Visual Place Recognition (VPR) aims to match a query image to reference images of the same place in a large-scale database. Recent state-of-the-art methods employ Vision Transformers (ViTs) as backbone foundation models to extract patch-level features that are robust to viewpoint, illumination, and seasonal variations, which are then aggregated into a compact global descriptor for retrieval. Most existing aggregation methods uniformly pool patch tokens into learned clusters, despite the fact that different clusters often encode distinct spatial or semantic patterns and contribute unequally to VPR performance. To address this limitation, we propose Weighted Aggregated Descriptor (WeiAD), which assigns weights to clusters during aggregation, producing more discriminative global representations. Beyond accuracy, retrieval latency is a critical concern for large-scale deployments and resource-constrained edge devices. Prior work mainly reduces latency by compressing global descriptors, while overlooking the cost of feature extraction, an issue exacerbated by ViT-based backbones. We therefore introduce WeiToP, a VPR-oriented token pruning framework that reduces feature extraction cost via self-distillation, where aggregation-induced token importance supervises a lightweight pruning module attached to an early transformer layer, enabling inference-time token pruning. After a single joint training phase, WeiToP enables plug-and-play token pruning at inference time, allowing flexible and on-demand control over the accuracy-efficiency trade-off without additional training. Moreover, WeiToP outperforms existing token pruning methods adapted from general vision tasks.
How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we e…
University of Technology Sydney（悉尼科技大学）；Baidu Research（百度研究院）
Network pruning reduces the computation costs of an over-parameterized network without performance damage. Prevailing pruning algorithms pre-define the width and depth of the pruned networks, and then transfer parameters from the unpruned network to pruned networks. To break the structure limitation of the pruned networks, we propose to apply neural architecture search to search directly for a network with flexible channel and layer sizes. The number of the channels/layers is learned by minimizing the loss of the pruned networks. The feature map of the pruned network is an aggregation of K fea…
ShaRP: SHAllow-LayeR Pruning for Efficient Video Large Language Models
VCIP & TMCC & DISSec, College of Computer Science, Nankai University（VCIP与TMCC与DISSec学院，南开大学计算机科学学院）；LIX, Ecole Polytechnique, IP Paris（LIX，巴黎高等理工学院，IP巴黎）；OPPO Research Institute（OPPO研究院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Northeastern University（东北大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world. Although adversarial training (AT) is currently one of the most effective methodologies to robustify DNNs, it is computationally very expensive (e.g., 5-10X costlier than standard training). To address this challenge, existing approaches focus on single-step AT, referred to as Fast AT, reducing the overhead of adversarial example generation. Unfortunately, these approaches are known to fail against stronger adversaries. To make AT computationally efficient witho…
We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the original network structure, thus it can be perfectly supported by any off-the-shelf deep learning libraries. We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which diff…
Filter Pruning For CNN With Enhanced Linear Representation Redundancy
Tianjin Normal University（天津师范大学）；Southwestern University of Finance and Economics（西南财经大学）；University of Electronic Science and Technology of China（电子科技大学）
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang Key Laboratory of Digital-Intelligence Service Technology（浙江省数字化服务技术重点实验室）；The University of Queensland（昆士兰大学）；Singapore Management University（新加坡管理大学）；The University of Southern Queensland（南昆士兰大学）
Center for Applied Scientific Computing（应用科学计算中心）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）
Recently, Frankle & Carbin (2019) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network. However, finding these high performing trainable subnetworks is expensive, requiring iterative process of training and pruning weights. In this paper, we propose (and prove) a stronger Multi-Prize Lottery Ticket Hypothesis: A sufficiently over-parameterized neural network with random weights contains several subnetworks (winning tickets) that (a) have comparable accuracy to a dense target netwo…
FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
Media IC and System Lab, the Graduate Institute of Electronics Engineering and Department of Electrical Engineering, National Taiwan University（媒体IC与系统实验室，电子工程研究所及电气工程系，国立台湾大学）
Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（人工智能与数字经济广东实验室）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；The Hong Kong Polytechnic University（香港理工大学）；Great Bay University（大鹏大学）；Shenzhen University（深圳大学）；Huawei（华为）
VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLMs Inference
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；Tencent Youtu Lab（腾讯优图实验室）
From Global to Local: Rethinking CLIP Feature Aggregation for Person Re-Identification
Department of Electrical and Computer Engineering, University of Washington（华盛顿大学电气与计算机工程系）；Applied Physics Laboratory, University of Washington（华盛顿大学应用物理实验室）
When Token Pruning is Worse than Random: Understanding Visual Token Information in VLLMs
Tongji University（同济大学）；University of California, Santa Cruz（加州大学圣克ruz分校）；Amazon（亚马逊）；East China Normal University（华东师范大学）；Shanghai Eye Disease Prevention and Treatment Center（上海眼病预防与治疗中心）
This is a short technical report introducing the solution of the Team TCParser for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this paper, we introduce a strong backbone which is cross-window based Shuffle Transformer for presenting accurate face parsing representation. To further obtain the finer segmentation results, especially on the edges, we introduce a Feature Alignment Aggregation (FAA) module. It can effectively relieve the feature misalignment issue caused by multi-resolution feature aggregation. Benefiting from the stronge…
University of Southern California（南加州大学）；DEVCOM US Army Research Lab（美国陆军作战能力发展司令部陆军研究实验室）
Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key technique for remote-sensing image recognition. The state-of-the-art convolutional neural networks (CNNs) for SAR ATR suffer from \emph{high computation cost} and \emph{large memory footprint}, making them unsuitable to be deployed on resource-limited platforms, such as small/micro satellites. In this paper, we propose a comprehensive GNN-based model-architecture {co-design} on FPGA to address the above issues. \emph{Model design}: we design a novel graph neural network (GNN) for SAR ATR. The proposed GNN model incorp…
University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；SenseTime Research（商汤科技研究院）
Point clouds and RGB images are two general perceptional sources in autonomous driving. The former can provide accurate localization of objects, and the latter is denser and richer in semantic information. Recently, AutoAlign presents a learnable paradigm in combining these two modalities for 3D object detection. However, it suffers from high computational cost introduced by the global-wise attention. To solve the problem, we propose Cross-Domain DeformCAFA module in this work. It attends to sparse learnable sampling points for cross-modal relational modeling, which enhances the tolerance to c…
