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Showing 17,113–17,136 of 49,285 skills
- Arxiv 2608 12932v1 Flashdrive Flash Vision Language Action Inference**arXiv ID:** 2608.12932v1 **Authors:** Zekai Li, Yihao Liang, Hongfei Zhang, Jian Chen, Yesheng Liang, Zhijian Liu **URL:** http://arxiv.org/abs/2608.12932v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06361v1 The Low Frequency Trap Video Language Models FailThe Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping (arXiv: 2608.06361v1)Votes: 0GitHub stars: 3
- Arxiv 2608 06311v1 Does Flair Super Resolution Erase Or Hallucinate S**arXiv ID:** 2608.06311v1 **Authors:** Zahra Khodakarami, Yue Li, Pulkit Khandelwal, John Detre, Sandhitsu Das, Christopher Brown, David Wolk, Paul Yushkevich **URL:** http://arxiv.org/abs/2608.06311v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06270v1 The Illusion Of Visual Tool Use A Causal Audit OfThe Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images (arXiv: 2608.06270v1)Votes: 0GitHub stars: 3
- Arxiv 2608 06243v1 Dash Divergence Adaptive Supervision Horizons For**arXiv ID:** 2608.06243v1 **Authors:** ZhiYan Hou, Xinyu Tang, Hongyan An, Jianjin Zhang, Weizhen Wang, Yunyun Han, Gengsheng Li, Xiangzhao Hao, Haiyun Guo, Wenbin Hu, Jinqiao Wang, Yafeng Deng **URL:** http://arxiv.org/abs/2608.06243v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06236v1 Depth Guided Video Object Counting In Crowded Scen**arXiv ID:** 2608.06236v1 **Authors:** Yuanjing Xu, Xinyan Liu, Weidong Chen, Zixuan Zou, Linhao Zhang, Zhuangzhe Meng, Antoni B. Chan, Weigang Zhang **URL:** http://arxiv.org/abs/2608.06236v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 06023v1 Biokd Selective Physiology To Video Knowledge Dist**arXiv ID:** 2608.06023v1 **Authors:** Bojing Hou, Ruohao Li, Yitong Zhu, Hongjun Liu, Luwen Yu, Yuyang Wang **URL:** http://arxiv.org/abs/2608.06023v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05948v1 Gauge A Measurement Grounded Benchmark For PhysicaGAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models (arXiv: 2608.05948v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05938v1 Mirrornet Can Medical Image Anonymization Really P**arXiv ID:** 2608.05938v1 **Authors:** Attila Simkó **URL:** http://arxiv.org/abs/2608.05938v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05833v1 Visr Kgc Visual Subgraph Reasoning With Vision LanViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion (arXiv: 2608.05833v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05782v1 Vsmp Imu Video Grounded Semantic Motion Programs F**arXiv ID:** 2608.05782v1 **Authors:** Lala Shakti Swarup Ray, Vitor Fortes Rey, Mengxi Liu, Paul Lukowicz, Bo Zhou **URL:** http://arxiv.org/abs/2608.05782v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05745v1 Univvt A Unified End To End Framework For High FidUniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on (arXiv: 2608.05745v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05744v1 Equipment Centric Workpiece Localization In Near REquipment-centric workpiece localization in near real-time using deep learning-based vision and event-driven finite state machines (arXiv: 2608.05744v1)Votes: 0GitHub stars: 3
- Arxiv 2608 05728v1 Engram E2vid Reference Based Event To Video Recons**arXiv ID:** 2608.05728v1 **Authors:** Feiyu Ji, Xiang Li, Hao Ma, Tianxiang Huang, Qingxin Lu, Mengqi Ji, Lei Han, Xiaokang Yang, Xiaoyun Yuan **URL:** http://arxiv.org/abs/2608.05728v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05684v1 Nonvisual Classification Of Ground Condition By Ar**arXiv ID:** 2608.05684v1 **Authors:** Hyoto Yamaguchi, Zenji Yatabe, Seiya Kasai **URL:** http://arxiv.org/abs/2608.05684v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Arxiv 2608 05675v1 Consistency Has A Computable Blind Spot A CommutatConsistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading (arXiv: 2608.05675v1)Votes: 0GitHub stars: 3
- Arxiv 2603 06639 Recap Local Hebbian Prototype Learning As A Self ORECAP: Local Hebbian Prototype Learning as a Self-Organizing Readout for Reservoir Dynamics (arXiv: 2603.06639)Votes: 0GitHub stars: 3
- Arxiv 2204 05138 Artificial Intelligence Software Structured To SimArtificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity (arXiv: 2204.05138)Votes: 0GitHub stars: 3
- Wavesense Efficient Temporal Convolutions With Spiking Neural Networks For Keyword Spotting**arXiv ID:** 2111.01456 **Authors:** Philipp Weidel, Sadique Sheik **Published:** 2021-11-02T09:38:22Z **Abstract:** Ultra-low power local signal processing is a crucial aspect for edge applications on always-on devices. Neuromorphic processors emulating spiking neural networks show great computational power while fulfilling the limited power budget as needed in this domain. In this work we propose spiking neural dynamics as a natural alternative to dilated temporal convolutions. We extend t...Votes: 0GitHub stars: 3
- Vo2 Mott Oscillator Spiking Neuron基于VO2 Mott振荡器的单片集成脉冲神经元，采用BEOL工艺在CMOS兼容SOI平台上实现1T-1MR架构，具备栅极可调振荡、超低能耗和纳米级耦合特性Votes: 0GitHub stars: 3
- Training Spiking Neural Networks Via Augmented Direct Feedback Alignment**arXiv ID:** 2409.07776 **Authors:** Yongbo Zhang, Katsuma Inoue, Mitsumasa Nakajima, Toshikazu Hashimoto, Yasuo Kuniyoshi, Kohei Nakajima **Published:** 2024-09-12T06:22:44Z **Abstract:** Spiking neural networks (SNNs), the models inspired by the mechanisms of real neurons in the brain, transmit and represent information by employing discrete action potentials or spikes. The sparse, asynchronous properties of information processing make SNNs highly energy efficient, leading to SNNs being pr...Votes: 0GitHub stars: 3
- Training Probabilistic Spiking Neural Networks With Firsttospike Decoding**arXiv ID:** 1710.10704 **Authors:** Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran **Published:** 2017-10-29T22:13:53Z **Abstract:** Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at harnessing the energy efficiency of spike-domain processing by building on computing elements that operate on, and exchange, spikes. In this paper, the problem of training a two-layer SNN is studied for the purpose of classification, under a Generalized Linear Model (GLM) probabilis...Votes: 0GitHub stars: 3
- Threefactor Learning In Spiking Neural Networks An Overview Of Methods And Trends From A Machine Learning Perspective**arXiv ID:** 2504.05341 **Authors:** Szymon Mazurek, Jakub Caputa, Jan K. Argasiński, Maciej Wielgosz **Published:** 2025-04-06T08:10:16Z **Abstract:** Three-factor learning rules in Spiking Neural Networks (SNNs) have emerged as a crucial extension to traditional Hebbian learning and Spike-Timing-Dependent Plasticity (STDP), incorporating neuromodulatory signals to improve adaptation and learning efficiency. These mechanisms enhance biological plausibility and facilitate improved credit ass...Votes: 0GitHub stars: 3
- Stochastic Spiking Attention Accelerating Attention With Stochastic Computing In Spiking Networks**arXiv ID:** 2402.09109 **Authors:** Zihang Song, Prabodh Katti, Osvaldo Simeone, Bipin Rajendran **Published:** 2024-02-14T11:47:19Z **Abstract:** Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency. Yet, the implementation of the attention mechanism using spiking signals on general-purpose computing platforms remains inefficient. In this paper, we propose a novel f...Votes: 0GitHub stars: 3