Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-21T07:18:57.039115Z
Paper Citation Record · LEDGER
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning
As of 7 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2605.20385.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
48 of 48 outbound references displayed
- verified exact12
- verified fuzzy36
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 016beb94-be7c-4fcf-96f1-3e825cddca78 · outbound
This paper cites Fully convolutional networks for semantic segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Fully convolutional networks for semantic segmentation
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.209508Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation dc237dfc-b4f6-44e2-8d09-f76b8b3d03bf · outbound
This paper cites Encoder- decoder with atrous separable convolution for semantic image segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Encoder- decoder with atrous separable convolution for semantic image segmentation
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.198968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b6f0ebf8-b783-47ef-a59e-15251082cb43 · outbound
This paper cites Segformer: Simple and efficient design for semantic segmentation with transformers.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Segformer: Simple and efficient design for semantic segmentation with transformers
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.206702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a813c43d-5b8c-46a9-b893-ca218e5e71de · outbound
This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Schwing, Alexander Kirillov, and Rohit Girdhar
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.203843Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5d5faa3c-5408-495d-b745-e0ccdf566f7f · outbound
This paper cites Segment anything.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Segment anything
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.201402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 681d364a-037e-48f3-8ad0-7dc5c6c0f97b · outbound
This paper cites Sam 3: Segment anything with concepts.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Sam 3: Segment anything with concepts
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.196580Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a42c3810-6598-4281-921b-75be663bff65 · outbound
This paper cites Context-independent and context-dependent information in concepts.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Context-independent and context-dependent information in concepts
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.264282Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fd648b85-63c4-4967-84ce-35355501672c · outbound
This paper cites Neural correlates of context- independent and context-dependent self-knowledge.Brainand Cognition, 125:23–31.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Neural correlates of context- independent and context-dependent self-knowledge.Brainand Cognition, 125:23–31
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.258896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4a557098-9187-4128-bef4-15ecda629317 · outbound
This paper cites Individual pattern representations are context indepen- dent,buttheircollectiverepresentationiscontextdependent.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Individual pattern representations are context indepen- dent,buttheircollectiverepresentationiscontextdependent
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.261388Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6ce66e71-9f69-4ac8-a53b-b5b3945c78ed · outbound
This paper cites Spider: a unified framework for context-dependent concept segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Spider: a unified framework for context-dependent concept segmentation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.254043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32e69853-526c-4581-812f-12ab69c9fc59 · outbound
This paper cites Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.756427Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3f21e113-da23-4823-a857-9e69c9901771 · outbound
This paper cites Seggpt: Towards segmenting everything in context.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Seggpt: Towards segmenting everything in context
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.249273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4cb3d230-b28c-4dea-a997-c00181bce85e · outbound
This paper cites Sam3-i: Segment anything with instructions.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Sam3-i: Segment anything with instructions
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.246903Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0a441029-53a9-470a-a482-5172f9d449cd · outbound
This paper cites Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.767662Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f48b71ec-24d0-40ff-a141-c3dbc2822433 · outbound
This paper cites Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.749161Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fcd04b4a-bd1c-46b1-917f-df15ada8f55a · outbound
This paper cites Lens: Learning to segment anything with unified reinforced reasoning.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Lens: Learning to segment anything with unified reinforced reasoning
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.244495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 248c9c68-f915-4973-a3f7-6ec4d10010ee · outbound
This paper cites Lisa: Reasoning segmentation via large language model.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Lisa: Reasoning segmentation via large language model
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.232047Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 25f8be5c-a041-43f6-b5e8-edd848ae06be · outbound
This paper cites Instructseg: Unifying instructed visual segmentation with multi-modal large language models.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Instructseg: Unifying instructed visual segmentation with multi-modal large language models
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.219862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ff9e118b-7d95-4e5d-859a-eba17704bbba · outbound
This paper cites Segagent: Exploring pixel understanding capabilities in mllms by imitating human annotator trajectories.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Segagent: Exploring pixel understanding capabilities in mllms by imitating human annotator trajectories
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.279866Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f49e25ea-93b2-4eb9-8d83-957be0c3fce5 · outbound
This paper cites Medsam-agent: Empowering interactive medical image segmenta- tion with multi-turn agentic reinforcement learning.arXivpreprint.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Medsam-agent: Empowering interactive medical image segmenta- tion with multi-turn agentic reinforcement learning.arXivpreprint
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.760217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0beefd03-c983-4d47-8f8b-3d16e5353938 · outbound
This paper cites Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.744672Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e78bb7de-31f8-4067-8789-b6d9ac4a7165 · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning The cityscapes dataset for semantic urban scene understanding
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.251781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6ec20c48-6019-4727-b8fa-76128796ef9e · outbound
This paper cites Agentrvos: Reasoning over object tracks for zero-shot referring video object segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Agentrvos: Reasoning over object tracks for zero-shot referring video object segmentation
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.732612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 25d4e8fe-8968-4926-92e0-225a51663694 · outbound
This paper cites Cot-seg: Rethinking segmentation with chain-of-thought reasoning and self-correction.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Cot-seg: Rethinking segmentation with chain-of-thought reasoning and self-correction
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T07:19:46.771292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6a7dae18-e32c-4e7b-97f4-879bcb767ff2 · outbound
This paper cites Glamm: Pixel grounding large multimodal model.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Glamm: Pixel grounding large multimodal model
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.277418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9f11d055-e56a-4c67-8951-6f53ec5e7c68 · outbound
This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Model-agnostic meta-learning for fast adaptation of deep networks
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.269881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5e9e917c-3da8-40e0-b709-866e11e5e194 · outbound
This paper cites On First-Order Meta-Learning Algorithms.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning On First-Order Meta-Learning Algorithms
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.763490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e7cbf96f-3381-453d-afbe-98a390494f4e · outbound
This paper cites Metaicl: Learning to learn in context.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Metaicl: Learning to learn in context
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.256633Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 387b6cd1-8592-47fd-a4c6-f653a496d570 · outbound
This paper cites Maml- en-llm: Model agnostic meta-training of llms for improved in-context learning.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Maml- en-llm: Model agnostic meta-training of llms for improved in-context learning
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.214595Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation db16f40e-0f4a-482c-9b39-2fd30c6d0499 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 30
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.728406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26248b46-9481-4eb1-a223-c105558aed0c · outbound
This paper cites Concrete Problems in AI Safety.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Concrete Problems in AI Safety
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.752589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 37024d76-ea68-4791-986c-4bc2e5775a57 · outbound
This paper cites Qwen2.5-VL Technical Report.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Qwen2.5-VL Technical Report
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.740489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c5ebc8f6-425f-4380-a984-197d93856230 · outbound
This paper cites Sam4mllm: Enhance multi-modal large language model for referring expression segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Sam4mllm: Enhance multi-modal large language model for referring expression segmentation
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.234403Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b21e4500-1b2f-4874-b377-76c3b240c71a · outbound
This paper cites Sam-r1: Leveraging sam for reward feedback in multimodal segmentation via reinforcement learning.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Sam-r1: Leveraging sam for reward feedback in multimodal segmentation via reinforcement learning
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.236976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0e0eada4-52d0-4aa1-aa36-550360f01bc3 · outbound
This paper cites Discriminativeperceptionviaanchoreddescription for reasoning segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Discriminativeperceptionviaanchoreddescription for reasoning segmentation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.241837Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 96ce9730-751b-429d-88e1-a56c05918629 · outbound
This paper cites Learning to detect salient objects with image-level supervision.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Learning to detect salient objects with image-level supervision
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.239484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f37278ed-5a75-4669-ab06-bd3b466e7451 · outbound
This paper cites Camou- flaged object detection.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Camou- flaged object detection
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.282354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 235f61ee-e315-4c82-b1cc-362d32923204 · outbound
This paper cites Fss-1000: A 1000-class dataset for few-shot segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Fss-1000: A 1000-class dataset for few-shot segmentation
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.227260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5c6682fa-5f57-4f34-9dec-c3dbf40de67a · outbound
This paper cites Migician: Revealing the magic of free-form multi-image grounding in multimodal large language models.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Migician: Revealing the magic of free-form multi-image grounding in multimodal large language models
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.222359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f855c1a2-833e-4efa-83ce-482cf24ab360 · outbound
This paper cites Re-thinking co-salient object detection.IEEE TPAMI, 44(8):4339–4354.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Re-thinking co-salient object detection.IEEE TPAMI, 44(8):4339–4354
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.274855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d9c3f9ec-0a34-4b02-9c8c-eb8e91904910 · outbound
This paper cites One-shot learning for semantic segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning One-shot learning for semantic segmentation
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.266997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 21cf35de-e8ed-44b6-9874-bcf27bffdd79 · outbound
This paper cites Segmenting transparent objects in the wild.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Segmenting transparent objects in the wild
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.217153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 865fdad6-40a6-4686-814b-7d29fcb7a62d · outbound
This paper cites Large-scale training of shadow detectors with noisily-annotated shadow examples.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Large-scale training of shadow detectors with noisily-annotated shadow examples
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.272453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 096909bb-8c39-433e-80f1-b6dbd51b527d · outbound
This paper cites Autocorrelation-aware aggregation network for salient object detection of strip steel surface defects.IEEE TIM, 72:1–12.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Autocorrelation-aware aggregation network for salient object detection of strip steel surface defects.IEEE TIM, 72:1–12
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.224909Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c79cf963-72b1-4ed8-945e-37c1d58e8f2a · outbound
This paper cites Pranet: Parallel reverse attention network for polyp segmentation.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Pranet: Parallel reverse attention network for polyp segmentation
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.229603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation cc982ec8-7af8-4a77-b3f4-0348a1eb15c9 · outbound
This paper cites Dataset of breast ultrasound images.Datain brief, 28:104863.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Dataset of breast ultrasound images.Datain brief, 28:104863
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.284761Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6bb23771-38b1-45d0-aadb-3bd4de5f2b66 · outbound
This paper cites Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
Reference 47
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T07:19:46.736797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6e02d98b-2022-4cc2-bdd6-31ae858e4c02 · outbound
This paper cites Decoupledweightdecayregularization.
ConceptSeg-R1: Segment Any Concept via Meta-Reinforcement Learning Decoupledweightdecayregularization
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-21T07:19:47.211782Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.