Skip to content

Latest commit

 

History

History
281 lines (273 loc) · 20.6 KB

poster.md

File metadata and controls

281 lines (273 loc) · 20.6 KB
Raw
layout title permalink
page
CS231n Poster Session
/poster-session/

2017 Stanford CS231n Poster Session

  • Location: Bing Concert Hall
  • Parking: Parking information can be found here
  • Sponsor Setup Time: 11:15 am - 12:00 pm
  • Poster Session: 12:00 pm - 2:45 pm
  • Award Ceremony: 2:55 pm - 3:15 pm

The 2017 Stanford CS231N poster session will showcase projects in Convolutional Neural Networks for Visual Recognition that students have worked on over the past quarter. This year, 750 students will be presenting over 350 projects. The topics range from Generative Adversarial Networks (GANs), healthcare and medical imaging, art and style transfer, satellite imaging, self-driving cars, video understanding and more! See the complete list of projects and poster session map below to find the location of a specific poster. We will be awarding 10+ awards to the top posters! The top prizes will be $500+ in value! Stanford affiliates (faculty, staff, students, alumni) and their guests are welcome to attend. Catered food and refreshments will be made available over the course of the event. This poster session is made possible through the generous support of Benchmark, Andreessen Horowitz, Nvidia and Apple!

Bing Map: See the Project IDs in the list below and find them on the map

List of Projects (3 Digit Number is ID to Locate the Poster)

  • 101 Invasive Species Detection
  • 102 Scene Classification with Convolutional Neural Networks
  • 104 Adaptive Regularization for Neural Networks
  • 105 Image-based Product Recommendation System with Convolutional Neural Networks
  • 107 Metric Learning for Clustering Images from Unknown Classes
  • 108 Intra-Class and Inter-Class Feature Learning through Deep Metric Learning for Large Scale Image Retrieval
  • 110 Neural Combinatorial Optimization for Solving Jigsaw Puzzles
  • 112 Faster R-CNN with RoI Refinement
  • 113 CNNs for Object Recognition in Surveillance
  • 114 Greedy Layer-wise Training for Weakly-supervised Object Localization and Segmentation
  • 116 Design And Analysis of a Hardware CNN Accelerator
  • 118 XNOR-Net on FPGA
  • 119 Convolutional Neural Network Classification of Functional Shoe Types
  • 121 CHILDNet: Curiosity-driven Human-In-the-Loop Deep Network
  • 122 Automated Smart TV UI Performance Testing with Visual Recognition
  • 125 Labeling Images with thematic and emotional content
  • 126 Classifying U.S. Houses by Architectural Style
  • 128 Finding Protests in Social Media Data
  • 129 Deep Visual Learning of Reddit Images
  • 130 Fast Softmax Sampling for Deep Neural Networks
  • 131 Prototypical one-shot learning using high-dimensional embeddings
  • 133 Malicious Dropout
  • 134 Compression and Acceleration of CNN Training and Evaluation
  • 135 YOLO for real time object detection on mobile
  • 136 Identifying Architectural Styles by Convolutional Neural Network
  • 200 UAV Depth Perception from Visual Images using a Deep Convolutional Neural Network
  • 203 Depth Estimation from Single Image Using Convolutional Neural Networks
  • 204 Improving 3D Scene Reconstruction through Object Recognition and Segmentation
  • 205 Depth-Enhanced Classification Network
  • 207 Depth Regression from a Single Monocular Image using a Multi-Scale Deep Network
  • 209 Understanding Physical Geometry of a Scene from Single Monocular Images
  • 210 StereoPhonic: Depth From Stereo on Phones
  • 211 Deep Video Interpolation
  • 212 To Post or Not To Post: Using CNNs to Classify Social Media Worthy Images
  • 213 Item Removal Detection for Retail Environments with Neural Networks
  • 214 Extracting Kinematic Information Using Pose Estimation
  • 216 Assessing Driver Distraction from Real-Time Video
  • 217 Hand Gesture Recognition using 3D models and Convolutional Neural Network
  • 218 Hand Gesture Recognition using Image Processing and Convolutional Neural Network
  • 219 Mobile, Marker-less, 3D Body Pose Estimation
  • 220 Deep 3D Human Key Point Estimation
  • 221 Touchy Feely: Real-time Emotion Recognition
  • 222 Face detection and recognition with YOLO
  • 223 Reconstructing Obfuscated Human Faces
  • 224 Recognizing facial expressions using deep learning
  • 225 Detection of Hand Grasping Tasks for “Grab and Go” Groceries
  • 226 Ava Makeup
  • 227 Lip Reading Word Classification
  • 229 Gaze Capture with CNNs
  • 230 Deep Networks for Robust Depth Estimation with Single-Photon Sensors
  • 231 Facial Emotion Recognition for Wild Images
  • 300 Effectiveness of Style Transfer as a Data Augmentation Technique
  • 301 Historical and Modern Image-to-Image Translation with Generative Adversarial Networks
  • 302 Colorization using ConvNets and GAN
  • 304 Automatic Manga Colorization with Hint
  • 305 Generative Adversarial Networks for Custom Image Generation
  • 306 Evaluation of Image Completion Algorithm: Deep Convolutional Generative Adversarial Nets vs. Exemplar-Based Inpainting
  • 307 Autoencoders for Inverse Rendering
  • 308 Adversarial Generator-Encoder Networks Based Visual Recommender System
  • 309 Semi-supervised learning via adversarial training
  • 311 Chinese Painting Generation Using Deep Convolutional Generative Adversarial Networks
  • 312 Super Resolution on Video using GANs
  • 313 Using Generative Models for Semi-Supervised Learning
  • 314 Class-Conditional Super-resolution with GANs
  • 315 FlowGAN
  • 316 Generative Text to Image Synthesis with Applications to Reinforcement Learning in Minecraft
  • 317 Frame Interpolation Using Generative Adversarial Networks
  • 318 Can We Train Dogs and Humans Together: Hidden Distribution GANs
  • 319 Multi-task Learning on Multi-Spectral Images Using GANs for Predicting Poverty
  • 320 From 2D Sketch to 3D Shading
  • 322 Art Generation from text with GANs
  • 323 Re(live) Photos: Predicting Neighboring Frames with GANs
  • 324 Multi-instances text-to-image generation with StackGAN
  • 325 Plant Disease Detection with Deep Learning
  • 327 Label-Free Object Detection in Video
  • 328 Filling the Blanks: GANs vs RNNs
  • 329 Towards Facial Reconstruction from Sparse Data: Utilizing Encoder-Decoder Networks with GANs to Infer Facial Rotations
  • 330 maaGMA: Modified-Adversarial-Autoencoding Generator with Multiple Adversaries
  • 331 visual search by brushing
  • 400 Using Convolutional Neural Networks to Predict Completion Year of Fine Art Paintings
  • 401 Multi-style Transfer: Generalizing Style Transfer to an Artist
  • 402 Style Transfer of Images Incorporating Depth Perception and Object Segmentation
  • 403 Automatic Sketch Colourization
  • 404 Semantic Segmented Style Transfer
  • 405 Mixed Style Transfer
  • 406 Artist Identification with Convolutional Neural Networks
  • 407 Learning Instance Normalization Parameters for Real-Time Arbitrary Style Transfer
  • 409 Automatic image colorization using deep neural networks
  • 410 Classifying Rjiksmuseum Paintings by Artist
  • 411 From Renaissance to Pop: A Study of Artistic Eras using Deep CNNs
  • 412 Style transfer between two photographs
  • 414 ArtTalk: Labeling Images with Thematic and Emotional Content
  • 415 Labeling Paintings with Thematic and Emotional Content
  • 416 Localized Style Transfer Using Semantic Segmentation
  • 417 Smooth In-Image Style Transitions
  • 418 AutoColorisation
  • 419 Judging Thematic Similarity in Paintings
  • 420 Freehand Sketch Recognition
  • 421 EXIF Estimation With Convolutional Neural Networks
  • 423 Full Resolutional Video Compression Using Recurrent Convolutional Neural Networks
  • 424 HiDDeN: Hiding Data with Deep Networks
  • 425 Line Drawing Colorization
  • 426 DeepSynth: Synthesizing A Musical Instrument With Video
  • 428 Exploring Style Transfer
  • 500 Mice behaviour analysis in open field test
  • 501 Unsupervised Deep Learning with variational autoencoder for Interpretable Mammogram CBIR and Risk scoring
  • 502 Brain Tumor Segmentation
  • 503 Vision-Based Approach to Senior Healthcare: Depth-Based Activity Recognition with Convolutional Neural Networks
  • 505 Privacy-Preserving Knowledge Transfer for Hand Hygiene Detection
  • 506 Depth-Based Activity Recognition in ICUs Using Convolutional Neural Networks
  • 507 BURNED: Efficient and Accurate Burn Prognosis Using Deep Learning
  • 511 Differentiating Tumor Cells from Healthy Cells in a Tumor Biopsy using CNNs
  • 512 Multimodal Brain MRI Tumor Segmentation via Convolutional Neural Networks
  • 513 Accelerating dynamic magnetic resonance image reconstruction using deep convolutional neural networks
  • 514 Predict DNA methylation states from whole slide images of brain tumors
  • 515 Predicting Lung Cancer Incidence from CT Imagery
  • 516 Automatic Neuronal Cell Classification in Calcium Imaging with Convolutional Neural Networks
  • 517 GlimpseNet: Multi-Instance Generative Attention for Full Mammogram Diagnosis
  • 518 Deep Convolutional Neural Networks for Lung Cancer Detection
  • 519 Predicting therapeutic efficacy in Non Small Cell Lung Cancer
  • 520 Breast Cancer Image Segmentation: Cancer Cell vs Stroma
  • 521 MRI to MGMT: Predicting Drug Efficacy for Glioblastoma Patients
  • 523 Prediction of Head and Neck Cancer Submolecular Types from Pathology Images
  • 524 Automated Detection of Diabetic Retinopathy using Deep Learning
  • 525 Patch-based Head and Neck Cancer Subtype Classification
  • 526 Deep Neural Nets for Brain Tumor Segmentation
  • 527 Deep Learning for Chest X-ray abnormality detection
  • 528 Multiparametric MR Image Analysis for Prostate Cancer Assessment with Convolutional Neural Networks
  • 530 MR Contrast Prediction Using Deep Learning
  • 531 Brendan - A Deep Convolutional Network for Representing Latent Features of Protein-Ligand Binding Poses
  • 533 Using Deep Learning for Segmentation of Microscopy Images
  • 534 Quantum Annealing Assisted Deep Learning for Lung Cancer Detection
  • 536 Early Stage Integrated Circuit Design Efficacy Prediction using Congestion and Cell Density Images
  • 537 Geological scenario identification using seismic impedance data
  • 538 Variational Autoencoders for Classical Ising Models
  • 539 Convolutional Neural Networks for Pile Up Identification in ATLAS
  • 541 Deep Learning application to proton radiography analysis
  • 542 Wave-dynamics simulation using deep neural networks
  • 545 RouteAI: A Convolutional Neural Network based Router for Integrated Circuits
  • 547 Convolutional Neural Networks For Automated Surface-Wettability Characterization
  • 548 Data Classification with Residual Network for Single Particle Imaging Experiments
  • 550 Extraction of Building Footprints from Satellite Imagery
  • 551 Predicting Land Use and Atmospheric Conditions from Amazon Rainforest Satellite Imagery
  • 552 Temporal Poverty Prediction
  • 553 UAV Road Mapping Using Convolutional Neural Nets
  • 554 Motion Prediction from Trajectory and Top-Down Visual Data
  • 555 Understanding Localized Remote Sensing-Based Crop Yield Prediction using Convolutional Neural Networks
  • 556 Neighborhood Watch: Using CNNs to Predict Income Brackets from Google StreetView Images
  • 557 CNNs for wide-area precipitation estimation from geostationary satellite imagery
  • 558 Mapping Tidal Salt Marshes
  • 559 Predicting Health using Satellite Images
  • 561 Convolutional Neural Nets for Segmentation of Satellite Imagery
  • 563 Discovering Scenic Roads Using Neural Networks
  • 564 Rail Network Detection from Aerial Imagery using Deep Learning
  • 601 AI Attack: Learning to Play a Video Game Using Visual Inputs
  • 602 Deep Predictive Action Conditional Neural Network for Frame Prediction in Atari Games
  • 603 Playing Go without Game Tree Search Using Convolutional Neural Networks
  • 604 Learning a Visual State Representation for Generative Adversarial Imitation Learning
  • 605 Playing Geometry Dash with Deep Reinforcement Learning
  • 606 End-to-end models for task-oriented visual dialogue with reinforcement
  • 607 Classifying grocery items images using Convolutional Neural Networks
  • 608 Imitation Learning with THOR
  • 610 Differentiable Neural Computer for Maze Navigation
  • 611 Playing Atari Games with Deep Learning
  • 612 Trajectory-Aware Visual Navigation in Indoor Scenes with Target-driven Deep Reinforcement Learning
  • 614 Indoor Navigation with Imitation Learning
  • 616 Deep Q-Learning with OpenAI Gym
  • 617 A3C Methods for General Game Playing
  • 618 Deep Reinforcement Learning using Memory-based Approaches
  • 619 FoxNet: A Deep-Learning Agent for Nintendo’s Star Fox 64
  • 620 Target-Driven Navigation with Imitation Learning
  • 621 End-to-End Learning for Fighting Forest Fires (EELFFF)
  • 622 Teach Me To Tango - Fidelity Estimation of Visual Odometry Systems for Robust Navigation
  • 623 Indoor Target-driven Visual Navigation
  • 624 NeuralKart: A Real-Time Mario Kart 64 AI
  • 625 Understanding Driver’s Intention Using Convolutional Neural Networks
  • 626 Self-Driving Car Steering Angle Prediction Based on Image Recognition
  • 627 Object Detection and Its Implementation on Android Devices
  • 629 Convolutional Architectures for Self-Driving Cars
  • 630 Real-Time Multiple Object Tracking for Autonomous Cars
  • 631 Single Stage Detector for Object Detection
  • 632 Convolutional Neural Network Information Fusion based on Dempster-Shafer Theory for Urban Scene Understanding
  • 633 Street View Instance Segmentation using R-CNN models
  • 700 Detecting videos containing (child) pornography
  • 701 Superflow: Frame Prediction with Convolutional Neural Network
  • 702 YouTube8-M: Video Classification
  • 705 YouTube-8M Video Classificatoin
  • 707 YOLO-based Adaptive Window Two-stream Convolutional Neural Network for Video Classification
  • 708 Spotlight: A Smart Video Highlight Generator
  • 709 Video Understanding: From Video Classification to Video Captioning
  • 710 Selecting Youtube Video Thumbnails via Convolutional Neural Networks
  • 711 Google Cloud and YouTube-8M Video Understanding Challenge
  • 713 Prediction of Personality First Impression with Deep Bimodal LSTM
  • 714 Video frame Interpolation and extrapolation
  • 715 Using Convolutional Neural Networks to Classify Noisy Sports Videos
  • 716 Detecting Guns in Video Content
  • 717 Soccer Stats with Computer Vision
  • 718 Real-time Surveillance Video Analytics System
  • 800 Using Graphical Programming Languages Designed for Novices to Train Neural Networks to Understand and Write Simple Programs
  • 801 Siamese Convolutional Neural Networks for Authorship Verification
  • 804 Deep Convolutional Neural Networks for Automatic Speech Recognition
  • 805 CLEVR Advancements for High-Level Visual Reasoning Programs
  • 806 Pix2Sketch
  • 807 Testing Image Understanding through Question Answering
  • 808 Real-time Object detection
  • 810 Handwritten Text Recognition using Deep Learning
  • 811 Bilinear Pooling and Co-Attention Inspired Models for Visual Question Answering
  • 812 Applying NLP Deep Learning Ideas to Image Classification
  • 814 You CAN Judge a Book by its Cover
  • 815 Image to Latex
  • 816 Combining RNN and CNN to Understand the Usefulness of Yelp Reviews
  • 817 Where is Waldo, a deep-learning approach to general purpose template matching
  • 818 An Automated Art Historian
  • 819 Deep Bayesian Pragmatics for Image Captioning
  • 900 Predicting Deforestation in the Amazon
  • 902 Amazon Rainforest Satellite Image Labeling
  • 903 Understanding the Amazon from Space
  • 904 Understand Amazon Deforestation using Neural Network
  • 905 Multi-label Classification on Satellite Images of the Amazon Rainforest
  • 906 Deep Multi-Label Classification for High Resolution Satellite Imagery of Rainforests
  • 907 Classification of natural landmarks and human footprint of Amazon using satellite data
  • 908 Image Classification with Deep Learning
  • 909 Understanding the Amazon Basin from Space
  • 911 Deeprootz: Mapping the Amazon
  • 912 Understanding the Amazon from Space
  • 913 Understanding the Amazon from Space
  • 914 Understanding the Amazon from Space
  • 915 DeepRootz: Classifying satellite images of the Amazon rainforest
  • 916 Trainforest
  • 917 Land Cover Classification in the Amazon
  • 918 Tracking Human Impact on the Amazon rainforest
  • 919 Understanding the Amazon from Space
  • 920 Guiding the management of cervical cancer with convolutional neural networks
  • 921 Cervix Screening Classification for Cancer Preventive Treatment
  • 922 Deep Learning Approaches for Determining Optimal Cervical Cancer Treatment
  • 923 Cervix Type Classification Using Deep Learning and Image Classification
  • 924 Deep Learning for Cervical Cancer
  • 925 Identifying Cervix Types using Deep Convolutional Networks
  • 926 Exploring Methods on Tiny ImageNet Problem
  • 927 Wide Residual Network for the Tiny Image Net Challenge
  • 928 The Power of Inception: Tackling the Tiny ImageNet Challenge
  • 929 Unconstrained Handwriting Recognition
  • 930 Tiny ImageNet Challenge
  • 931 Deep Convolutional Neural Networks for ImageNet Classification
  • 933 Overwhelming Tiny ImageNet: Bayesian Optimization on Residual Networks
  • 934 A Dense Take on Inception
  • 935 Tiny ImageNet Challenge
  • 937 Classification on Tiny ImageNet
  • 938 Tiny ImageNet Challenge Investigating the Scaling of Inception Layers for Reduced Scale Classification Problems
  • 939 Attention Networks for ImageNet Classification
  • 940 Image Classification for Tiny ImageNet Challenge
  • 941 Analyzing Deforestation in the Amazon