Advanced Neural Network Applications

Neueste Paper

• Spatial Lifting for Dense Prediction

  Mingzhi Xu, Tao Zhou, Yong Li, Yizhe Zhang · 2. Oktober 2026

  We present Spatial Lifting (SL), a novel methodology for dense prediction tasks. SL operates by lifting standard inputs, such as 2D images, into a higher-dimensional space and subsequently processing them using networks designed for that higher dimension, such as a 3D U-Net. Counterintuitively, this…

• MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation

  Ivan Martinovi\'c, Josip \v{S}ari\'c, Yuki M. Asano, Sini\v{s}a \v{S}egvi\'c · 1. Oktober 2026

  Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g., synthetic) and an unlabeled target domain to bridge the distribution gap. Existing panoptic UDA methods rely on teacher-student consistency learni…

• DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

  ZiAn Wang, MingZhe Liu, Chaoyi Guo, ChangChun Li, Fangming Gu · 1. Oktober 2026

  Pedestrian detection plays a crucial role in computer vision with applications in autonomous driving, surveillance, and public safety. However, real-world dense scenes bring severe challenges, including heavy occlusion, drastic scale variations, and strict real-time requirements. Existing lightweigh…

• COBICount: Separating Object and Background Responses for Remote Sensing Object Counting Without Training on Target Data

  Junjing Zheng, Zhiyi Zhou, Ningrui Yang, Hongying Meng · 1. Oktober 2026

  Remote sensing object counting estimates how many buildings, vehicles, or ships appear in overhead images. Most supervised counters predict a density map, whose sum gives the object count, and assume similar categories, sizes, and backgrounds. Applying them across regions, sensors, or categories oft…

• On the Relaxation of Conditional Independence Assumption for Image Segmentation

  Zixun Wang, Ben Dai · 1. Oktober 2026

  In semantic segmentation, a recent line of RankSEG methods directly optimizes Dice/IoU scores at inference time, improving alignment with evaluation metrics without modifying model training. Despite its theoretical and empirical success, RankSEG relies on the restrictive Conditional Independence Ass…

• SANTA++: Sampling Attention through Representative Keys

  Kyle Lee, Christian Z. Pratt, Ruoyu Fang, Heekyung Lee, Avinash Lohitsa, Ryan Modafe, Kerem Y. Camsari · 30. September 2026

  Attention often concentrates on a small subset of tokens in the context, but which subset matters changes from one query to the next. To exploit this changing structure, we introduce SANTA++, a training-free stochastic attention method that uses representative keys for memory-efficient selection wit…

• When Can Attention Heads Be Statically Defined?

  Weixian Waylon Li, Yintao Tai, Marcio Fonseca, Shay B. Cohen · 30. September 2026

  Some attention heads learn similar patterns across inputs. Reusing these patterns could reduce training cost by avoiding repeated query-key score computation and softmax. Through controlled pretraining comparisons, we identify Selective Attention Freezing (SAF), which selects heads with low attentio…

• From Unity Simulation to Diffusion-Based Augmentation: Quantifying Dataset Balance for Robust Object Detection

  Mohamed Benkedadra, Aissa Saoudi, Maxime Gloesener, Sidi Ahmed Mahmoudi, Matei Mancas · 30. September 2026

  Modern computer vision models achieve high accuracy when trained on large-scale annotated datasets. In critical domains such as construction safety monitoring, data collection is costly, hazardous, and ethically constrained. This paper presents a systematic study comparing two complementary data gen…

• Embedded Bi-Temporal Building Damage Assessment for On-Board Data Reduction

  Thomas Goudemant, Benjamin Francesconi, Marjorie Bellizzi, Adrien Dorise · 30. September 2026

  Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire relevant imagery shortly after an event, but exploitation is limited by uplink and downlink capacity and by ground-processing latency. We address this with…

• Progressive-View On-Policy Distillation for Regional-to-Global Transfer in Multimodal LLMs

  Shanfeng Huang, Zhou Fang, Song Xiao, Hai Du · 29. September 2026

  Regional-to-global distillation uses crop-conditioned guidance to improve full-image understanding. The challenge is to effectively transfer the teacher's crop-based advantage to the student's full-image inference. We propose progressive-view on-policy distillation (PVD), which shifts the student's …

• CASS: Contribution-Aware Structured Sparsity for Model Merging

  Yan Li, Guiping Cao, Meng Xu, Tao Jiang, Yaguang Song, Ming Tao, Yaowei Wang, Dongmei Jiang · 29. September 2026

  Model merging integrates task-specific fine-tuned models into a single multi-task model, but often suffers from parameter interference caused by conflicting task-vector updates. Existing methods typically mitigate conflicts by pruning task vectors based on weight magnitude or random heuristics, trea…

• AdapToPASS: Ambiguity-aware Adaptive Spherical Transformer for Panoramic Semantic Segmentation

  Soumyaratna Debnath, Weiming Zhang, Shriram Damodaran, Dingwen Xiao, Addison Lin Wang · 28. September 2026

  Spherical Transformers have emerged as a promising framework for panoramic semantic segmentation (PASS) by operating directly on spherical geometry and alleviating projection-induced distortions. However, existing architectures often assume canonical spherical structure and stable viewpoints, which …

• CSCWD: Cross-Scale Channel-wise Knowledge Distillation for Lightweight Tiny Object Detection on Edge Devices

  Amir Zamani, Zeinab Ghasemi-Naraghi · 28. September 2026

  Real-time tiny object detection in aerial imagery is constrained by the weak spatial evidence of very small objects and the loss of high-resolution detail in lightweight detectors. This study presents Cross-Scale Channel-wise Knowledge Distillation (CSCWD), a training-time framework that transfers h…

• FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection

  Ben Liang, Chao Sui, Junqi Bai, Yuan Liu, Chunlai Li, Xiubao Sui, Qian Chen · 25. September 2026

  In aerial RGB--IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex illumination and environmental conditions. Existing multimodal detectors mainly focus on spatial-domain interaction or frequency-specific feature enh…

• RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation

  Shaohua Dong, Zexuan Meng, Haiyan Sun, Bing Fan, Cuicui Zhang, Dylan Joseph, Kewei Sha, Yunhe Feng, Heng Fan · 25. September 2026

  In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possesses several attractive properties: (1) Expanded Semantic Space. In particular, it…

• OD3: Optimization-free Dataset Distillation for Object Detection

  Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao, Zhiqiang Shen · 24. September 2026

  Training large neural networks on large-scale datasets requires substantial computational resources, particularly for dense prediction tasks such as object detection. Although dataset distillation (DD) has been proposed to alleviate these demands by synthesizing compact datasets from larger ones, mo…

• SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection

  TianYi Yu, DaJian Zhong, Lilin Wang · 24. September 2026

  Traffic sign detection is a challenging visual signal processing task for advanced driver assistance, where small objects, scale variation, and occlusion limit conventional detectors with fixed receptive fields. This paper proposes State-space Modeling and Dynamic Dual Fusion Network (SMDDFNet), a d…

• LightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder

  Andrei Arhire, Mihaela-Elena Breab\u{a}n, Radu Timofte · 24. September 2026

  We present LightMIS, a scalable family of ultra-lightweight convolutional networks for 2D binary medical image segmentation without a learned stage-wise decoder. LightMIS aligns the outputs of a five-level encoder to a common resolution using Scale-Aligned Projection blocks, aggregates them once, an…

• PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

  Armin Maleki, Hayder Radha · 24. September 2026

  Heterogeneity across Collaborative Perception (CP) agents is a major challenge for emerging CP frameworks due to domain gaps from differing sensors, architectures, and training data. Prior works mitigate this challenge by aligning features in a unified space via model retraining or per-agent-type in…

• GTR: Gated Token Recurrence for Efficient Dense Prediction

  Zhe Feng, Longfei Liu, Wei Liu, Kai Chen, Jiangjiang Kong, Wei Zhou, Yifeng Qian, Dexiong Chen, Xuanlong Yu, Xi Shen · 23. September 2026

  Self-attention-based vision backbones perform well on dense prediction, but the quadratic computational cost of global softmax attention limits their efficiency as image resolution increases. We introduce Gated Token Recurrence (GTR), a softmax-free recurrent vision backbone that combines gated line…

• DTKDP: A Dual Teacher Knowledge Distillation and Pruning Framework for Lightweight Oriented SAR Ship Detection

  Yuming Li, Fan Zhang, Alin M. Achim · 22. September 2026

  Two-stage oriented detectors achieve high localization accuracy in synthetic aperture radar (SAR) ship detection, but their large backbones, feature pyramids, proposal modules, and heavy region of interest (RoI) heads hinder deployment. Existing lightweight SAR ship detectors typically use one-stage…

• Ev-YOLO: Uncertainty-Aware Object Detection via a Unified Evidential Formulation

  Simon Barbarit-Gaboriau (LITIS - STI, INSA Rouen Normandie), Hind Laghmara (LITIS - STI), R\'emi Boutteau (LITIS - STI), Samia Ainouz (LITIS, LITIS - STI) · 22. September 2026

  Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep Learning (EDL) provides a principled framework for uncertainty-aware classification by representing network outputs as evidence and interpreting pred…

• LiAuto-MindViT: A Hybrid Vision Backbone with Adaptive Bidirectional Mamba

  Lifu Mu, Shuai Chen, Wen Zheng, Haoyi Sun, Xueyang Fu, Pengfei Yu, Ning Mao, Tao Wei, Zhou Pan · 22. September 2026

  While Mamba-based models have shown strong potential for long sequence modeling, adapting them to vision is challenging due to the requirement of local neighborhood correlations and multi-directional spatial contexts for visual understanding. In this paper, we present LiAuto-MindViT, a novel hybrid …

• SRPR-Net: Semantic and Relational Prompt Refinement for Automated SAM-based Instance Segmentation

  Lufei Liu, Guojie Li, Suncheng Xiang, Fan Zhang · 22. September 2026

  Instance segmentation is a fundamental computer vision task with diverse real-world applications. Recently, prompt-driven foundation models have shown promising generalization. However, automated prompting remains limited by insufficient semantic guidance and inter-instance modeling. To address this…

• Lightweight Pedestrian Head-Orientation Recognition Network for Safe Pedestrian-Vehicle Interaction

  Yuanzhe Li, Yidi Huang, Xiaotong Chang, Hounian Liu · 22. September 2026

  Pedestrian head orientation recognition plays an important role in autonomous driving by providing valuable cues for understanding pedestrian attention and anticipating potential crossing behavior. However, reliable recognition in real-world traffic scenes remains challenging because pedestrian head…