XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
Department of Research Computing（研究计算系）；University of Houston（休斯敦大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Engineering Technology（工程科技系）
Object detection on heterogeneous edge devices must satisfy strict energy, latency, and memory constraints while still providing reliable perception for downstream autonomy. Existing energy-aware NAS methods often target limited deployment settings, while real energy remains difficult to optimize because it is highly device-dependent and costly to measure. We address these challenges with an energy-adaptive framework that combines an energy-aware XiResOFA search space, a two-stage energy estimator, and iterative search to identify a single energy-efficient base architecture. We then apply compound scaling to transform this base design into the XiYOLO family across deployment budgets, enabling interpretable accuracy-energy tradeoffs under sparse hardware measurements. Experiments on PascalVOC, COCO, and real-device deployment show that XiYOLO achieves a stronger energy-accuracy tradeoff than YOLO baselines. On PascalVOC, the medium XiYOLO model reaches 86.15 mAP50 while reducing energy relative to YOLOv12m by 20.6% on GPU and 35.9% on NPU. On COCO, XiYOLO reduces energy relative to YOLOv12 by up to 53.7% on GPU and 51.6% on NPU at the small scale. The proposed two-stage estimator also improves sample efficiency over a joint predictor under few-shot adaptation with only 2-20 target-device samples.
Shanghai Jiao Tong University（上海交通大学）；East China Normal University（华东师范大学）；Tongji University（同济大学）
Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints. While iterative in nature, most existing interactive segmentation methods tend to ignore the dynamics of successive interactions and take each interaction independently. We here propose to model iterative interactive image segmentation with a Markov decision process (MDP) and solve it with reinforcement learning (RL) where each voxel is treated as an agent. Considering the large exploration space for voxel-wise prediction and the de…
Weakly supervised object detection (WSOD), where a detector is trained with only image-level annotations, is attracting more and more attention. As a method to obtain a well-performing detector, the detector and the instance labels are updated iteratively. In this study, for more efficient iterative updating, we focus on the instance labeling problem, a problem of which label should be annotated to each region based on the last localization result. Instead of simply labeling the top-scoring region and its highly overlapping regions as positive and others as negative, we propose more effective …
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Learning segmentation from noisy labels is an important task for medical image analysis due to the difficulty in acquiring highquality annotations. Most existing methods neglect the pixel correlation and structural prior in segmentation, often producing noisy predictions around object boundaries. To address this, we adopt a superpixel representation and develop a robust iterative learning strategy that combines noise-aware training of segmentation network and noisy label refinement, both guided by the superpixels. This design enables us to exploit the structural constraints in segmentation lab…
Institute of Computer Science II（第二计算机科学研究所）；University of Bonn（波恩大学）；School of Computing（计算学院）；National University of Singapore（新加坡国立大学）
In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate scale-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform …
Universite Paris Est（巴黎东大学）；Ecole des Ponts ParisTech（巴黎路桥学院）
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the ef…
Aviation University of Air Force（空军航空大学）；JD Explore Academy, JD.COM（京东探索研究院，京东集团）；The University of Melbourne（墨尔本大学）
Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG) strategy. The GAA module relies on the epipolar geometry cu…
Culture-TRIP: Culturally-Aware Text-to-Image Generation with Iterative Prompt Refinement
Department of Computer Science and Engineering, Dongguk University（计算机科学与工程系，东国大学）；Department of Computer Science and Artificial Intelligence, Dongguk University（计算机科学与人工智能系，东国大学）
MagicMan: Generative Novel View Synthesis of Humans with 3D-Aware Diffusion and Iterative Refinement
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Tencent AI Lab（腾讯人工智能实验室）；The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）
Fudan University（复旦大学）；Tencent AI Lab（腾讯人工智能实验室）；Zhejiang Lab（之江实验室）
Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communic…
Hefei University of Technology（合肥工业大学）；Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）
Visual dialog is a challenging task that requires the comprehension of the semantic dependencies among implicit visual and textual contexts. This task can refer to the relation inference in a graphical model with sparse contexts and unknown graph structure (relation descriptor), and how to model the underlying context-aware relation inference is critical. To this end, we propose a novel Context-Aware Graph (CAG) neural network. Each node in the graph corresponds to a joint semantic feature, including both object-based (visual) and history-related (textual) context representations. The graph st…
Washington University in St. Louis（圣路易斯华盛顿大学）；Megvii Inc.（旷视科技）
In this paper, we propose a novel learning-based pipeline for partially overlapping 3D point cloud registration. The proposed model includes an iterative distance-aware similarity matrix convolution module to incorporate information from both the feature and Euclidean space into the pairwise point matching process. These convolution layers learn to match points based on joint information of the entire geometric features and Euclidean offset for each point pair, overcoming the disadvantage of matching by simply taking the inner product of feature vectors. Furthermore, a two-stage learnable poin…
SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels
The Chinese University of Hong Kong（香港中文大学）；The Shun Hing Institute of Advanced Engineering（信兴高等工程研究院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion
School of Automation, Southeast University（东南大学自动化学院）；Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich（慕尼黑工业大学机器人、人工智能与实时系统研究所）
No-reference based automatic parameter optimization for iterative reconstruction using a novel search space aware crow search algorithm
Danube Private University (DPU)（多瑙河私立大学）；Medical University Vienna（维也纳医科大学）；University of Cambridge（剑桥大学）；MedPhoton GmbH（MedPhoton有限公司）；University of Vienna（维也纳大学）；Austrian Center for Medical Innovation and Technology (ACMIT)（奥地利医学创新与技术中心）
University of Hong Kong（香港大学）；DJI corporation（大疆创新）；Uber ATG；University of Toronto（多伦多大学）；Vector Institute（Vector研究所）；University of Oxford（牛津大学）；The Chinese University of Hong Kong（香港中文大学）
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on top of two-stream CNNs, GeoNet++ captures the geometric relationships between depth and surface normals with the proposed depth-to-normal and normal-to-depth modules. In particular, the "depth-to-normal" module exploits the least square solution of estimating surface normals from depth to improve their quality, while the "normal-to-depth" module refines the depth map based on the constraints on surface normals through …
IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement
Centre for Intelligent Cyber Physical Systems, Indian Institute of Technology Guwahati（印度古瓦哈蒂理工学院智能信息物理系统中心）；Dept. of Electronics and Electrical Engg., Indian Institute of Technology Guwahati（印度古瓦哈蒂理工学院电子与电气工程系）
The problem of phase retrieval, or the algorithmic recovery of lost phase information from measured intensity alone, underlies various imaging methods from astronomy to nanoscale imaging. Traditional methods of phase retrieval are iterative in nature, and are therefore computationally expensive and time consuming. More recently, deep learning (DL) models have been developed to either provide learned priors to iterative phase retrieval or in some cases completely replace phase retrieval with networks that learn to recover the lost phase information from measured intensity alone. However, such m…
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sichuan University（四川大学）；Shenzhen University（深圳大学）；The University of Hong Kong（香港大学）
Iterative Occlusion-Aware Light Field Depth Estimation using 4D Geometrical Cues
Instituto de Telecomunicações, Portugal（葡萄牙电信研究所）；ESTG - Polytechnic University of Leiria（莱里亚理工学院）；PEE, COPPE, Federal University of Rio de Janeiro（里约热内卢联邦大学）
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Hôpital Pitié-Salpétrière（皮蒂耶-萨尔佩特里埃医院）；Assistance Publique des Hôpitaux de Paris（巴黎公共援助医院）；Sorbonne University（索邦大学）
U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction
Computing and Mathematical Sciences, California Institute of Technology（计算与数学科学系，加州理工学院）；Mathematical Sciences, California Institute of Technology（数学科学系，加州理工学院）
Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction
School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Department of Engineering and System Science, National Tsing Hua University（清华大学系统科学系）
Convolutional neural network (CNN) delivers impressive achievements in computer vision and machine learning field. However, CNN incurs high computational complexity, especially for vision quality applications because of large image resolution. In this paper, we propose an iterative architecture-aware pruning algorithm with adaptive magnitude threshold while cooperating with quality-metric measurement simultaneously. We show the performance improvement applied on vision quality applications and provide comprehensive analysis with flexible pruning configuration. With the proposed method, the Mul…
