Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:28:02.289894Z
Paper Citation Record · LEDGER
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model
As of 18 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2505.04861.
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 2026-08-18T06:34:40.430872+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
63 of 63 outbound references displayed
- verified exact2
- verified fuzzy43
- unresolved18
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 87245482-f018-47c7-ad17-436aa2a9bc53 · outbound
This paper cites Crowd-SAM: Sam as a smart annotator for object detection in crowded scenes.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Crowd-SAM: Sam as a smart annotator for object detection in crowded scenes
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.141859Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8727467c-24cb-4141-a5ba-e9b9d0c9fb9e · outbound
This paper cites DearKD: Data-efficient early knowledge distillation for vision transformers.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model DearKD: Data-efficient early knowledge distillation for vision transformers
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.130374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 762bec37-1efa-4c18-b0e4-59ac1260c066 · outbound
This paper cites An Effective Information Theoretic Framework for Channel Pruning.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model An Effective Information Theoretic Framework for Channel Pruning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.067796Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6604d28-af0c-4c8c-bd57-3dda960a38c9 · outbound
This paper cites Tracking any- thing with decoupled video segmentation.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Tracking any- thing with decoupled video segmentation
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.120419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 9b04cf27-bfca-413d-85f1-58cbb7a86885 · outbound
This paper cites Low-bit quantization of neural networks for efficient inference.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Low-bit quantization of neural networks for efficient inference
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.110395Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a7b910ed-8c97-4d8d-b58d-c40f64b690a5 · outbound
This paper cites Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Mixed-Precision Quantization for Deep Vision Models with Integer Quadratic Programming
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T23:28:02.519114Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ffce265c-3407-4dbb-9e8f-76a9c42916d4 · outbound
This paper cites CVXPY: A python-embedded modeling language for convex op- timization.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model CVXPY: A python-embedded modeling language for convex op- timization
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.100183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b2a92965-945d-48ea-8dfa-2a0771199528 · outbound
This paper cites Towards accurate post-training quan- tization for vision transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Towards accurate post-training quan- tization for vision transformer
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.088374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 45c4045a-20fe-43d4-b6cf-569bd80fe0b4 · outbound
This paper cites HAWQ: Hessian aware quantization of neural networks with mixed-precision.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model HAWQ: Hessian aware quantization of neural networks with mixed-precision
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.074854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b3aed52d-bee4-451f-a963-c73f943c5dc9 · outbound
This paper cites HAWQ- V2: Hessian aware trace-weighted quantization of neural networks.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model HAWQ- V2: Hessian aware trace-weighted quantization of neural networks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.061211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 02ca7676-1cbc-473b-82a1-bf21b0cdf50c · outbound
This paper cites Layer-wise Model Pruning based on Mutual Information.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Layer-wise Model Pruning based on Mutual Information
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.098410Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ec90ce1-e24a-412a-b426-05d1178a41e7 · outbound
This paper cites YOLOX: Exceeding YOLO Series in 2021.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model YOLOX: Exceeding YOLO Series in 2021
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.102310Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1423703a-5f23-4b71-bba5-4ff57a284836 · outbound
This paper cites Soft filter pruning for accelerating deep convolutional neural networks.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Soft filter pruning for accelerating deep convolutional neural networks
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.046898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation da1d4575-451f-4307-b463-ec9838119404 · outbound
This paper cites Quantization and train- ing of neural networks for efficient integer-arithmetic- only inference.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Quantization and train- ing of neural networks for efficient integer-arithmetic- only inference
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.035141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 937a8c48-4077-4614-900d-c3410adb6c93 · outbound
This paper cites Detrs with hybrid matching.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Detrs with hybrid matching
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.024578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2b24f66c-382e-4a7d-8d89-40c76b464843 · outbound
This paper cites Segment anything in high quality.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Segment anything in high quality
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.013798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 826d2e52-2311-490e-9552-cb20c1adf2a6 · outbound
This paper cites SAM-Net: self-attention based feature matching with spatial transformers and knowledge distillation.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model SAM-Net: self-attention based feature matching with spatial transformers and knowledge distillation
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:03.003238Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a73d9e03-755b-49f4-a4eb-8be5e406e217 · outbound
This paper cites Segment anything.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Segment anything
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.991855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 26f9bb15-d40d-441c-9b23-e1c1edbbc359 · outbound
This paper cites Optimizing expo- nent bias for sub-8bit floating-point inference of fine- tuned transformers.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Optimizing expo- nent bias for sub-8bit floating-point inference of fine- tuned transformers
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.979970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation be9baadb-e2a4-465e-b4da-10f2de65436d · outbound
This paper cites FlexRound: Learnable rounding based on element-wise division for post-training quantiza- tion.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model FlexRound: Learnable rounding based on element-wise division for post-training quantiza- tion
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.970630Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0b38942a-0138-4ed2-9008-98acfc8989bc · outbound
This paper cites Differentiable Search for Finding Optimal Quantization Strategy.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Differentiable Search for Finding Optimal Quantization Strategy
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T23:28:02.482032Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 778a77cf-f79b-4571-8701-800b9b4fb17a · outbound
This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.139392Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6d7ae2b-9868-4295-ba30-5b139a6bfa27 · outbound
This paper cites RepQ-ViT: Scale reparameterization for post- training quantization of vision transformers.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model RepQ-ViT: Scale reparameterization for post- training quantization of vision transformers
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.961164Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e546f497-16eb-496a-b621-5e7db663cdf5 · outbound
This paper cites Knowledge distillation via the target- aware transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Knowledge distillation via the target- aware transformer
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.951895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dd113ceb-0e32-4400-a014-3c0772020a33 · outbound
This paper cites Microsoft COCO: Com- mon objects in context.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Microsoft COCO: Com- mon objects in context
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.942333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0c55ba2e-054e-4058-8588-da3128fe33e9 · outbound
This paper cites FQ-ViT: Post-training quantization for fully quantized vision transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model FQ-ViT: Post-training quantization for fully quantized vision transformer
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.931128Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e3f4fe7d-36db-41c6-aa7d-fb93452138e5 · outbound
This paper cites Perceptual-sensitive gan for generating adversarial patches.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Perceptual-sensitive gan for generating adversarial patches
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.919526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 60b8fc4d-55d2-4e55-89b8-76c79214c54d · outbound
This paper cites PD-Quant: Post- training quantization based on prediction difference metric.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model PD-Quant: Post- training quantization based on prediction difference metric
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.907964Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b5e210a4-a033-4087-afaa-ac6858d2a029 · outbound
This paper cites PQ-SAM: Post-training quantization for segment anything model.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model PQ-SAM: Post-training quantization for segment anything model
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.896263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 869a468e-55ca-437e-af8e-4d5ea3ee69f6 · outbound
This paper cites NoisyQuant: Noisy bias-enhanced post-training activation quantization for vision transformers.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model NoisyQuant: Noisy bias-enhanced post-training activation quantization for vision transformers
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.885348Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 198532d3-7770-49b5-b133-20bdb96dd04b · outbound
This paper cites Post-training quantization for vision transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Post-training quantization for vision transformer
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.874324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 91c569e1-4fe7-4e0c-8e1f-1f2d4c9bc7b3 · outbound
This paper cites AutoQ: Automated kernel-wise neural network quantization.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model AutoQ: Automated kernel-wise neural network quantization
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.862760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d0308f70-2c16-4dc1-9523-a5f90414c966 · outbound
This paper cites DeepBurning-MixQ: An open source mixed-precision neural network accelerator de- sign framework for fpgas.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model DeepBurning-MixQ: An open source mixed-precision neural network accelerator de- sign framework for fpgas
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.851148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 08950e12-2a66-4fde-a09b-a07001a3a323 · outbound
This paper cites PTQ4SAM: Post-training quan- tization for segment anything.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model PTQ4SAM: Post-training quan- tization for segment anything
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.839521Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d130b590-9144-459a-961e-316810ed5fba · outbound
This paper cites Seg- ment anything model for medical image analysis: an experimental study.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Seg- ment anything model for medical image analysis: an experimental study
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.828190Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4b00c065-b5f4-4337-a200-8a62d975e1a5 · outbound
This paper cites SAM-PM: Enhancing video camouflaged ob- ject detection using spatio-temporal attention.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model SAM-PM: Enhancing video camouflaged ob- ject detection using spatio-temporal attention
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.817261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation dbd9b930-7882-44cd-b02f-3f7915aa9245 · outbound
This paper cites Up or down? adaptive rounding for post-training quantiza- tion.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Up or down? adaptive rounding for post-training quantiza- tion
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.806977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 78491f87-c2c5-4cc0-ae30-e0298b320b40 · outbound
This paper cites LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model LRP-QViT: Mixed-Precision Vision Transformer Quantization via Layer-wise Relevance Propagation
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.196619Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac1d3947-90c0-4c2c-b3ed-ffc1da70f673 · outbound
This paper cites Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Mix-QViT: Mixed-Precision Vision Transformer Quantization Driven by Layer Importance and Quantization Sensitivity
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.200399Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdc24428-ec97-400c-9506-925ee5252303 · outbound
This paper cites Faster R-CNN: Towards real-time object detec- tion with region proposal networks.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Faster R-CNN: Towards real-time object detec- tion with region proposal networks
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.784740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation de41e63d-8969-41df-aac8-66040cf3d9d2 · outbound
This paper cites Quantized-ViT efficient training via fisher matrix regularization.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Quantized-ViT efficient training via fisher matrix regularization
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.763088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f129aaa3-b021-4511-9bd6-80ac1456277d · outbound
This paper cites Anything-3D: Towards Single-view Anything Reconstruction in the Wild.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Anything-3D: Towards Single-view Anything Reconstruction in the Wild
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.211151Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1b7d19e-ddae-4d5d-bf61-3ed262d0504a · outbound
This paper cites TinySAM: Pushing the Envelope for Efficient Segment Anything Model.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.214828Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32fe6162-da8d-4c1e-a482-bdf072027a91 · outbound
This paper cites MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.218818Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5490b35-8ba6-45c6-87e4-604d7c2c9f2a · outbound
This paper cites Mixed-precision neural network quantization via learned layer-wise importance.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Mixed-precision neural network quantization via learned layer-wise importance
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.752136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0bf9839c-4db9-4165-926e-cd98ada42d3e · outbound
This paper cites HAQ: Hardware-aware automated quantization with mixed precision.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model HAQ: Hardware-aware automated quantization with mixed precision
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.740506Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7be40541-a935-40c9-a245-fb7796696425 · outbound
This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.229668Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe916578-f469-4f43-9311-ca167b95c30d · outbound
This paper cites Mutual Information Preserving Neural Network Pruning.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Mutual Information Preserving Neural Network Pruning
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.233478Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d860aecf-3c99-4e78-820a-323e707235b5 · outbound
This paper cites Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.237219Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0f0424aa-4f92-4457-8e36-b12c48067f31 · outbound
This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.240818Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17c0c60c-f2ca-45c1-b263-87b87e290f9b · outbound
This paper cites AdaLog: Post-training quantization for vision transformers with adaptive log- arithm quantizer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model AdaLog: Post-training quantization for vision transformers with adaptive log- arithm quantizer
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.729586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ff418a5f-e032-4a9a-bc74-835ea4443e27 · outbound
This paper cites Patch-wise mixed-precision quantization of vi- sion transformer.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Patch-wise mixed-precision quantization of vi- sion transformer
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.719182Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1da0719f-5b88-482b-858d-6db7ad86cada · outbound
This paper cites EfficientSAM: Lever- aged masked image pretraining for efficient segment anything.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model EfficientSAM: Lever- aged masked image pretraining for efficient segment anything
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.706869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation aff9ec6d-f943-4f8d-8675-1bcbce4a70bf · outbound
This paper cites Mixed precision quantization of transformer language models for speech recognition.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Mixed precision quantization of transformer language models for speech recognition
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.589932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 1fa1c05b-e2f7-490f-8b65-64cbf7ae6b9a · outbound
This paper cites Global vi- sion transformer pruning with hessian-aware saliency.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Global vi- sion transformer pruning with hessian-aware saliency
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.577767Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 03e28815-1e0e-40f1-a570-263a22b3296f · outbound
This paper cites Track Anything: Segment Anything Meets Videos.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Track Anything: Segment Anything Meets Videos
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.262871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d92c3b57-8f8f-40ac-8a0b-bbb24588325b · outbound
This paper cites Width & depth pruning for vision transformers.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Width & depth pruning for vision transformers
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.563924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c3a10c91-84bb-4db8-a454-2bc187027e6d · outbound
This paper cites Inpaint Anything: Segment Anything Meets Image Inpainting.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Inpaint Anything: Segment Anything Meets Image Inpainting
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.270591Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 556d3132-1ae9-4d75-ba35-7da75f63e4fa · outbound
This paper cites PTQ4ViT: Post-training quantiza- tion for vision transformers with twin uniform quan- tization.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model PTQ4ViT: Post-training quantiza- tion for vision transformers with twin uniform quan- tization
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.552510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8f5f46c2-32c2-4a01-a5b4-32370e020b37 · outbound
This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.277300Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 305081b0-f7db-4dd4-a253-77d3dbce7278 · outbound
This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.281787Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 886701a5-48a0-4a69-aba7-ead861ff1d20 · outbound
This paper cites Fast Segment Anything.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model Fast Segment Anything
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:28:02.286033Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79005f7c-d723-418c-ae8a-4383fd756455 · outbound
This paper cites DarkSAM: Fooling segment anything model to segment nothing.
Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model DarkSAM: Fooling segment anything model to segment nothing
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T23:28:02.541453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.