Papers › Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and...

Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives

17 Apr 2024arXiv:2404.11317archive 2025-07-28

Zhangchi Feng, Richong Zhang, Zhijie Nie

The Composed Image Retrieval (CIR) task aims to retrieve target images using a composed query consisting of a reference image and a modified text. Advanced methods often utilize contrastive learning as the optimization objective, which benefits from adequate positive and negative examples. However, the triplet for CIR incurs high manual annotation costs, resulting in limited positive examples. Furthermore, existing methods commonly use in-batch negative sampling, which reduces the negative number available for the model. To address the problem of lack of positives, we propose a data generation method by leveraging a multi-modal large language model to construct triplets for CIR. To introduce more negatives during fine-tuning, we design a two-stage fine-tuning framework for CIR, whose second stage introduces plenty of static representations of negatives to optimize the representation space rapidly. The above two improvements can be effectively stacked and designed to be plug-and-play, easily applied to existing CIR models without changing their original architectures. Extensive experiments and ablation analysis demonstrate that our method effectively scales positives and negatives and achieves state-of-the-art results on both FashionIQ and CIRR datasets. In addition, our method also performs well in zero-shot composed image retrieval, providing a new CIR solution for the low-resources scenario. Our code and data are released at https://github.com/BUAADreamer/SPN4CIR.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2404.11317")

Code

Syntology Ran 3 of 4 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 3 ran with no contract checked.

By repository: official repository: 4 samples from 1 repository, 3 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

BUAADreamer/SPN4CIR officialmentioned in papermentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

4 samples harvested; 3 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran
1unverified

Licence: 0 of the 4 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from BUAADreamer/SPN4CIR. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

generate_randomized_fiq_caption BUAADreamer/SPN4CIR/blip24cir/data_utils.py official repository ran MIT (permissive) · 1f20723d4586980b · report
squarepad_transform BUAADreamer/SPN4CIR/blip24cir/data_utils.py official repository ran MIT (permissive) · a1580dd1883758f6 · report
targetpad_transform BUAADreamer/SPN4CIR/blip24cir/data_utils.py official repository ran MIT (permissive) · 9e2b18431cf6776f · report
collate_fn BUAADreamer/SPN4CIR/blip24cir/utils.py official repository unverified MIT (permissive) · d12ba8b8f88ca00d · report

Tasks

Contrastive LearningImage RetrievalLanguage ModellingLarge Language ModelRetrievalZero-Shot Composed Image Retrieval (ZS-CIR)

2 archive task tags without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Retrieval CIRR SPN4CIR (SPRC) (Recall@5+Recall_subset@1)/2 82.69 #2 of 17 Archive leaderboard report
Image Retrieval CIRR SPN4CIR (SPRC) Recall@10 90.87 #2 of 17 Archive leaderboard report
Image Retrieval Fashion IQ SPN4CIR (SPRC) (Recall@10+Recall@50)/2 66.41 #3 of 22 Archive leaderboard report
Image Retrieval Fashion IQ SPN4CIR (SPRC) Recall@10 56.37 #3 of 22 Archive leaderboard report
Zero-Shot Composed Image Retrieval (ZS-CIR) CIRR SPN4CIR (SPN-CC) R@5 65.42 #25 of 47 Archive leaderboard report
Zero-Shot Composed Image Retrieval (ZS-CIR) CIRR SPN4CIR (SPN-CC) Rsubset@1 64.87 #25 of 47 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections