Paper Citation Record · LEDGER

Composed Object Retrieval: Object-level Retrieval via Composed Expressions

As of 10 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2508.04424.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.04424 v3

Coverage vector

measured 50 of 50 reference resolution

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 3ff5a8ee-bbea-46b3-9394-257e93d7b780 · outbound

This paper cites Probabilistic embeddings for cross-modal retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Probabilistic embeddings for cross-modal retrieval

Reference 1

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Observation 56c43217-a4b7-478e-8d61-405f0a11bde7 · outbound

This paper cites A decade survey of content based image retrieval using deep learning.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions A decade survey of content based image retrieval using deep learning

Reference 2

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Observation 062bcc90-1591-479f-b662-40dbc750bc8f · outbound

This paper cites Image retrieval on real-life images with pre-trained vision-and-language models.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Image retrieval on real-life images with pre-trained vision-and-language models

Reference 3

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Observation 336c940b-3bd4-4680-87ae-23e597a15324 · outbound

This paper cites Composed image retrieval using contrastive learn- ing and task-oriented clip-based features.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Composed image retrieval using contrastive learn- ing and task-oriented clip-based features

Reference 4

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Observation 083b8db4-fc91-478d-ad9c-ab7673bc5ed8 · outbound

This paper cites Dy- namic weighted combiner for mixed-modal image retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Dy- namic weighted combiner for mixed-modal image retrieval

Reference 5

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Observation c87ed141-8d31-4f6b-8b52-31c3083bbf75 · outbound

This paper cites Microsoft coco: Common objects in context.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Microsoft coco: Common objects in context

Reference 6

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Observation 6f1f3474-7b1c-4e17-8ba3-f5bd3f2d4164 · outbound

This paper cites LVIS: A dataset for large vocabulary instance segmentation.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions LVIS: A dataset for large vocabulary instance segmentation

Reference 7

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Observation 076f00b1-c1b1-434a-98f7-ae31fd8b6fa5 · outbound

This paper cites Qwen2.5-VL Technical Report.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Qwen2.5-VL Technical Report

Reference 8

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This paper cites Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder

Reference 9

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Observation 27aeab5e-43d4-479e-b2da-3d19be0c89ea · outbound

This paper cites Sentence- level prompts benefit composed image retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Sentence- level prompts benefit composed image retrieval

Reference 10

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Observation ec42beac-acb0-4b26-841a-d71b2a0dd15e · outbound

This paper cites Improving composed image retrieval via contrastive learning with scaling positives and negatives.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Improving composed image retrieval via contrastive learning with scaling positives and negatives

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T00:04:56.566369Z

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Observation 116cd206-1a83-47cc-8dbb-d417848061ef · outbound

This paper cites Modality-Agnostic Attention Fusion for visual search with text feedback.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Modality-Agnostic Attention Fusion for visual search with text feedback

Reference 12

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Observation d7373451-312b-401c-b66d-242235bcb29f · outbound

This paper cites Dual compositional learning in interactive image retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Dual compositional learning in interactive image retrieval

Reference 13

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This paper cites Composing text and image for im- age retrieval-an empirical odyssey.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Composing text and image for im- age retrieval-an empirical odyssey

Reference 14

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Observation db4e325c-1676-4ace-928f-17d4dd63e782 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Learning transferable visual models from natural language supervision

Reference 15

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Observation 154933a2-5583-4f10-a9aa-ee399e322b22 · outbound

This paper cites Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Blip: Bootstrapping language-image pre-training for unified vision- language understanding and generation

Reference 16

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Observation 2877d753-2d3c-497e-9b82-7b0a38fd6e06 · outbound

This paper cites Target-guided composed image retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Target-guided composed image retrieval

Reference 17

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Observation 7d4d089f-958b-41b9-b48b-66c511193178 · outbound

This paper cites Data roaming and quality assessment for composed image retrieval.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Data roaming and quality assessment for composed image retrieval

Reference 18

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This paper cites Progressive learning for image retrieval with hybrid-modality queries.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Progressive learning for image retrieval with hybrid-modality queries

Reference 19

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This paper cites CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion

Reference 20

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This paper cites Align before fuse: Vision and language representation learning with momen- tum distillation.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Align before fuse: Vision and language representation learning with momen- tum distillation

Reference 21

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This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions Sigmoid loss for language image pre-training

Reference 23

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This paper cites Segment anything.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Segment anything

Reference 24

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions De- coupling zero-shot semantic segmentation

Reference 25

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This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Open-vocabulary semantic segmentation with mask-adapted clip

Reference 26

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions Scaling open-vocabulary image segmentation with image-level labels

Reference 27

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This paper cites Mask-adapter: The devil is in the masks for open-vocabulary segmentation.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Mask-adapter: The devil is in the masks for open-vocabulary segmentation

Reference 28

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This paper cites Detecting twenty-thousand classes using image-level supervision.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Detecting twenty-thousand classes using image-level supervision

Reference 29

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This paper cites Bi-directional training for composed image retrieval via text prompt learning.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Bi-directional training for composed image retrieval via text prompt learning

Reference 30

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Reference 31

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This paper cites Return 1 if all the conditions are met; otherwise, return 0.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions Return 1 if all the conditions are met; otherwise, return 0

Reference 32

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This paper cites This ensures clear and discriminative reference objects for retrieval tasks.

Composed Object Retrieval: Object-level Retrieval via Composed Expressions This ensures clear and discriminative reference objects for retrieval tasks

Reference 33

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions reference

Reference 36

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions reference,

Reference 38

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Composed Object Retrieval: Object-level Retrieval via Composed Expressions change the color to bright

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Reference 46

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Reference 47

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Reference 48

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This paper cites The Dice score on Test-base falls from 0.7703 to 0.6770 (a 12.11% drop), and on Test-novel, it drops from 0.7102 to 0.6137 (a 13.59% drop).

Composed Object Retrieval: Object-level Retrieval via Composed Expressions The Dice score on Test-base falls from 0.7703 to 0.6770 (a 12.11% drop), and on Test-novel, it drops from 0.7102 to 0.6137 (a 13.59% drop)

Reference 49

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Reference 50

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Pith citing papers

No inbound Pith citation observations are available.