Tootfinder

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
Chenhao Li, Marco Hutter, Andreas Krause
https://arxiv.org/abs/2507.05906 https://…

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why
This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations, with a focus on the structure of reward functions and their implications for policy learning. Feature-based methods offer dense, interpretable rewards that excel at high-fidelity motion imitation, yet often require sophisticated representations of references and struggle with generalization in unstructured settings. GAN-based methods, in contrast, use implicit, distributional s…

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
Swakshar Deb, Nian Wu, Frederick H. Epstein, Miaomiao Zhang
https://arxiv.org/abs/2507.02003

Unsupervised Cardiac Video Translation Via Motion Feature Guided Diffusion Model
This paper presents a novel motion feature guided diffusion model for unpaired video-to-video translation (MFD-V2V), designed to synthesize dynamic, high-contrast cine cardiac magnetic resonance (CMR) from lower-contrast, artifact-prone displacement encoding with stimulated echoes (DENSE) CMR sequences. To achieve this, we first introduce a Latent Temporal Multi-Attention (LTMA) registration network that effectively learns more accurate and consistent cardiac motions from cine CMR image videos.…

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
Zhiwang Zhang, Dong Xu, Wanli Ouyang, Chuanqi Tan
https://arxiv.org/abs/2506.20567

Show, Tell and Summarize: Dense Video Captioning Using Visual Cue Aided Sentence Summarization
In this work, we propose a division-and-summarization (DaS) framework for dense video captioning. After partitioning each untrimmed long video as multiple event proposals, where each event proposal consists of a set of short video segments, we extract visual feature (e.g., C3D feature) from each segment and use the existing image/video captioning approach to generate one sentence description for this segment. Considering that the generated sentences contain rich semantic descriptions about the …

Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
Shumin Lian, Jingwen Tong, Jun Zhang, Liqun Fu
https://arxiv.org/abs/2506.04594

Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM
WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense netwo…

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.02233 …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This imp…

Slot Attention with Re-Initialization and Self-Distillation
Rongzhen Zhao, Yi Zhao, Juho Kannala, Joni Pajarinen
https://arxiv.org/abs/2507.23755 https://a…

Slot Attention with Re-Initialization and Self-Distillation
Unlike popular solutions based on dense feature maps, Object-Centric Learning (OCL) represents visual scenes as sub-symbolic object-level feature vectors, termed slots, which are highly versatile for tasks involving visual modalities. OCL typically aggregates object superpixels into slots by iteratively applying competitive cross attention, known as Slot Attention, with the slots as the query. However, once initialized, these slots are reused naively, causing redundant slots to compete with inf…

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Jikai Wang, Yunqi Cheng, Zonghai Chen
https://arxiv.org/abs/2507.12800

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Though visual and repeat navigation is a convenient solution for mobile robot self-navigation, achieving balance between efficiency and robustness in task environment still remains challenges. In this paper, we propose a novel visual and repeat robotic autonomous navigation method that requires no accurate localization and dense reconstruction modules, which makes our system featured by lightweight and robustness. Firstly, feature flow is introduced and we develop a qualitative mapping between …

C-DOG: Training-Free Multi-View Multi-Object Association in Dense Scenes Without Visual Feature via Connected {\delta}-Overlap Graphs
Yung-Hong Sun, Ting-Hung Lin, Jiangang Chen, Hongrui Jiang, Yu Hen Hu
https://arxiv.org/abs/2507.14095

C-DOG: Training-Free Multi-View Multi-Object Association in Dense Scenes Without Visual Feature via Connected δ-Overlap Graphs
Multi-view multi-object association is a fundamental step in 3D reconstruction pipelines, enabling consistent grouping of object instances across multiple camera views. Existing methods often rely on appearance features or geometric constraints such as epipolar consistency. However, these approaches can fail when objects are visually indistinguishable or observations are corrupted by noise. We propose C-DOG, a training-free framework that serves as an intermediate module bridging object detecti…

Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination
David Nunez, Francesc Wilhelmi, Maksymilian Wojnar, Katarzyna Kosek-Szott, Szymon Szott, Boris Bellalta
https://arxiv.org/abs/2507.19377

Deep Reinforcement Learning-Based Scheduling for Wi-Fi Multi-Access Point Coordination
Multi-access point coordination (MAPC) is a key feature of IEEE 802.11bn, with a potential impact on future Wi-Fi networks. MAPC enables joint scheduling decisions across multiple access points (APs) to improve throughput, latency, and reliability in dense Wi-Fi deployments. However, implementing efficient scheduling policies under diverse traffic and interference conditions in overlapping basic service sets (OBSSs) remains a complex task. This paper presents a method to minimize the network-wi…

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Sheethal Bhat, Bogdan Georgescu, Adarsh Bhandary Panambur, Mathias Zinnen, Tri-Thien Nguyen, Awais Mansoor, Karim Khalifa Elbarbary, Siming Bayer, Florin-Cristian Ghesu, Sasa Grbic, Andreas Maier
https://arxiv.org/abs/2507.19621

Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyond
Detecting abnormalities in medical images poses unique challenges due to differences in feature representations and the intricate relationship between anatomical structures and abnormalities. This is especially evident in mammography, where dense breast tissue can obscure lesions, complicating radiological interpretation. Despite leveraging anatomical and semantic context, existing detection methods struggle to learn effective class-specific features, limiting their applicability across differe…

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Jianfei Jiang, Qiankun Liu, Haochen Yu, Hongyuan Liu, Liyong Wang, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2507.11333

MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network
Learning-based Multi-View Stereo (MVS) methods aim to predict depth maps for a sequence of calibrated images to recover dense point clouds. However, existing MVS methods often struggle with challenging regions, such as textureless regions and reflective surfaces, where feature matching fails. In contrast, monocular depth estimation inherently does not require feature matching, allowing it to achieve robust relative depth estimation in these regions. To bridge this gap, we propose MonoMVSNet, a …