Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking | Paper Citation Record

Observation 624f2eb8-7378-4077-8229-39ac2c924d30 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 1

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Observation 0bdc142f-fb82-40c0-aad6-0cdb77fb790f · outbound

This paper cites Qwen2.5-vl technical report, 2025.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Qwen2.5-vl technical report, 2025

Reference 2

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Observation 78257f7a-e6f6-47ec-be0d-957035f92538 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 3

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Observation a2f5cc7b-6af3-4f62-9971-926b4dc231c9 · outbound

This paper cites How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.Science China Information Sciences, 67(12):220101, 2024.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking How far are we to gpt-4v? closing the gap to commercial multimodal models with open-source suites.Science China Information Sciences, 67(12):220101, 2024

Reference 4

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Observation 375c014b-7702-4a1f-a90c-920ddf494a85 · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 5

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Observation 1f114bcf-c7e3-4505-b39e-b7a84fb848b2 · outbound

This paper cites Scaling instruction- finetuned language models.Journal of Machine Learning Research, 25(70):1–53, 2024.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Scaling instruction- finetuned language models.Journal of Machine Learning Research, 25(70):1–53, 2024

Reference 6

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Observation 93ed8c7d-9d7e-4b67-970d-27bfa3af3e27 · outbound

This paper cites Blink: Multimodal large language models can see but not perceive.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Blink: Multimodal large language models can see but not perceive

Reference 7

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Observation b0158267-52e2-4167-bac1-f521860f18d4 · outbound

This paper cites ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking ReFocus: Visual Editing as a Chain of Thought for Structured Image Understanding

Reference 8

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Observation 06d2e6eb-2c52-468d-b1f1-12a8af746a4d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 9

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Observation b250ffd4-01c1-4988-88e3-6274333d9a2f · outbound

This paper cites Gqa: A new dataset for real-world visual reasoning and compositional question answering.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Gqa: A new dataset for real-world visual reasoning and compositional question answering

Reference 10

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Observation 1f9e0db2-9a3f-44fd-89da-e7b7dbfe272f · outbound

This paper cites GPT-4o System Card.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking GPT-4o System Card

Reference 11

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Observation 13d08545-213c-46e7-8dc7-6b93aa1547cb · outbound

This paper cites Segment any- thing.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Segment any- thing

Reference 12

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Observation 038b0e83-9800-4ea1-b521-3a7b956348cc · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Lisa: Reasoning segmentation via large language model

Reference 13

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Observation 63d30524-ffeb-4982-878d-2cf6bf74c372 · outbound

This paper cites Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Search

Reference 14

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Observation 88fad2b5-15ee-4e45-b3c4-7e5450b75292 · outbound

This paper cites Proxyclip: Proxy attention improves clip for open-vocabulary segmentation.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Proxyclip: Proxy attention improves clip for open-vocabulary segmentation

Reference 15

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Observation 53019933-e538-4706-9d80-cda7ecf9d91c · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking LLaVA-OneVision: Easy Visual Task Transfer

Reference 16

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Observation 4762c3f9-f9c9-482d-a9b2-5a497cff9b30 · outbound

This paper cites Imagine while Reasoning in Space: Multimodal Visualization-of-Thought.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Imagine while Reasoning in Space: Multimodal Visualization-of-Thought

Reference 17

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Observation ed0d7422-c2fd-4e26-92a2-113dd333771f · outbound

This paper cites Dyfo: A training-free dynamic focus visual search for enhancing lmms in fine-grained visual understanding.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Dyfo: A training-free dynamic focus visual search for enhancing lmms in fine-grained visual understanding

Reference 18

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Observation d921ab3e-a537-4d9e-bf56-15b7bb96a34d · outbound

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

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 19

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Observation f5fe546e-47f0-4f33-b2df-f9ef43d8d2e8 · outbound

This paper cites STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking STAR-R1: Spatial TrAnsformation Reasoning by Reinforcing Multimodal LLMs

Reference 20

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Observation 67fed3bd-6feb-43d8-b1d8-55f082c2fb01 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 21

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Observation deb2d40a-32c4-4b20-818a-437662714a79 · outbound

This paper cites Improved baselines with visual instruction tuning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Improved baselines with visual instruction tuning

Reference 22

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Observation 9fe82f8f-07c5-4216-bc65-26df4ea9444e · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? InEuropean conference on computer vi- sion, pages 216–233.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Mmbench: Is your multi-modal model an all-around player? InEuropean conference on computer vi- sion, pages 216–233

Reference 23

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Observation 75fccbc4-421e-4bc6-92e4-8fb8090d3fd3 · outbound

This paper cites Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement

Reference 24

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Observation f45d257b-0d11-45a7-813b-85ed3811a840 · outbound

This paper cites Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models

Reference 25

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Observation b8ed3323-8f9f-4cbf-b2f5-cb797d032153 · outbound

This paper cites Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

Reference 26

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Observation 8345f5b2-37c8-4226-87d3-703105c277c3 · outbound

This paper cites Kam-cot: Knowledge augmented multimodal chain-of-thoughts reasoning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Kam-cot: Knowledge augmented multimodal chain-of-thoughts reasoning

Reference 27

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Observation fcd3e3e9-6173-49b9-a64f-107ce2f14ba5 · outbound

This paper cites SpaceR: Reinforcing MLLMs in Video Spatial Reasoning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking SpaceR: Reinforcing MLLMs in Video Spatial Reasoning

Reference 28

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Observation a2d951b1-2f45-4b65-8f82-007f77d935dd · outbound

This paper cites LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL

Reference 29

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Observation 4bc20847-b4cb-47cb-8924-6df3cec9b50c · outbound

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

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking SAM 2: Segment Anything in Images and Videos

Reference 30

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Observation f70fc475-3425-41fc-a0b8-31a16e7f0505 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

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Observation 87762264-c57f-424c-9ee5-131cc2f8cd53 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

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Observation 59cfc4d9-e090-4efd-8b30-3d4ab5e173bd · outbound

This paper cites Towards more unified in-context visual un- derstanding.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Towards more unified in-context visual un- derstanding

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Observation 529647dc-f670-4dbf-903e-3e8e8aabcafd · outbound

This paper cites Unicl-sam: Uncertainty-driven in-context segmen- tation with part prototype discovery.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Unicl-sam: Uncertainty-driven in-context segmen- tation with part prototype discovery

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Observation 2e88218d-1b10-4c89-bf1f-2ef2484e6ea3 · outbound

This paper cites Towards vqa models that can read.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Towards vqa models that can read

Reference 35

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Observation e37d1f31-e28a-4786-acab-79b7433001e5 · outbound

This paper cites Visual Agents as Fast and Slow Thinkers.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Visual Agents as Fast and Slow Thinkers

Reference 36

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Observation 5ae947d2-dff7-4f1f-a675-7657876b8f93 · outbound

This paper cites Ufo: A unified approach to fine-grained visual perception via open- ended language interface.arXiv preprint arXiv:2503.01342,.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Ufo: A unified approach to fine-grained visual perception via open- ended language interface.arXiv preprint arXiv:2503.01342,

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Observation 20e78449-b433-488d-b358-8498b99ed11a · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Llama 2: Open Foundation and Fine-Tuned Chat Models

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Observation 0634e6cd-b627-4ba0-a116-5786d85fca10 · outbound

This paper cites Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning

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Observation db97c725-27fa-4f50-b2b1-d98cee142c0f · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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Observation c6f2a3f7-1ca0-43a2-9199-2b8143d14489 · outbound

This paper cites Divide, conquer and combine: A training-free framework for high-resolution image perception in multimodal large language models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Divide, conquer and combine: A training-free framework for high-resolution image perception in multimodal large language models

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Observation 91522a8d-c611-4dae-837e-c4b9a38de124 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking SegGPT: Segmenting Everything In Context

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Observation f0cade3e-1810-4212-9911-35a10379f17c · outbound

This paper cites Perception in Reflection.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Perception in Reflection

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Observation e9eaa125-ef7c-4cd5-a590-ab38b62dff7a · outbound

This paper cites Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing

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Observation 326ed9e6-ee2c-41c4-a2f3-22771ab8c559 · outbound

This paper cites V?: Guided visual search as a core mechanism in multimodal llms.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking V?: Guided visual search as a core mechanism in multimodal llms

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Observation 32c978e5-72ac-4e97-9eb9-3b192df18201 · outbound

This paper cites Datasetdm: Synthesizing data with perception annota- tions using diffusion models.Advances in Neural Informa- tion Processing Systems, 36:54683–54695, 2023.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Datasetdm: Synthesizing data with perception annota- tions using diffusion models.Advances in Neural Informa- tion Processing Systems, 36:54683–54695, 2023

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Observation c5c55497-ab2b-406d-b563-a9ef4a519f50 · outbound

This paper cites Side adapter network for open-vocabulary semantic segmentation.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Side adapter network for open-vocabulary semantic segmentation

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Observation d126c982-7dab-48d7-be4d-f357a6dc06b2 · outbound

This paper cites React: Synergizing reasoning and acting in language models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking React: Synergizing reasoning and acting in language models

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Observation 3140b4cb-9160-42a0-a6d6-f3f2771cfed9 · outbound

This paper cites MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs

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Observation a3e8e83f-6bb1-4c79-9598-9c9ef67a5e66 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Personalize Segment Anything Model with One Shot

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Observation 00e56dd2-bb90-4f30-913e-19bf356825d7 · outbound

This paper cites Chain-of-focus: Adaptive visual search and zooming for multimodal reasoning via rl.arXiv e-prints, pages arXiv–2505, 2025.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Chain-of-focus: Adaptive visual search and zooming for multimodal reasoning via rl.arXiv e-prints, pages arXiv–2505, 2025

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Observation 5a095b40-a9ce-47b5-b778-51d713d1c199 · outbound

This paper cites X-paste: Revisiting scalable copy- paste for instance segmentation using clip and stablediffu- sion.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking X-paste: Revisiting scalable copy- paste for instance segmentation using clip and stablediffu- sion

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Observation 2a92220d-4646-4c71-bce5-85013db02f22 · outbound

This paper cites Training- free open-vocabulary semantic segmentation via diverse pro- totype construction and sub-region matching.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Training- free open-vocabulary semantic segmentation via diverse pro- totype construction and sub-region matching

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Observation 07365d9e-1949-4bbf-9b67-511f38baf2fb · outbound

This paper cites DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning

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Observation 3f33e287-c5ab-4cee-829d-c2d9d7ec5a35 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b93316d2-dadb-4564-92b7-369e328527f8 · outbound

This paper cites an unresolved cited work.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2495ae9-348f-4b3d-9e9b-7d4c8dea264d · outbound

This paper cites All experiments in this section are conducted with the maximum number of visual tokens set to 1,024.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking All experiments in this section are conducted with the maximum number of visual tokens set to 1,024

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eabe086-2d96-48cb-835f-2299af607e9b · outbound

This paper cites The first strategy, ‘Mix Data’, involves training the model in a single stage by mix- ing our collected data with the Visual Probe data at a 1:1 ratio.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking The first strategy, ‘Mix Data’, involves training the model in a single stage by mix- ing our collected data with the Visual Probe data at a 1:1 ratio

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4d209f3-acd6-4874-852e-919a21c4e18e · outbound

This paper cites an unresolved cited work.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f33b41f0-fe88-4764-a955-434accec6444 · outbound

This paper cites We also compare our training time with that of DeepEyes as shown in Table 11.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking We also compare our training time with that of DeepEyes as shown in Table 11

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f58de2fb-b045-4012-bf94-562d4f4f10c5 · outbound

This paper cites In the first example, while both mod- els correctly crop the flag, DeepEyes provides an incorrect answer, whereas Stage-I arrives at the correct one.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking In the first example, while both mod- els correctly crop the flag, DeepEyes provides an incorrect answer, whereas Stage-I arrives at the correct one

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91031dfe-bbcb-4660-902a-7b25b7683d78 · outbound

This paper cites We address this by propos- ing an information gap mechanism.

Fully Spiking Neural Networks with Target Awareness for Energy-Efficient UAV Tracking We address this by propos- ing an information gap mechanism

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-13T16:55:20.099628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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