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.
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.
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
| 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) |
Latest news and blog
Discover the latest news and research from the IBM Research AI Computer Vision Team.
IBM Research AI at CVPR 2019
Rogerio Feris | June 14, 2019
SpotTune: Transfer Learning through Adaptive Fine-Tuning
Rogerio Feris | June 14, 2019
IBM Research AI Advancing, Trusting, and Scaling Learning at ICLR
John R. Smith | May 2, 2019
Dialog-Based Interactive Image Retrieval
Xiaoxiao Guo and Hui Wu | February 27, 2019
Efficient Adversarial Robustness Evaluation of AI Models with Limited Access
Pin-Yu Chen and Sijia Liu | January 30, 2019
IBM Research Releases ‘Diversity in Faces’ Dataset to Advance Study of Fairness in Facial Recognition Systems
John R. Smith | January 29, 2019
Delta-Encoder: Synthesizing a Full Set of Samples From One Image
IBM Research AI | November 27, 2018
Restoring Balance in Machine Learning Datasets
IBM Research AI | October 10, 2018
Video Scene Detection Using Optimal Sequential Grouping
IBM Research AI | September 13, 2018
AI and Human Creativity Go Hand in Hand
IBM Research AI | October 19, 2018
Come do your best work
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.