Paper Citation Record · LEDGER

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals

As of 22 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2411.13774.

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

pith.paper-citation-record.v1
2411.13774 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:59:25.729680Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

verified exact2
verified fuzzy6
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e16be4dd-0cb9-4c06-b555-1aa81cab0d8d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.367219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d421ed1a-7691-4b06-a909-2c7429a334b9 · outbound

This paper cites Language Models are Few-Shot Learners.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Language Models are Few-Shot Learners

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.601189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a27a341f-0d62-4a9e-aef5-522f36154f85 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals LLaMA: Open and Efficient Foundation Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.606924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ddaf4c3-ff18-4ac8-b305-bbc6a954afce · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Masked Autoencoders Are Scalable Vision Learners

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.614831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 165429a1-bbb9-4546-a1c6-2bc42c7e0309 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals DINOv2: Learning Robust Visual Features without Supervision

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.620826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee11981-d049-406f-99f7-2c2b9602c525 · outbound

This paper cites Generative Adversarial Networks.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Generative Adversarial Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.626093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e0f5f58-1887-40b3-b6d7-e4a61e03e809 · outbound

This paper cites Conditional Generative Adversarial Nets.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Conditional Generative Adversarial Nets

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.631026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aed892f-1d44-4054-81b6-faa2a84f6dce · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals High-Resolution Image Synthesis with Latent Diffusion Models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.350506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea3b9286-0adf-4a01-8223-b4a60c875481 · outbound

This paper cites Segment Anything.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Segment Anything

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.644892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7549e8c7-ec05-416b-9ffd-aed3fed4a5df · outbound

This paper cites Segment Everything Everywhere All at Once.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Segment Everything Everywhere All at Once

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.650704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33fe7f9b-fd74-42e8-9ed8-c69a780ca3ce · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals SAM 2: Segment Anything in Images and Videos

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.656088Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40810d27-c125-48bf-a2e0-bb5e010fc214 · outbound

This paper cites SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.661593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad7b505-a5f1-44c0-906a-aa64b76c2f01 · outbound

This paper cites Visual In-Context Prompting Zero-shot Video Object and Part Segmentation Visual Prompting Referring Segmentation 8 Visual Prompting Generic Segmentation,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Visual In-Context Prompting Zero-shot Video Object and Part Segmentation Visual Prompting Referring Segmentation 8 Visual Prompting Generic Segmentation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.335443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7f6050ec-ebfe-4908-a096-720255d1d55f · outbound

This paper cites VRP-SAM: SAM with Visual Reference Prompt,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals VRP-SAM: SAM with Visual Reference Prompt,

Reference 14

Resolution: verified exact; raw_fallback, observed 2026-08-12T15:59:26.072673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5598ab61-4f1c-41c3-94fc-f24d98ac485a · outbound

This paper cites Personalize Segment Anything Model with One Shot,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Personalize Segment Anything Model with One Shot,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.317262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4146c1b4-9cbd-452a-bcdc-b8d519f7a2bd · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.686681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02f6555c-e801-4fc1-96bf-1b7bb5328afb · outbound

This paper cites Exploring Effective Factors for Improving Visual In-Context Learning,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Exploring Effective Factors for Improving Visual In-Context Learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.691998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81789812-47d3-48e9-bf42-93fb616e8534 · outbound

This paper cites What Makes Good Examples for Visual In-Context Learning?.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals What Makes Good Examples for Visual In-Context Learning?

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.697007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48db5922-3f01-4a92-82d9-3e0a47912000 · outbound

This paper cites Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:59:25.821934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6f68032e-8d71-416c-8318-183130473073 · outbound

This paper cites Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.708560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96e2a2eb-2e9e-4ba9-8783-18dac8ae93ae · outbound

This paper cites A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.301235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2c7c7367-8766-48ef-8a7f-99b8d46749f1 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals SegGPT: Segmenting Everything In Context

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.720068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 629c7436-845e-4dc8-8bac-4a14c7255885 · outbound

This paper cites Matching Networks for One Shot Learning.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Matching Networks for One Shot Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.725245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7a6aec9-aef0-4732-ae70-20ea1087ee0f · outbound

This paper cites Panoptic Segmentation,.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Panoptic Segmentation,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:59:26.285532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ee3c7c92-3233-49da-b20f-0317ca922f72 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.595425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fad78dec-153a-41a3-a6af-90cdb5002994 · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals High-Resolution Image Synthesis with Latent Diffusion Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.640150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99846db9-c6ca-42b2-a6b4-e8d1d4d29a9e · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals Personalize Segment Anything Model with One Shot

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T15:59:25.680542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.