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- 496
- Vision-Based Anti-UAV Detection and Tracking307
A clear and simple tracking algorithm combined with detection that inherits the detector’s high precision is proposed, thus providing a new attempt at UAV tracking using the proposed DUT Anti-UAV dataset.
- Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline298
A large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV), including 500 sequences with 1.7 million high-resolution (1920* 1080 pixels) frame pairs is constructed, and a coarse-to-fine attribute annotation is provided, where frame-level attributes are provided to exploit the potential of challenge-specific trackers.
- Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking206
This study develops a novel late fusion method to infer the fusion weight maps of both RGB and thermal (T) modalities and proposes a tracker switcher to switch the appearance and motion trackers flexibly.
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- Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem33
This work introduces the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agentic model and releases ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
- CloudSim 7G : An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments31
Cloud simulators are an essential tool for academic and industrial researchers, to investigate the effectiveness of novel algorithms and mechanisms in large‐scale scenarios.
- Riker31
This work develops a new PQA framework (named Riker) that substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations, and compares keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.
- Charge Prediction by Constitutive Elements Matching of Crimes29
A novel method named Constitutive Elements-guided Charge Prediction (CECP), which mimics human's charge identification process to extract potential instances of CEs and generate predictions accordingly and shows the superiority of CECP over competitive baselines.
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- Leveraging the Power of Data Augmentation for Transformer-based Tracking19
This paper explores the impact of general data augmentations on transformer-based trackers via systematic experiments, and proposes two data augmentation methods customized for tracking, including a token-level feature mixing augmentation strategy which enables the model against challenges like background interference.
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- Fundus Image Generation and Classification of Diabetic Retinopathy Based on Convolutional Neural Network13
A new conditional generative adversarial network with a self-attention mechanism named SACGAN is proposed to augment the number of diabetic retinopathy fundus images, thereby addressing the problems of insufficient and imbalanced data samples and gradient-weighted class activation mapping is utilized to prove the proposed model’s interpretability.
- Online Filtering Training Samples for Robust Visual Tracking12
An optimization module named MetricNet is proposed for online filtering training samples that applies a matching network containing the classification and distance branches, and uses multiple metric methods for different type samples to improve the tracking performance.
- An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective12
This work proposes an end-to-end deep neural network framework, which is trained by a novel group-level objective function that directly optimizes the answer triggering performance and penalizes three potential types of error.
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- Video Annotation for Visual Tracking via Selection and Refinement10
A new framework to facilitate bounding box annotations for video sequences is presented, which investigates a selection-and-refinement strategy to automatically improve the preliminary annotations generated by tracking algorithms.
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- Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning7
This work bridges the sim-to-real gap through a semi-virtual environment, including a real robot and real-time aspects, while utilizing a simulated sensor and obstacles to enable environment randomization and automated episode resetting.
- Texture Classification Using Scattering Statistical and Cooccurrence Features6
New approaches for texture features extraction using scattering transform derived from subbands of the scattering decomposition and original images and used for classification for the four datasets containing 20, 30, 112, and 129 texture images.
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- Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking5
The core innovation of HiT lies in its Bridge Module, which connects lightweight transformers to the tracking framework, enhancing feature representation quality, and a training-free acceleration method based on the dynamic routing architecture of DyHiT is introduced.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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