pwc-archive/papers-with-abstracts huggingface dataset:
FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation
Authors: Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li
Date: 2023-07-20 00:00:00
Tasks: Federated Learning, image-classification, Image Classification, Out-of-Distribution Generalization

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance. We evaluate our method on retinal and pathological image classification tasks, and our proposed method achieves significant improvements for out-of-distribution generalization. Our code is available at https://github.com/ubc-tea/FedSoup.
Is Grad-CAM Explainable in Medical Images?
Authors: Subhashis Suara, Aayush Jha, Pratik Sinha, Arif Ahmed Sekh
Date: 2023-07-20 00:00:00
Tasks: Decision Making, Deep Learning, Interpretable Machine Learning

Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model's decision-making process, increasing interpretability and trust in the results. It is applied in many computer vision (CV) tasks such as classification and explanation. This study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (will be available).
Identifying Interpretable Subspaces in Image Representations
Authors: Neha Kalibhat, Shweta Bhardwaj, Bayan Bruss, Hamed Firooz, Maziar Sanjabi, Soheil Feizi
Date: 2023-07-20 00:00:00
Tasks: counterfactual, Language Modeling, Language Modelling

We propose Automatic Feature Explanation using Contrasting Concepts (FALCON), an interpretability framework to explain features of image representations. For a target feature, FALCON captions its highly activating cropped images using a large captioning dataset (like LAION-400m) and a pre-trained vision-language model like CLIP. Each word among the captions is scored and ranked leading to a small number of shared, human-understandable concepts that closely describe the target feature. FALCON also applies contrastive interpretation using lowly activating (counterfactual) images, to eliminate spurious concepts. Although many existing approaches interpret features independently, we observe in state-of-the-art self-supervised and supervised models, that less than 20% of the representation space can be explained by individual features. We show that features in larger spaces become more interpretable when studied in groups and can be explained with high-order scoring concepts through FALCON. We discuss how extracted concepts can be used to explain and debug failures in downstream tasks. Finally, we present a technique to transfer concepts from one (explainable) representation space to another unseen representation space by learning a simple linear transformation. Code available at https://github.com/NehaKalibhat/falcon-explain.
Eye Disease Classification Using Deep Learning Techniques
Authors: Tareq Babaqi, Manar Jaradat, Ayse Erdem Yildirim, Saif H. Al-Nimer, Daehan Won
Date: 2023-07-19 00:00:00
Tasks: Classification, Deep Learning, Multi-class Classification, Transfer Learning

Eye is the essential sense organ for vision function. Due to the fact that certain eye disorders might result in vision loss, it is essential to diagnose and treat eye diseases early on. By identifying common eye illnesses and performing an eye check, eye care providers can safeguard patients against vision loss or blindness. Convolutional neural networks (CNN) and transfer learning were employed in this study to discriminate between a normal eye and one with diabetic retinopathy, cataract, or glaucoma disease. Using transfer learning for multi-class classification, high accuracy was achieved at 94% while the traditional CNN achieved 84% rate.
Mining Conditional Part Semantics with Occluded Extrapolation for Human-Object Interaction Detection
Authors: Guangzhi Wang, Yangyang Guo, Mohan Kankanhalli
Date: 2023-07-19 00:00:00
Tasks: Human-Object Interaction Detection, Object, Scene Understanding

Human-Object Interaction Detection is a crucial aspect of human-centric scene understanding, with important applications in various domains. Despite recent progress in this field, recognizing subtle and detailed interactions remains challenging. Existing methods try to use human-related clues to alleviate the difficulty, but rely heavily on external annotations or knowledge, limiting their practical applicability in real-world scenarios. In this work, we propose a novel Part Semantic Network (PSN) to solve this problem. The core of PSN is a Conditional Part Attention (CPA) mechanism, where human features are taken as keys and values, and the object feature is used as query for the computation in a cross-attention mechanism. In this way, our model learns to automatically focus on the most informative human parts conditioned on the involved object, generating more semantically meaningful features for interaction recognition. Additionally, we propose an Occluded Part Extrapolation (OPE) strategy to facilitate interaction recognition under occluded scenarios, which teaches the model to extrapolate detailed features from partially occluded ones. Our method consistently outperforms prior approaches on the V-COCO and HICO-DET datasets, without external data or extra annotations. Additional ablation studies validate the effectiveness of each component of our proposed method.
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
Authors: James Chapman, Bohan Chen, Zheng Tan, Jeff Calder, Kevin Miller, Andrea L. Bertozzi
Date: 2023-07-19 00:00:00
Tasks: Active Learning, Graph Learning, Transfer Learning

Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data arXiv:2204.00005. In each iteration, sequential active learning selects a query set of size one while batch active learning selects a query set of multiple datapoints. While batch active learning methods exhibit greater efficiency, the challenge lies in maintaining model accuracy relative to sequential active learning methods. We developed a novel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set (DAC) for core-set generation and LocalMax for batch sampling. The batch active learning process that combines DAC and LocalMax achieves nearly identical accuracy as sequential active learning but is more efficient, proportional to the batch size. As an application, a pipeline is built based on transfer learning feature embedding, graph learning, DAC, and LocalMax to classify the FUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms the state-of-the-art CNN-based methods.
Findings of Factify 2: Multimodal Fake News Detection
Authors: S Suryavardan, Shreyash Mishra, Megha Chakraborty, Parth Patwa, Anku Rani, Aman Chadha, Aishwarya Reganti, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar
Date: 2023-07-19 00:00:00
Tasks: Fact Verification, Fake News Detection

With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.
Confidence Estimation Using Unlabeled Data
Authors: Chen Li, Xiaoling Hu, Chao Chen
Date: 2023-07-19 00:00:00
Tasks: Active Learning, image-classification, Image Classification

Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task. The code is available at https://github.com/TopoXLab/consistency-ranking-loss
POV-Surgery: A Dataset for Egocentric Hand and Tool Pose Estimation During Surgical Activities
Authors: Rui Wang, Sophokles Ktistakis, Siwei Zhang, Mirko Meboldt, Quentin Lohmeyer
Date: 2023-07-19 00:00:00
Tasks: 3D Hand Pose Estimation, hand-object pose, Mixed Reality, Pose Estimation, Semantic Segmentation, Synthetic Data Generation

The surgical usage of Mixed Reality (MR) has received growing attention in areas such as surgical navigation systems, skill assessment, and robot-assisted surgeries. For such applications, pose estimation for hand and surgical instruments from an egocentric perspective is a fundamental task and has been studied extensively in the computer vision field in recent years. However, the development of this field has been impeded by a lack of datasets, especially in the surgical field, where bloody gloves and reflective metallic tools make it hard to obtain 3D pose annotations for hands and objects using conventional methods. To address this issue, we propose POV-Surgery, a large-scale, synthetic, egocentric dataset focusing on pose estimation for hands with different surgical gloves and three orthopedic surgical instruments, namely scalpel, friem, and diskplacer. Our dataset consists of 53 sequences and 88,329 frames, featuring high-resolution RGB-D video streams with activity annotations, accurate 3D and 2D annotations for hand-object pose, and 2D hand-object segmentation masks. We fine-tune the current SOTA methods on POV-Surgery and further show the generalizability when applying to real-life cases with surgical gloves and tools by extensive evaluations. The code and the dataset are publicly available at batfacewayne.github.io/POV_Surgery_io/.
TokenFlow: Consistent Diffusion Features for Consistent Video Editing
Authors: Michal Geyer, Omer Bar-Tal, Shai Bagon, Tali Dekel
Date: 2023-07-19 00:00:00
Tasks: Video Editing

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io/
Improving Multimodal Datasets with Image Captioning
Date: 2023-07-19 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image Captioning

Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to reduce noise often come at the expense of data diversity. Our work focuses on caption quality as one major source of noise, and studies how generated captions can increase the utility of web-scraped datapoints with nondescript text. Through exploring different mixing strategies for raw and generated captions, we outperform the best filtering method proposed by the DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a candidate pool of 128M image-text pairs. Our best approach is also 2x better at Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an effective source of text supervision. In experimenting with different image captioning models, we also demonstrate that the performance of a model on standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable indicator of the utility of the captions it generates for multimodal training. Finally, our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text, as well as the importance of image curation with increasing training data quantity. The synthetic captions used in our experiments are now available on HuggingFace.
Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A User-Study
Authors: Ioannis Sarridis, Jochen Spangenberg, Olga Papadopoulou, Symeon Papadopoulos
Date: 2023-07-19 00:00:00

Exposure to disturbing imagery can significantly impact individuals, especially professionals who encounter such content as part of their work. This paper presents a user study, involving 107 participants, predominantly journalists and human rights investigators, that explores the capability of Artificial Intelligence (AI)-based image filters to potentially mitigate the emotional impact of viewing such disturbing content. We tested five different filter styles, both traditional (Blurring and Partial Blurring) and AI-based (Drawing, Colored Drawing, and Painting), and measured their effectiveness in terms of conveying image information while reducing emotional distress. Our findings suggest that the AI-based Drawing style filter demonstrates the best performance, offering a promising solution for reducing negative feelings (-30.38%) while preserving the interpretability of the image (97.19%). Despite the requirement for many professionals to eventually inspect the original images, participants suggested potential strategies for integrating AI filters into their workflow, such as using AI filters as an initial, preparatory step before viewing the original image. Overall, this paper contributes to the development of a more ethically considerate and effective visual environment for professionals routinely engaging with potentially disturbing imagery.
Towards Automated Semantic Segmentation in Mammography Images
Authors: Cesar A. Sierra-Franco, Jan Hurtado, Victor de A. Thomaz, Leonardo C. da Cruz, Santiago V. Silva, Alberto B. Raposo
Date: 2023-07-18 00:00:00
Tasks: Deep Learning, Segmentation, Semantic Segmentation

Mammography images are widely used to detect non-palpable breast lesions or nodules, preventing cancer and providing the opportunity to plan interventions when necessary. The identification of some structures of interest is essential to make a diagnosis and evaluate image adequacy. Thus, computer-aided detection systems can be helpful in assisting medical interpretation by automatically segmenting these landmark structures. In this paper, we propose a deep learning-based framework for the segmentation of the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-view mammography images. We introduce a large private segmentation dataset and extensive experiments considering different deep-learning model architectures. Our experiments demonstrate accurate segmentation performance on variate and challenging cases, showing that this framework can be integrated into clinical practice.
ECSIC: Epipolar Cross Attention for Stereo Image Compression
Authors: Matthias Wödlinger, Jan Kotera, Manuel Keglevic, Jan Xu, Robert Sablatnig
Date: 2023-07-18 00:00:00
Tasks: Image Compression

In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-the-art performance in stereo image compression on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding.
Semi-supervised Cycle-GAN for face photo-sketch translation in the wild
Authors: Chaofeng Chen, Wei Liu, Xiao Tan, Kwan-Yee K. Wong
Date: 2023-07-18 00:00:00
Tasks: Translation

The performance of face photo-sketch translation has improved a lot thanks to deep neural networks. GAN based methods trained on paired images can produce high-quality results under laboratory settings. Such paired datasets are, however, often very small and lack diversity. Meanwhile, Cycle-GANs trained with unpaired photo-sketch datasets suffer from the \emph{steganography} phenomenon, which makes them not effective to face photos in the wild. In this paper, we introduce a semi-supervised approach with a noise-injection strategy, named Semi-Cycle-GAN (SCG), to tackle these problems. For the first problem, we propose a {\em pseudo sketch feature} representation for each input photo composed from a small reference set of photo-sketch pairs, and use the resulting {\em pseudo pairs} to supervise a photo-to-sketch generator $G_{p2s}$. The outputs of $G_{p2s}$ can in turn help to train a sketch-to-photo generator $G_{s2p}$ in a self-supervised manner. This allows us to train $G_{p2s}$ and $G_{s2p}$ using a small reference set of photo-sketch pairs together with a large face photo dataset (without ground-truth sketches). For the second problem, we show that the simple noise-injection strategy works well to alleviate the \emph{steganography} effect in SCG and helps to produce more reasonable sketch-to-photo results with less overfitting than fully supervised approaches. Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.
Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value Ranges
Authors: Daniel Braun, Rita Borgo, Max Sondag, Tatiana von Landesberger
Date: 2023-07-18 00:00:00
Tasks: Time Series

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.
Survey on Controlable Image Synthesis with Deep Learning
Authors: ShiXiong Zhang, Jiao Li, Lu Yang
Date: 2023-07-18 00:00:00
Tasks: Deep Learning, Image Generation, Survey

Image synthesis has attracted emerging research interests in academic and industry communities. Deep learning technologies especially the generative models greatly inspired controllable image synthesis approaches and applications, which aim to generate particular visual contents with latent prompts. In order to further investigate low-level controllable image synthesis problem which is crucial for fine image rendering and editing tasks, we present a survey of some recent works on 3D controllable image synthesis using deep learning. We first introduce the datasets and evaluation indicators for 3D controllable image synthesis. Then, we review the state-of-the-art research for geometrically controllable image synthesis in two aspects: 1) Viewpoint/pose-controllable image synthesis; 2) Structure/shape-controllable image synthesis. Furthermore, the photometrically controllable image synthesis approaches are also reviewed for 3D re-lighting researches. While the emphasis is on 3D controllable image synthesis algorithms, the related applications, products and resources are also briefly summarized for practitioners.