Italy

Feature Pyramid Network(FPN) employs a top-down path to enhance low level feature by utilizing high level feature. However, further improvement of detector is greatly hindered by the inner defect of FPN. The dilution issue in FPN is analyzed in this paper, and a new architecture named Semantic Feature Pyramid Network(SFPN) is introduced to address the information imbalance problem caused by information dilution. The proposed method consists of two simple and effective components: Semantic Pyramid Module(SPM) and Semantic Feature Fusion Module(SFFM). To compensate for the weaknesses of FPN, the semantic segmentation result is utilized as an extra information source in our architecture.
By constructing a semantic pyramid based on the segmentation result and fusing it with FPN, feature maps at each level can obtain the necessary information without suffering from the dilution issue. The proposed architecture could be applied on many detectors, and non-negligible improvement could be achieved. Although this method is designed for object detection, other tasks such as instance segmentation can also largely benefit from it. The proposed method brings Faster R-CNN and Mask R-CNN with ResNet-50 as backbone both 1.8 AP improvements respectively.
Furthermore, SFPN improves Cascade R-CNN with backbone ResNet-101 from 42.4 AP to 43.5 AP.

ICPR 2020

SFPN: Semantic Feature Pyramid Network for Object Detection

feature pyramid network

object detection

deep learning

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-31, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.

Class Conditional Alignment for Partial Domain Adaptation

In our speech, we present a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on a hat. The adversarial sticker is prepared with a novel algorithm for off-plane transformations of the image which imitates sticker location on the hat. Such an approach confuses the state-of-the-art public Face ID model and is transferable to other Face ID models.

AdvHat: Real-World Adversarial Attack on ArcFace Face ID System

In this paper, we present our Pyramid Pooling Module (PPM) with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transformation module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transformation module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features instead of interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation


Deep learning has thrived by training on large-scale datasets. However, in many applications, as for medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and color discrepancies all at once through pretrained model fine-tuning. We propose a dedicated network module that focuses on color adaptation, thus pre-processing the input into a form (RGB) that is closer to the domain the classification backbone was trained on. We combine learning from scratch of the color module with transfer learning of different classification backbones, obtaining an end-to-end, easy-to-train architecture for diagnostic image recognition on X-ray images. Extensive experiments showed how our approach is particularly efficient in case of data scarcity and provides a new path for further transferring the learned color information across multiple medical datasets. 


Bridging the gap between Natural and Medical Images through Deep Colorization


Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask RCNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

Self-Training for Domain Adaptive Scene Text Detection

Deep convolutional neural networks (DCNN) based methodologies have achieved remarkable performance on various low-level vision tasks recently. Restoring images captured at night is one of the trickiest low-level vision tasks due to its high-level noise and low-level intensity. We propose a DCNN-based methodology, Illumination and Noise Separation Network (INSNet), which performs both denoising and enhancement on these extremely low-light images. INSNet fully utilizes global-ware features and local-ware features using the modified network structure and image sampling scheme. Compared to well-designed complex neural networks, our proposed methodology only needs to add a bypass network to the existing network. However, it can boost the quality of recovered images dramatically but only increase the computational cost by less than 0.1%. Even without any manual settings, INSNet can stably restore the extremely low-light images to desired high-quality images.

Automatical Enhancement and Denoising of Extremely Low-light Images

Completing sparse data captured with commercial depth sensors is a vital and fundamental procedure for many computer vision applications. For execution in real-world scenarios, a good trade-off between accuracy and speed is increasingly in demand for depth completion methods. Most previous methods achieve satisfactory accuracy on standard benchmarks. However, they extensively rely on heavy models to handle diverse structures and require additional run time on multimodal data. In this paper, we present an efficient method of depth completion. We propose a grouped fusion strategy for efficiently extracting depth and guidance features in parallel and fusing them naturally in the feature spaces to achieve high performance. Instead of a monolithic architecture, we employ cascaded hourglass networks, each of which is specialized for certain structures and has a lightweight architecture. Given the sparsity of the depth maps, we downsample the inputs to multiple scales to further accelerate the computation. Our model runs at over 39 FPS on an embedded GPU with high-resolution inputs. Evaluations on the KITTI benchmark demonstrate that the proposed model is an ideal approach for real-world applications.

FastCompletion: A Cascade Network with Multiscale Group-Fused Inputs for Real-Time Depth Completion


Photo retouching features are being integrated into a growing number of mobile applications. Current learning-based approaches enhance images using large convolutional neural network-based models, where the result is received directly from the neural network outputs. This method can lead to artifacts in the resulting images, models that are complicated to interpret, and can be computationally expensive.
In this paper, we explore the application of a filter-based approach in order to overcome the problems outlined above. We focus on creating a lightweight solution suitable for use on mobile devices when designing our model. A significant performance increase was achieved through implementing consistency regularization used in semi-supervised learning. The proposed model can be used on mobile devices and achieves competitive results compared to known models.

LFIEM: Lightweight Filter-based Image Enhancement Model

Visual Question Answering (VQA) is one of the most challenging emerging applications of deep learning. Providing powerful attention mechanisms is crucial for VQA, since the model must correctly identify the region of an image that is relevant to the question at hand. However, existing models analyze the input images at a fixed and typically small resolution, often leading to discarding valuable fine-grained details. To overcome this limitation, in this work we propose a reinforcement learning- based active perception approach that works by applying a series of transformation operations on the images (translation, zoom) in order to facilitate answering the question at hand. This allows for performing fine-grained analysis, effectively increasing the resolution at which the models process information. The proposed method is orthogonal to existing attention mechanisms and it can be combined with most existing VQA methods. The effectiveness of the proposed method is experimentally demonstrated on a challenging VQA dataset.

Improving Visual Question Answering using Active Perception on Static Images


Several deep supervised hashing techniques have been proposed to allow for querying large image databases. However, it is often overlooked that the process of information retrieval can be modeled using information-theoretic metrics, leading to optimizing various proxies for the problem at hand instead. Contrary to this, we propose a deep supervised hashing algorithm that optimizes the learned codes using an information-theoretic measure, the Quadratic Mutual Information (QMI). The proposed method is adapted to the needs of large-scale hashing and information retrieval leading to a novel information-theoretic measure, the Quadratic Spherical Mutual Information (QSMI), that is inspired by QMI, but leads to significant better retrieval precision. Indeed, the effectiveness of the proposed method is demonstrated under several different scenarios, using different datasets and network architectures, outperforming existing deep supervised image hashing techniques.

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Class Conditional Alignment for Partial Domain Adaptation

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