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seqamlab

Sequence Analysis and Modeling Lab

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Posted on October 20, 2020October 20, 2020

Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images

This is a joint work by Jiatong Li, Fangda Han, Ricardo Guerrero and Vladimir Pavlovic. The paper is accepted by the 25th International Conference on Pattern Recognition (ICPR 2020).

Abstract

Increased awareness of the impact of food consumption on health … Read the rest

Posted on September 30, 2020September 30, 2020

New NSF Grant: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction

Happy to announce that we have been recently awarded a new NSF grant:

Collaborative Research: RI: Medium: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction

This $1M grant from the Division of Information & Intelligent Systems / Robust Intelligence … Read the rest

Posted on April 2, 2020April 4, 2020

CookGAN recognized by the CV/ML community

I’m very glad to have our work CookGAN recognized by the CV/ML community SyncedReview on Medium! We are working on building a demo webpage for it.

… Read the rest
Posted on February 24, 2020February 27, 2020

CookGAN: Meal Image Synthesis from Ingredients

Following the previous work that generates meal image from ingredients, we extends it with more experiments in our new paper that has been accepted by WACV 2020.

Paper

Arxiv: https://arxiv.org/abs/2002.11493

  • F. Han, R. Guerrero, and V. Pavlovic, “CookGAN: Meal Image
… Read the rest

Posted on December 19, 2019December 19, 2019

Samsung Best Paper Award 2019

Proud to have received a Samsung Best Paper Award for 2019 with my team at SAIC Cambridge.

Although only Minyoung and I are listed as the recipients, this work would not have been possible without my students Pritish Sahu … Read the rest

Posted on November 23, 2019

Keynote at 1st Chengdu International Forum on Frontier Medicine

Honored to give a keynote on AI & Nutrition at the 1st Chengdu Int’l Forum on Frontier Medicine.

… Read the rest
Posted on September 22, 2019October 23, 2019

Deep Cooking: Predicting Relative Food Ingredient Amounts from Images

This is a joint work by Jiatong Li, Ricardo Guerrero and Vladimir Pavlovic. The paper is accepted by the 5th International Workshop on Multimedia Assisted Dietary Management (MADiMa), ACM International Conference on Multimedia (ACMMM2019).

Abstract

In this paper, we study … Read the rest

Posted on September 22, 2019October 2, 2019

Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation

This is a joint work by Behnam Gholami, Pritish Sahu, Minyoung Kim, Vladimir Pavlovic

Our new paper on Domain Adaptation was accepted in Multi-Discipline Approach for Learning Concepts – Zero-Shot, One-Shot, Few-Shot, and Beyond and Beyond Workshop in conjunction with ICCV 2019.

Abstract

… Read the rest
Posted on September 22, 2019October 7, 2019

Fast Adaptation of Deep Models for Facial Action Unit Detection Using Model-Agnostic Meta-Learning

Mihee Lee, Ognjen (Oggi) Rudovic, Vladimir Pavlovic, and Maja Pantic

Abstract

Detecting facial action units (AU) is one of the fundamental steps in automatic recognition of facial expression of emotions and cognitive states. Though there have been a variety of … Read the rest

Posted on September 21, 2019October 2, 2019

Relevance Factor VAE: Learning and Identifying Disentangled Factors

Minyoung Kim, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic.

Abstract

We propose a novel VAE-based deep auto- encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all … Read the rest

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Recent Posts

  • “These Pizzas Do Not Exist”
  • Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images
  • New NSF Grant: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction
  • CookGAN Virtual Cooking Demo
  • CookGAN recognized by the CV/ML community

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    SEQAM Lab
    CBIM Center
    Rutgers University
    617 Bowser Road
    Piscataway, New Jersey 08854
    United States of America
    Phone: +1 (848) 445-8846
    Fax:  +1 (732) 445-0537

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