Deep learning (DL) has advanced the field of dense prediction, while gradually dissolving the inherent barriers between different tasks. However, most existing works focus on designing architectures and constructing visual cues only for the specific task, which ignores the potential uniformity introduced by the DL paradigm. In this paper, we attempt to construct a novel $\underline{ComP}$lementary $\underline{tr}$ansformer, $\textbf{ComPtr}$, for diverse bi-source dense prediction tasks. Specifically, unlike existing methods that over-specialize in a single task or a subset of tasks, ComPtr starts from the more general concept of bi-source dense prediction. Based on the basic dependence on information complementarity, we propose consistency enhancement and difference awareness components with which ComPtr can evacuate and collect important visual semantic cues from different image sources for diverse tasks, respectively. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer. This task-generic design provides a smooth foundation for constructing the unified model that can simultaneously deal with various bi-source information. In extensive experiments across several representative vision tasks, i.e. remote sensing change detection, RGB-T crowd counting, RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed method consistently obtains favorable performance. The code will be available at https://github.com/lartpang/ComPtr.
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；IBM Research（IBM研究院）；McMaster University（麦克马斯特大学）；National Institute for Space Research (INPE)（巴西国家空间研究所）；Santa Catarina State University (UDESC)（圣卡塔琳娜州立大学）
This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoder…
Currently, existing salient object detection methods based on convolutional neural networks commonly resort to constructing discriminative networks to aggregate high level and low level features. However, contextual information is always not fully and reasonably utilized, which usually causes either the absence of useful features or contamination of redundant features. To address these issues, we propose a novel ladder context correlation complementary network (LC3Net) in this paper, which is equipped with three crucial components. At the beginning, we propose a filterable convolution block (F…
Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense…
In this paper, we are interested in building lightweight and efficient convolutional neural networks. Inspired by the success of two design patterns, composition of structured sparse kernels, e.g., interleaved group convolutions (IGC), and composition of low-rank kernels, e.g., bottle-neck modules, we study the combination of such two design patterns, using the composition of structured sparse low-rank kernels, to form a convolutional kernel. Rather than introducing a complementary condition over channels, we introduce a loose complementary condition, which is formulated by imposing the comple…
Sichuan University（四川大学）；Nankai University（南开大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Wuhan University（武汉大学）
This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such schemes can easily be constrained by a limited amount of training data or over-reliance on an elaborately-designed training process. In contrast, our JL-DCF learns from both RGB and depth inputs through a Siamese network. To this end, we propose two effective components: joint learning (JL), and densely-cooperative fusion (DCF)…
Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional ``deeper'' schemes, this paper proposes a ``wider'' network architecture which consists of parallel sub networks with totally different network architectures. In this way, those deep features obtained via these two sub networks will exhibit large diversity, which will…
CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition
College of Computer Science, Zhejiang University（浙江大学计算机学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Ant Group（蚂蚁集团）；College of Computer Science and Technology, Zhejiang University（计算机科学与技术学院，浙江大学）
A key technical challenge in performing 6D object pose estimation from RGB-D image is to fully leverage the two complementary data sources. Prior works either extract information from the RGB image and depth separately or use costly post-processing steps, limiting their performances in highly cluttered scenes and real-time applications. In this work, we present DenseFusion, a generic framework for estimating 6D pose of a set of known objects from RGB-D images. DenseFusion is a heterogeneous architecture that processes the two data sources individually and uses a novel dense fusion network to e…
Korea University（高丽大学）；Microsoft Research Asia（微软亚洲研究院）
We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggregation. However, they generally aggregate one or the other but not both, though joint aggregation would boost each other by providing information that one has but other lacks, i.e., structural or semantic information of an image, or pixel-wise matching similarity. In this work, we propose a novel Transformer-based network that interleaves both forms of aggregations in a way that exploits their complementary informat…
Spatio-Temporal Difference Guided Motion Deblurring with the Complementary Vision Sensor
Department of Precision Instrument, Tsinghua University（清华大学精密仪器系）；Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University（厦门大学萨本栋微米纳米科学技术研究院）；Communication University of China（中国传媒大学）；Primevision Technology（普视科技）
In video-based action recognition, viewpoint variations often pose major challenges because the same actions can appear different from different views. We use the complementary RGB and Depth information from the RGB-D cameras to address this problem. The proposed technique capitalizes on the spatio-temporal information available in the two data streams to the extract action features that are largely insensitive to the viewpoint variations. We use the RGB data to compute dense trajectories that are translated to viewpoint insensitive deep features under a non-linear knowledge transfer model. Si…
UniGP: Taming Diffusion Transformer for Prior-Preserved Unified Generation and Perception
The Hong Kong University of Science and Technology（香港科学与技术大学）；DAMO Academy, Alibaba Group（阿里云达摩院）；Hupan Lab（虎扑实验室）；Zhejiang University（浙江大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Zeekr Automobile R&D Co., Ltd.（小鹏汽车研发有限公司）
Spatially-guided Temporal Aggregation for Robust Event-RGB Optical Flow Estimation
Department of Automation, University of Science and Technology of China（自动化系，中国科学技术大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）；Department of Mechanical Engineering, City University of Hong Kong（机械工程系，香港城市大学）；College of Computer Science, Beijing University of Technology（计算机科学学院，北京理工大学）
