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Computer Vision

IBM Research is a leading player in the quest to give AI systems sight. We’re enabling Watson, IBM's AI platform, to interpret visual content as easily as it does text.

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About us

The field of computer vision has been transformed by the introduction of deep learning. State-of-the-art computer vision systems can now achieve superhuman accuracy and speed for certain tasks in image recognition and analysis. But these systems are still far from truly understanding what they see and making intelligent use of visual data. We aim to advance computer vision analysis from static scenes and images toward dynamic scenes and to integrate audio-visual perception, eventually enabling these systems to understand video input.

CVPR 2019

IBM Research AI is expanding AI’s Field of Computer Vision at CVPR 2019

 

Focus areas

IBM Research AI is enabling Watson, IBM's AI platform, to interpret visual content as easily as it does text.

 

Visual learning with limited labeled data

Multimodal video understanding 

Vision and language

Object detection

Skin image analysis 

Assistive technology  

Efficient visual analysis

 

Featured work

Learning More from Less

SpotTune: Transfer Learning through Adaptive Fine-Tuning

IBM Research, in collaboration with University of California, San Diego and University of Texas at Austin, created a novel adaptive fine-tuning method called SpotTune that automatically decides which layers of a model should be frozen or fine-tuned.

Multimodal Video Understanding

Workshop on Multi-modal Video Analysis and Moments in Time Challenge

The Workshop on Multi-modal Video Analysis and Moments in Time Challenge at ICCV 2019 aims to particularly focus on modeling, understanding, and leveraging the multi-modal nature of video.

Image Retrieval

Fashion Interactive Queries demo and challenge

IBM Research proposes a new natural language-based system for interactive, fine-grained image retrieval. This proposal is a framework of an image retrieval system which learns to seek natural language feedback from the user and iteratively refines the retrieval result.

Identifying Skin Cancer

Skin lesion analysis towards melanoma detection

Recently, we’ve been developing techniques in computer vision that could one day enable clinical staff to use pictures to help them screen for disease. Our vision is that taking pictures to diagnose melanoma might one day be as routine as drawing blood to detect other diseases.

Publications

The IBM Research AI Computer Vision team aims to advance computer vision analysis from static scenes and images toward dynamic scenes and to integrate audio-visual perception, eventually enabling these systems to understand video input. Our research has been recognized at major conferences such as CVPR, NeurIPS, and ICLR.

Please explore all of our computer vision research papers

All publications

TITLE RESEARCH AREA VENUE ACCESS
LaSO: Label-Set Operations Networks for Multi-label Few-shot Learning Visual learning with limited labeled data CVPR (2019)
SpotTune: Transfer Learning Through Adaptive Fine-Tuning Visual learning with limited labeled data CVPR (2019)
RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection Visual learning with limited labeled data CVPR (2019)
Transferable AutoML by Model Sharing Over Grouped Datasets Visual learning with limited labeled data CVPR (2019)
Adversarial Semantic Alignment for Improved Image Captions Vision and language CVPR (2019)
Dialog-based Interactive Image Retrieval Vision and Language NeurIPS (2018)
Delta-Encoder: an Effective Sample Synthesis Method for Few-shot Object Recognition Visual learning with limited labeled data NeurIPS (2018)
Moments in Time dataset: one million videos for event understanding Multimodal video understanding TPAMI (2019)
Automatic Curation of Sports Highlights using Multimodal Excitement Features Multimodal video understanding TMM (2018)
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC) Skin image analysis ISBI (2018)

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We are looking for talented researchers who are as passionate as we are about artificial intelligence, advancing science, and inventing the next generation of intelligent machines.

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