Weakly supervised semantic segmentation (WSSS) with only image-level supervision is a challenging task. Most existing methods exploit Class Activation Maps (CAM) to generate pixel-level pseudo labels for supervised training. However, due to the local receptive field of Convolution Neural Networks (CNN), CAM applied to CNNs often suffers from partial activation -- highlighting the most discriminative part instead of the entire object area. In order to capture both local features and global representations, the Conformer has been proposed to combine a visual transformer branch with a CNN branch. In this paper, we propose TransCAM, a Conformer-based solution to WSSS that explicitly leverages the attention weights from the transformer branch of the Conformer to refine the CAM generated from the CNN branch. TransCAM is motivated by our observation that attention weights from shallow transformer blocks are able to capture low-level spatial feature similarities while attention weights from deep transformer blocks capture high-level semantic context. Despite its simplicity, TransCAM achieves a new state-of-the-art performance of 69.3% and 69.6% on the respective PASCAL VOC 2012 validation and test sets, showing the effectiveness of transformer attention-based refinement of CAM for WSSS.
CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University（合成视觉基础科学国家重点实验室，四川大学）；College of Computer Science, Sichuan University（计算机学院，四川大学）
South China University of Technology（华南理工大学）；The Chinese University of Hong Kong（香港中文大学）；The Open University of Hong Kong（香港公开大学）
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. The texture-aware refinement module computes the covariance matrices of feature responses to extract the texture information, designs an affinity loss to learn a set of parameter maps that help to sepa…
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
The Hong Kong University of Science & Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science & Technology（香港科技大学）；PhiGent Robotics（菲格特机器人公司）；Shanghai Jiao Tong University（上海交通大学）
Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；South China University of Technology（华南理工大学）
Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realistic setting. In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels. State of the art methods typically propose an iterative approach, alternating between generating pseudo-labels and upda…
Effective integration of local and global contextual information is crucial for semantic segmentation and dense image labeling. We develop two encoder-decoder based deep learning architectures to address this problem. We first propose a network architecture called Label Refinement Network (LRN) that predicts segmentation labels in a coarse-to-fine fashion at several spatial resolutions. In this network, we also define loss functions at several stages to provide supervision at different stages of training. However, there are limits to the quality of refinement possible if ambiguous information …
Amazon Lab126（亚马逊Lab126）；University of Washington（华盛顿大学）
To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this problem, we present CameraPose, a weakly-supervised framework for 3D human pose estimation from a single image, which can not only be applied on 2D-3D pose pairs but also on 2D alone annotations. By adding a camera parameter branch, any in-the-wild 2D annotations can be fed into our…
DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection
School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China（计算机网络与信息集成重点实验室（东南大学），教育部，中国）
Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；Computer Vision Center, Barcelona（巴塞罗那计算机视觉中心）；Facebook AI Research（Facebook人工智能研究）
State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segment…
Image-level weakly supervised semantic segmentation (WSSS) relies on class activation maps (CAMs) for pseudo labels generation. As CAMs only highlight the most discriminative regions of objects, the generated pseudo labels are usually unsatisfactory to serve directly as supervision. To solve this, most existing approaches follow a multi-training pipeline to refine CAMs for better pseudo-labels, which includes: 1) re-training the classification model to generate CAMs; 2) post-processing CAMs to obtain pseudo labels; and 3) training a semantic segmentation model with the obtained pseudo labels. …
Assisted Refinement Network Based on Channel Information Interaction for Camouflaged and Salient Object Detection
Department of Physics, J.K. Institute of Science（物理系，J.K.科学研究院）；World Scientific University（世界科学大学）；University of Intelligent Studies（智能研究大学）；School of Computer Science and Technology, Xinjiang University（计算机科学与技术学院，新疆大学）；Department of Electronic Engineering, Tsinghua University（电子工程系，清华大学）
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
School of Cyber Science and Engineering, Huazhong University of Science and Technology（华中科技大学网络空间科学与工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Computer Science & Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Intelligent Science and Technology Academy of CASIC（中国航天科工集团有限公司智能科技研究院）
Tsinghua University（清华大学）；State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Huawei Inc.（华为公司）
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the information loss by depth-agnostic cameras. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. To improve the efficiency of sampling, we propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d paramete…
LDRFusion: A LiDAR-Dominant multimodal refinement framework for 3D object detection
School of Computer Science and Technology, Tongji University, Shanghai 201804, China（计算机科学与技术学院，同济大学，上海）；School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China（电子信息工程学院，同济大学，上海）
University of Science and Technology of China（中国科学技术大学）
Video semantic segmentation aims to generate accurate semantic maps for each video frame. To this end, many works dedicate to integrate diverse information from consecutive frames to enhance the features for prediction, where a feature alignment procedure via estimated optical flow is usually required. However, the optical flow would inevitably suffer from inaccuracy, and then introduce noises in feature fusion and further result in unsatisfactory segmentation results. In this paper, to tackle the misalignment issue, we propose a spatial-temporal fusion (STF) module to model dense pairwise rel…
Southeast University（东南大学）；Shanghai Jiao Tong University（上海交通大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The University of Sydney（悉尼大学）；Hong Kong Baptist University（香港浸会大学）
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns to an interesting working mechanism in agent learning named Complementary Learning System (CLS). CLS holds that the neocortex builds a sensation of general knowledge, while the hippocampus specially learns specific details, completing the learned patterns. Motivated by this simpl…
Human keypoint detection from a single image is very challenging due to occlusion, blur, illumination and scale variance of person instances. In this paper, we find that context information plays an important role in addressing these issues, and propose a novel method named progressive context refinement (PCR) for human keypoint detection. First, we devise a simple but effective context-aware module (CAM) that can efficiently integrate spatial and channel context information to aid feature learning for locating hard keypoints. Then, we construct the PCR model by stacking several CAMs sequentia…
The costly process of obtaining semantic segmentation labels has driven research towards weakly supervised semantic segmentation (WSSS) methods, using only image-level, point, or box labels. The lack of dense scene representation requires methods to increase complexity to obtain additional semantic information about the scene, often done through multiple stages of training and refinement. Current state-of-the-art (SOTA) models leverage image-level labels to produce class activation maps (CAMs) which go through multiple stages of refinement before they are thresholded to make pseudo-masks for s…
Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University of Technology Sydney（悉尼科技大学）；Tongji University（同济大学）
We propose a CNN-based approach for multi-camera markerless motion capture of the human body. Unlike existing methods that first perform pose estimation on individual cameras and generate 3D models as post-processing, our approach makes use of 3D reasoning throughout a multi-stage approach. This novelty allows us to use provisional 3D models of human pose to rethink where the joints should be located in the image and to recover from past mistakes. Our principled refinement of 3D human poses lets us make use of image cues, even from images where we previously misdetected joints, to refine our e…
Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Huawei Inc.（华为公司）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件学院）
Self-supervised Learning of Neural Implicit Feature Fields for Camera Pose Refinement
Faculty of Electrical Engineering, Czech Technical University in Prague（布拉格捷克技术大学电气工程学院）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（布拉格捷克技术大学捷克信息学、机器人学与控制论研究所）；NAVER LABS Europe（NAVER欧洲实验室）
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
Department of Electrical and Computer Engineering, The University of Arizona（电气与计算机工程系，亚利桑那大学）；Center For Urban Informatics and Progress (CUIP), UTC Research Institute, University of Tennessee at Chattanooga（城市信息与进步中心（CUIP）、UTC研究机构、田纳西大学查塔努加分校）；Electrical Engineering Department, University of San Francisco（电气工程系，旧金山大学）
calibfusion: Transformer-Based Differentiable Calibration for Radar-Camera Fusion Detection in Water-Surface Environments
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Tsinghua University（清华大学）；BNRist（北京国家信息科学与技术研究中心）；University of Washington（华盛顿大学）
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc. Orthogonal to them, this work presents a category-level object pose and size refiner CATRE, which is able to iteratively enhance pose estimate from point clouds to produce accurate results. Given an initial pose estimate, CATRE predicts a relative transformation between the initial pose and ground truth by means of aligning the partially observed point cloud and an …
In this paper, we aim to address the challenging task of semantic matching where matching ambiguity is difficult to resolve even with learned deep features. We tackle this problem by taking into account the confidence in predictions and develop a novel refinement strategy to correct partial matching errors. Specifically, we introduce a Confidence-Aware Semantic Matching Network (CAMNet) which instantiates two key ideas of our approach. First, we propose to estimate a dense confidence map for a matching prediction through self-supervised learning. Second, based on the estimated confidence, we r…
SurgAtt-Tracker: Online Surgical Attention Tracking via Temporal Proposal Reranking and Motion-Aware Refinement
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学电子工程系）；Division of Gastrointestinal Surgery, Shenzhen People's Hospital, China（深圳人民医院胃肠外科）；College of Mechatronics and Control Engineering, Shenzhen University, China（深圳大学机电与控制工程学院）；Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China（香港大学机械工程系）；Department of Electronic and Electrical Engineering, Southern University of Science and Technology（南方科技大学电子与电气工程系）
