Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2401.17868.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T12:25:38.758872Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 85449d0f-825a-4362-94c3-50ba742c4e74 · inbound
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization cites this paper.
Reclaiming Residual Knowledge: A Novel Paradigm to Low-Bit Quantization Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T22:28:31.063585Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation aecfe045-340b-480f-b08a-898d1b6cfd40 · inbound
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes cites this paper.
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 33
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-23T21:28:27.544027Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ccc915c8-cd69-426d-9175-e88c2c771754 · inbound
Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation cites this paper.
Dante: An Open Source Model Pre-Training and Fine-Tuning Tool for the Dafne Federated Framework for Medical Image Segmentation Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T05:35:24.326616Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 76a1b497-315e-4854-92ce-b291c412d3b2 · inbound
M$^4$-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection cites this paper.
M$^4$-SAM: Multi-Modal Mixture-of-Experts with Memory-Augmented SAM for RGB-D Video Salient Object Detection Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 52
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:17:22.993461Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0e8282d0-aa58-41d1-af78-98a66f16e2f5 · inbound
CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation cites this paper.
CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 42
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-30T12:34:39.041561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b0e43c58-5457-4418-b266-53f9da76207c · inbound
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation cites this paper.
Affordance2Action: Task-Conditioned Scene-level Affordance Grounding for Real-Time Manipulation Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T03:46:32.444244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.