Dense small objects in UAV imagery are often missed due to long-range viewpoints, occlusion, and clutter[cite: 5]. This paper presents a detector-agnostic post-processing framework that converts overlap-induced redundancy into group evidence[cite: 6]. Overlapping tiling first recovers low-confidence candidates[cite: 7]. A Spatial Gate (DBSCAN on box centroids) and a Semantic Gate (DBSCAN on ResNet-18 embeddings) then validates group evidence[cite: 7]. Validated groups receive controlled confidence reweighting before class-aware NMS fusion[cite: 8]. Experiments on VisDrone show a recall increase from 0.685 to 0.778 (+0.093) and a precision adjustment from 0.801 to 0.595, yielding F1=0.669[cite: 9]. Post-processing latency averages 0.095 s per image[cite: 10]. These results indicate recall-first, precision-trade-off behavior that benefits recall-sensitive applications such as far-field counting and monitoring[cite: 10]. Ablation confirms that tiling exposes missed objects, spatial clustering stabilizes geometry, semantic clustering enforces appearance coherence, and reweighting provides calibrated integration with the baseline[cite: 11]. The framework requires no retraining and integrates with modern detectors[cite: 12]. Future work will reduce semantic gating cost and extend the approach with temporal cues[cite: 13].
This report describes a 2nd place solution of the detection challenge which is held within CVPR 2020 Retail-Vision workshop. Instead of going further considering previous results this work mainly aims to verify previously observed takeaways by re-experimenting. The reliability and reproducibility of the results are reached by incorporating a popular object detection toolbox - MMDetection. In this report, I firstly represent the results received for Faster-RCNN and RetinaNet models, which were taken for comparison in the original work. Then I describe the experiment results with more advanced m…
Institute for Research in Fundamental Sciences (IPM)（基础科学研究所（IPM））；Sharif University of Technology（谢里夫理工大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
Medical image segmentation has been very challenging due to the large variation of anatomy across different cases. Recent advances in deep learning frameworks have exhibited faster and more accurate performance in image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net for medical image segmentation, in which we take full advantages of U-Net, Squeeze and Excitation (SE) block, bi-directional ConvLSTM (BConvLSTM), and the mechanism of dense convolutions. (I) We improve the segmentation pe…
Zero-Parameter Geometric Gating for Temporally Stable Low-Altitude UAV Video Semantic Segmentation
Beihang University（北京航空航天大学）；Northeastern University（东北大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Beijing Institute of Technology（北京理工大学）
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
The classification and regression head are both indispensable components to build up a dense object detector, which are usually supervised by the same training samples and thus expected to have consistency with each other for detecting objects accurately in the detection pipeline. In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency. MuSu def…
DeGMix: Efficient Multi-Task Dense Prediction with Deformable and Gating Mixer
Department of Computer Science & Technology, Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）；School of Computer Science, Wuhan University（计算机科学学院，武汉大学）；King Abdullah University of Science and Technology（卡布斯大学）
ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection
Faculty of Information Technology, City University, Malaysia（马来西亚城市大学信息技术学院）；Kyung Hee University（庆熙大学）；National University of Sciences and Technology, Pakistan（巴基斯坦国家科技大学）；School of Computing and Artificial Intelligence, Sunway University, Malaysia（马来西亚双威大学计算与人工智能学院）；King Fahd University of Petroleum and Minerals, Saudi Arabia（沙特阿拉伯法赫德国王石油和矿产大学）；SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Saudi Arabia（沙特阿拉伯SDAIA-KFUPM人工智能联合研究中心）
South China University of Technology（华南理工大学）；Tencent AI Lab（腾讯人工智能实验室）
Dense video captioning is a newly emerging task that aims at both localizing and describing all events in a video. We identify and tackle two challenges on this task, namely, (1) how to utilize both past and future contexts for accurate event proposal predictions, and (2) how to construct informative input to the decoder for generating natural event descriptions. First, previous works predominantly generate temporal event proposals in the forward direction, which neglects future video context. We propose a bidirectional proposal method that effectively exploits both past and future contexts to…
Uber Advanced Technologies Group（优步先进技术集团）；University of Toronto（多伦多大学）
Conventional deep convolutional neural networks (CNNs) apply convolution operators uniformly in space across all feature maps for hundreds of layers - this incurs a high computational cost for real-time applications. For many problems such as object detection and semantic segmentation, we are able to obtain a low-cost computation mask, either from a priori problem knowledge, or from a low-resolution segmentation network. We show that such computation masks can be used to reduce computation in the high-resolution main network. Variants of sparse activation CNNs have previously been explored on …
GateMOT: Q-Gated Attention for Dense Object Tracking
Huazhong University of Science and Technology, Wuhan, China（华中科技大学，武汉，中国）；Zhejiang University, Hangzhou, China（浙江大学，杭州，中国）；La Trobe University, Melbourne, Australia（拉筹伯大学，墨尔本，澳大利亚）
CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling. This paper makes two contributions: (1) We demonstrate that while the apparent spatial resolution of convolutional feature maps is low, the high-dimensional feature representation contains significant sub-pixel localization information. (2) We describe a multi-resolution reconstruction architecture based on a Laplacian pyramid that uses skip connections from higher resolution feature maps and multiplicative gating to su…
Videos convey rich information. Dynamic spatio-temporal relationships between people/objects, and diverse multimodal events are present in a video clip. Hence, it is important to develop automated models that can accurately extract such information from videos. Answering questions on videos is one of the tasks which can evaluate such AI abilities. In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions. Specifically, we first employ dense image captions to help identify o…
This paper presents a novel 3D object detection framework that processes LiDAR data directly on its native representation: range images. Benefiting from the compactness of range images, 2D convolutions can efficiently process dense LiDAR data of a scene. To overcome scale sensitivity in this perspective view, a novel range-conditioned dilation (RCD) layer is proposed to dynamically adjust a continuous dilation rate as a function of the measured range. Furthermore, localized soft range gating combined with a 3D box-refinement stage improves robustness in occluded areas, and produces overall mor…
Attention-Guided Dual-Stream Learning for Group Engagement Recognition: Fusing Transformer-Encoded Motion Dynamics with Scene Context via Adaptive Gating
The Pennsylvania State University（宾夕法尼亚州立大学）；Scale AI（Scale AI公司）
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting diverse attributes over multiple categories, 2) modeling attributes-category dependency, 3) capturing both global and local scene context, and 4) predicting attributes of objects with low pixel-count. To address these issues, we propose a novel multi-category attribute prediction deep architecture named Gl…
