O-DRUM @ CVPR 2022

Workshop on Open-Domain Retrieval Under Multi-Modal Settings

in conjunction with CVPR 2022, New Orleans, June 20

Room 239, Ernest M Morial Convention Center


Video Recording: YouTube

Information Retrieval (IR) is an essential aspect of the internet era and improvements in IR algorithms directly lead to a better search experience for the end-user. IR also serves as a vital component in many natural language processing tasks such as open-domain question answering and knowledge and commonsense-based question answering, Recent advances in visual representation learning have also enabled image retrieval applications that have become a vital part of knowledge-based and commonsense visual question answering. Many datasets and IR algorithms have been developed to deal with input queries from a single modality, such as for document retrieval from text queries, image retrieval from text queries, text retrieval form video queries, etc. However, in many cases, the query may be multi-modal, for instance an image of a milkshake and a complementary textual description “restaurants near me” should return potential matches of nearby restaurants serving milkshakes. Similarly, sick patients may be able to input their signs and symptoms (for instance photographs of swelling and natural lanaguage descriptions of fever) in order to retrieve more information about their condition. Such functionality is desirable in situations where each modality communicates partial, yet vital information about the required output.

O-DRUM 2022 seeks to address this emerging topic area of research. The workshop aims to bring together researchers from information retrieval, natural language processing, computer vision, and knowledge representation and reasoning to address information retrieval with queries that may come from multiple modalities (such as text, images, videos, audio, etc.), or multiple formats (paragraphs, tables, charts, etc.).

Schedule

0800 - 0820 CDT Welcome and Introductory Remarks Man Luo / Tejas Gokhale
0820 - 0855 CDT Danqi Chen
Princeton University 		Dr Chen is an Assistant professor of Computer Science at Princeton University and co-lead of the Princeton NLP Group. She is also part of the larger Princeton AIML group and affiliated with Princeton Center for Statistics and Machine Learning (CSML). Her broad interests are in in natural language processing and machine learning, and her research is mostly driven by two goals: (1) developing effective and fundamental methods for learning representations of language and knowledge, and their interplay, and (2) building practical systems including question answering, information extraction and conversational agents. Learning Representations for Text Retrieval: What we Learned
0855 - 0930 CDT Xin (Eric) Wang
University of California, Santa Cruz 		Dr. Wang is an Assistant Professor of Computer Science and Engineering at UC Santa Cruz. His research interests include Natural Language Processing, Computer Vision, and Machine Learning, with an emphasis on building embodied AI agents that can communicate with humans using natural language to perform real-world multimodal tasks. (Multilingual) Fairness in Vision-and-Language Models
0930 - 1030 CDT Coffee Break and Poster Session
1030 - 1105 CDT Hao Tan
Adobe Research 		Dr. Tan is a Research Scientist at Adobe Research. He completed his PhD in 2021 from the University of North Carolina, advised by Mohit Bansal. He is broadly interested in vision and language research. His PhD dissertation made significant contributions to assigning language meaning to visual concepts, including cross-modal representation learning, cross-modal retrieval, and visual/language grounding. From Neural Encoders to the Neural Retriever
Multimodal retrieval is about estimating relevance. Encoder-based method uses separate encoders and then calculates the relevance score based on vector similarity. It is efficient but shows a performance gap to the slower cross-modal approach, which explicitly models the multimodal interactions. In this talk, I will present the ways to enhance the retrieval model in the past (through knowledge distillation), for now (through implicit cross-modal modules), and in the future (rebuild the traditional retrieval pipeline with neural networks).
1105 - 1140 CDT Diane Larlus
NAVER Labs Europe 		Dr Larlus is a Principal Research Scientist at Naver Labs Europe working on computer vision and machine learning, and a chair holder on Life-long representation learning within the MIAI AI research institute of Grenoble, working towards a semantic understanding of visual scenes. Her current interests are in lifelong learning, continual domain adaptation, and instance-level, semantic, and cross-modal visual search. Using Text in Computer Vision
Many computer vision tasks, including open-domain retrieval, become easier to tackle if some companion text is available, at train or at test time. In the first part of this talk, we will see how, using relatively small sets of captioned images, one can train effective visual representations from scratch. In a second part, we will consider several flavors of image retrieval, and discuss how each flavor can be tackled and even enhanced using textual information.
1140 - 1215 CDT Aniruddha Kembhavi
Allen Institute for AI 		Dr. Kembhavi leads PRIOR, the computer vision team at the Allen Institute for AI. He is also an Affiliate Associate Professor at the Computer Science & Engineering department at the University of Washington. His research interests are in research problems at the intersection of vision, language, and embodiment. Towards General Purpose Vision
1215 -- 1300 CDT Spotlight Talks and Q&A:
  • Niv Cohen et al., "This is my unicorn, Fluffy": Personalizing frozen vision-language representations [slides]
  • Guillaume Couairon et al., Embedding Arithmetic of Multimodal Queries for Image Retrieval [slides]
  • Marco Bertini et al. , Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features [slides]
  • Yue Yang et al., Induce, Edit, Retrieve: Language Grounded Multimodal Schema for Instructional Video Retrieval [slides]

Accepted Papers

The Proceedings are available via the CVF Open Access website . All workshop papers are also available below.


Call for Papers

We invite submissions related to the broad topic area of multi-modal retrieval, including but not limited to the following topic areas:

We encourage submissions of two types:

Submissions should be anonymized and formatted using the CVPR 2022 template. Accepted papers will be presented as posters during the workshop, where attendees, invited speakers and organizers can engage in discussion. We plan to highlight the best 3 papers via spotlight talks during the workshop session. We will give authors of all accepted papers an option to opt-in or opt-out of CVPR proceedings.

Important Dates:

♦ Submission Deadline:April 08, 2022 (Friday), 23:59 PDT
♦ Notification of Decision:2nd week of April
♦ Camera Ready Deadline:April 19, 2022 (Tuesday), 23:59 PDT
Submission website (CMT): https://cmt3.research.microsoft.com/ODRUM2022

Organizers


Man Luo
ASU
Tejas Gokhale
ASU
Chitta Baral
ASU
Damien Teney
Idiap
Kenneth Marino
Deepmind
Pratyay Banerjee
ASU

Somak Aditya
IIT Kharagpur
Tianlu Wang
Meta AI Research
Yezhou Yang
ASU
Zhiyuan Fang
ASU
Zhe Gan
Microsoft
Please contact Man Luo (mluo26@asu.edu) or Tejas Gokhale (tgokhale@asu.edu) for additional details
The workshop is supported by NSF grant 2132724 as part of Research, Education, and Outreach activities.

Website maintained by Tejas Gokhale