding
Big-Little-Video-Net: Work smarter, not harder, for video understanding
Computer Vision
 
This object-recognition dataset stumped the world’s best computer vision models
This object-recognition dataset stumped the world’s best computer vision models
MIT News
↗
Cross-channel Communication Networks
Cross-channel Communication Networks
NeurIPSDeep Learning
 
Watch, Reason and Code: Learning to Represent Videos Using Program
Watch, Reason and Code: Learning to Represent Videos Using Program
Computer Vision
 
This Technique Can Make It Easier for AI to Understand Videos
This Technique Can Make It Easier for AI to Understand Videos
Wired
↗
Powerful computer vision algorithms are now small enough to run on your phone
Powerful computer vision algorithms are now small enough to run on your phone
MIT Technology Review
↗
Faster video recognition for the smartphone era
Faster video recognition for the smartphone era
MIT News
↗
MIT-IBM developed a faster way to train video recognition AI
MIT-IBM developed a faster way to train video recognition AI
Engadget
↗
Local Unsupervised Learning for Image Analysis
Local Unsupervised Learning for Image Analysis
Unsupervised LearningComputer Vision
 
Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering
Beyond RNNs: Positional Self-Attention with Co-Attention for Video Question Answering
Computer VisionTime Series
 
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Neuro-Symbolic AIComputer Vision
 
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Computer Vision
 
Weakly Supervised Dense Event Captioning in Videos
Weakly Supervised Dense Event Captioning in Videos
Computer Vision
 
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
Computer VisionTime Series
 
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Neuro-Symbolic AIComputer Vision
 
Machine learning system tackles speech and object recognition, all at once
Machine learning system tackles speech and object recognition, all at once
MIT News
↗
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Computer VisionFew-shot Learning
 
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation
CVPRComputer Vision
 
Dialog-based Interactive Image Retrieval
Dialog-based Interactive Image Retrieval
NeurIPSComputer Vision
 
The Sound of Pixels
The Sound of Pixels
Multimodal LearningComputer Vision
 
Learning to Separate Object Sounds by Watching Unlabeled Video
Learning to Separate Object Sounds by Watching Unlabeled Video
Computer Vision
 
Artificial intelligence in action
Artificial intelligence in action
MIT News
↗
BlockDrop: Dynamic Inference Paths in Residual Networks
BlockDrop: Dynamic Inference Paths in Residual Networks
Computer Vision