Image Retrieval with Multi-Modal Query

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (10 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Show and Tell: A Neural Image Caption Generator
  17 Nov 2014 74 repositories listed Syntology ran 13 of 34 samples · 21 unverified · 6 pointer-only (licence)
  Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.
• A simple neural network module for relational reasoning
  5 Jun 2017 20 repositories listed Syntology ran 3 of 7 samples · 4 unverified · 1 pointer-only (licence)
  Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
• FiLM: Visual Reasoning with a General Conditioning Layer
  22 Sep 2017 7 repositories listed Syntology ran 9 of 10 samples · 1 unverified · 7 pointer-only (licence)
  We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
• Composing Text and Image for Image Retrieval - An Empirical Odyssey
  18 Dec 2018 4 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input image.
• Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
  14 Nov 2022 1 repository listed Syntology ran 3 of 10 samples · 7 unverified
  The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively.
• Compositional Learning of Image-Text Query for Image Retrieval
  19 Jun 2020 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.
• Attributes as Operators: Factorizing Unseen Attribute-Object Compositions
  27 Mar 2018 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  In addition, we show that not only can our model recognize unseen compositions robustly in an open-world setting, it can also generalize to compositions where objects themselves were unseen during training.
• Automatic Spatially-aware Fashion Concept Discovery
  3 Aug 2017 1 repository listed
  This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites.
• Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction
  18 Nov 2015 1 repository listed
  We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.