About

Rogerio Feris

Research Manager, Computer Vision

LinkedIn Publications

Who they work with

Rogerio Schmidt Feris is a principal scientist and a research manager at the MIT-IBM Watson AI Lab. He joined IBM in 2006 after receiving a Ph.D. from the University of California, Santa Barbara. He has also worked as an Affiliate Associate Professor at the University of Washington and as an Adjunct Associate Professor at Columbia University. He has authored over 100 technical papers and has over 40 issued patents in the areas of computer vision, multimedia, and machine learning. Rogerio is a principal investigator of several projects within the MIT-IBM Watson AI Lab. His work has not only been published in top AI conferences (NeurIPS, CVPR, ICLR, ICCV, ECCV, SIGGRAPH), but has also been integrated into multiple IBM products, including Watson Visual Recognition, Watson Media, and Intelligent Video Analytics

Publications with the MIT-IBM Watson AI Lab

ConStruct-VL: Data-Free Continual Structured VL Concepts Learning

CVPR

Teaching Structured Vision & Language Concepts to Vision & Language Models

CVPR

CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning

CVPR

Learning to Grow Pretrained Models for Efficient Transformer Training

ICLR

Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

ICLR

How Transferable are Video Representations Based on Synthetic Data?

NeurIPS

FETA: Towards Specializing Foundation Models for Expert Task Applications

NeurIPS

Procedural Image Programs for Representation Learning

NeurIPS

VALHALLA: Visual Hallucination for Machine Translation

CVPR

Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data

CVPR

SimVQA: Exploring Simulated Environments for Visual Question Answering

CVPR

Targeted Supervised Contrastive Learning for Long-Tailed Recognition

CVPR

IA-RED^2 : Interpretability-Aware Redundancy Reduction for Vision Transformers

NeurIPS

Everything at Once – Multi-modal Fusion Transformer for Video Retrieval

CVPR

Unsupervised Domain Generalization by Learning a Bridge Across Domains

CVPR

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

NeurIPS

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

Computer Vision Computational neuroscience

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

ICCV Multimodal Learning

Dynamic Video Quantization for Efficient Inference

ICCV ICCV

A Broad Study on the Transferability of Visual Representations With Contrastive Learning

ICCV

Detector-Free Weakly Supervised Grounding by Separation

ICCV

Multimodal Clustering Networks for Self-Supervised Learning From Unlabeled Videos

ICCV

Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

CVPR Computer Vision

Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions

CVPR Computer Vision

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

AAAI Computer Vision

Semi-Supervised Action Recognition with Temporal Contrastive Learning

CVPR Computer Vision

StarNet: towards weakly supervised few-shot detection and explainable few-shot classification

AAAI Computer Vision

Fashion IQ: A New Dataset towards Retrieving Images by Natural Language Feedback

CVPR Computer Vision

Separating Skills and Concepts for Novel Visual Question Answering

CVPR

Fine-grained Angular Contrastive Learning with Coarse Labels

CVPR Computer Vision

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

ICLR Computer Vision

VA-RED^2: Video Adaptive Redundancy Reduction

ICLR Computer Vision

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Computer Vision Multimodal Learning

A Broader Study of Cross-Domain Few-Shot Learning

ECCV Computer Vision

OnlineAugment: Online Data Augmentation with Less Domain Knowledge

ECCV Computer Vision

TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification

ECCV Computer Vision

We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos

ECCV Computer Vision

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

ECCV Computer Vision

Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors

CVPR Computer Vision

RepMet: Representative-based metric learning for classification and one-shot object detection

Computer Vision

SpotTune: Transfer Learning through Adaptive Fine-tuning

Computer Vision Transfer Learning

LaSO: Label-Set Operations networks for multi-label few-shot learning

Computer Vision Few-shot Learning

Identifying Interpretable Action Concepts in Deep Networks

CVPR

Grounding Spoken Words in Unlabeled Video

CVPR

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Computer Vision

Co-regularized Alignment for Unsupervised Domain Adaptation

Transfer Learning Deep Learning

Delta-encoder: an effective sample synthesis method for few-shot object recognition

Computer Vision Few-shot Learning

Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation

CVPR Computer Vision

Dialog-based Interactive Image Retrieval

NeurIPS Computer Vision

Learning to Separate Object Sounds by Watching Unlabeled Video

Computer Vision

BlockDrop: Dynamic Inference Paths in Residual Networks

Computer Vision