pwc-archive/papers-with-abstracts huggingface dataset:
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation
Authors: Md Shazid Islam, Sayak Nag, Arindam Dutta, Miraj Ahmed, Fahim Faisal Niloy, Amit K. Roy-Chowdhury
Date: 2023-12-08 00:00:00
Tasks: Active Learning, Domain Adaptation, Federated Learning, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Privacy Preserving, Semantic Segmentation

Unsupervised domain adaptive segmentation typically relies on self-training using pseudo labels predicted by a pre-trained network on an unlabeled target dataset. However, the noisy nature of such pseudo-labels presents a major bottleneck in adapting a network to the distribution shift between source and target datasets. This challenge is exaggerated when the network encounters an incoming data stream in online fashion, where the network is constrained to adapt to incoming streams of target domain data in exactly one round of forward and backward passes. In this scenario, relying solely on inaccurate pseudo-labels can lead to low-quality segmentation, which is detrimental to medical image analysis where accuracy and precision are of utmost priority. We hypothesize that a small amount of pixel-level annotation obtained from an expert can address this problem, thereby enhancing the performance of domain adaptation of online streaming data, even in the absence of dedicated training data. We call our method ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation that adapts to each incoming data batch in an online setup, incorporating feedback from an expert through active learning. Through active learning, the most informative pixels in each image can be selected for expert annotation. However, the acquisition of pixel-level annotations across all images in a batch often leads to redundant information while increasing temporal overhead in online learning. To reduce the annotation acquisition time and make the adaptation process more online-friendly, we further propose a novel image-pruning strategy that selects the most useful subset of images from the current batch for active learning. Our proposed approach outperforms existing online adaptation approaches and produces competitive results compared to offline domain adaptive active learning methods.
Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook
Authors: Reza Azad, Moein Heidary, Kadir Yilmaz, Michael Hüttemann, Sanaz Karimijafarbigloo, Yuli Wu, Anke Schmeink, Dorit Merhof
Date: 2023-12-08 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, loss functions are crucial for shaping the development of deep learning-based segmentation algorithms and improving their overall performance. To aid researchers in identifying the optimal loss function for their particular application, this survey provides a comprehensive and unified review of $25$ loss functions utilized in image segmentation. We provide a novel taxonomy and thorough review of how these loss functions are customized and leveraged in image segmentation, with a systematic categorization emphasizing their significant features and applications. Furthermore, to evaluate the efficacy of these methods in real-world scenarios, we propose unbiased evaluations of some distinct and renowned loss functions on established medical and natural image datasets. We conclude this review by identifying current challenges and unveiling future research opportunities. Finally, we have compiled the reviewed studies that have open-source implementations on our GitHub page.
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models
Authors: Yusuf Dalva, Pinar Yanardag
Date: 2023-12-08 00:00:00
Proceeding: CVPR 2024 1
Tasks: Contrastive Learning, Image Generation

Generative models have been very popular in the recent years for their image generation capabilities. GAN-based models are highly regarded for their disentangled latent space, which is a key feature contributing to their success in controlled image editing. On the other hand, diffusion models have emerged as powerful tools for generating high-quality images. However, the latent space of diffusion models is not as thoroughly explored or understood. Existing methods that aim to explore the latent space of diffusion models usually relies on text prompts to pinpoint specific semantics. However, this approach may be restrictive in areas such as art, fashion, or specialized fields like medicine, where suitable text prompts might not be available or easy to conceive thus limiting the scope of existing work. In this paper, we propose an unsupervised method to discover latent semantics in text-to-image diffusion models without relying on text prompts. Our method takes a small set of unlabeled images from specific domains, such as faces or cats, and a pre-trained diffusion model, and discovers diverse semantics in unsupervised fashion using a contrastive learning objective. Moreover, the learned directions can be applied simultaneously, either within the same domain (such as various types of facial edits) or across different domains (such as applying cat and face edits within the same image) without interfering with each other. Our extensive experiments show that our method achieves highly disentangled edits, outperforming existing approaches in both diffusion-based and GAN-based latent space editing methods.
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Authors: Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, Phillip Isola
Date: 2023-06-15 00:00:00
Proceeding: NeurIPS 2023 11

Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture mid-level similarities and differences in image layout, object pose, and semantic content. In this paper, we develop a perceptual metric that assesses images holistically. Our first step is to collect a new dataset of human similarity judgments over image pairs that are alike in diverse ways. Critical to this dataset is that judgments are nearly automatic and shared by all observers. To achieve this we use recent text-to-image models to create synthetic pairs that are perturbed along various dimensions. We observe that popular perceptual metrics fall short of explaining our new data, and we introduce a new metric, DreamSim, tuned to better align with human perception. We analyze how our metric is affected by different visual attributes, and find that it focuses heavily on foreground objects and semantic content while also being sensitive to color and layout. Notably, despite being trained on synthetic data, our metric generalizes to real images, giving strong results on retrieval and reconstruction tasks. Furthermore, our metric outperforms both prior learned metrics and recent large vision models on these tasks.
Unmixing Optical Signals from Undersampled Volumetric Measurements by Filtering the Pixel Latent Variables
Authors: Catherine Bouchard, Andréanne Deschênes, Vincent Boulanger, Jean-Michel Bellavance, Julia Chabbert, Alexy Pelletier-Rioux, Flavie Lavoie-Cardinal, Christian Gagné
Date: 2023-12-08 00:00:00

The development of signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific imaging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enhancing the spatio-temporal resolution or expanding the number of detection channels often leads to diminished sampling rate and signal-to-noise ratio (SNR), significantly affecting the efficacy of signal unmixing algorithms. We propose Latent Unmixing, a new approach which applies band-pass filters to the latent space of a multi-dimensional convolutional neural network to disentangle overlapping signal components. It enables better isolation and quantification of individual signal contributions, especially in the context of undersampled distributions. Using multi-dimensional convolution kernels to process all dimensions simultaneously enhances the network's ability to extract information from adjacent pixels, and time- or spectral-bins. This approach enables more effective separation of components in cases where individual pixels do not provide clear, well-resolved information. We showcase the method's practical use in experimental physics through two test cases that highlight the versatility of our approach: fluorescence lifetime microscopy and mode decomposition in optical fibers. The latent unmixing method extracts valuable information from complex signals that cannot be resolved by standard methods. It opens new possibilities in optics and photonics for multichannel separations at an increased sampling rate.
Neither hype nor gloom do DNNs justice
Authors: Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Date: 2023-12-08 00:00:00

Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other.
A Review of Machine Learning Methods Applied to Video Analysis Systems
Authors: Marios S. Pattichis, Venkatesh Jatla, Alvaro E. Ullao Cerna
Date: 2023-12-08 00:00:00
Tasks: Active Learning, Activity Recognition, Human Activity Recognition, Self-Supervised Learning, Survey, Zero-Shot Learning

The paper provides a survey of the development of machine-learning techniques for video analysis. The survey provides a summary of the most popular deep learning methods used for human activity recognition. We discuss how popular architectures perform on standard datasets and highlight the differences from real-life datasets dominated by multiple activities performed by multiple participants over long periods. For real-life datasets, we describe the use of low-parameter models (with 200X or 1,000X fewer parameters) that are trained to detect a single activity after the relevant objects have been successfully detected. Our survey then turns to a summary of machine learning methods that are specifically developed for working with a small number of labeled video samples. Our goal here is to describe modern techniques that are specifically designed so as to minimize the amount of ground truth that is needed for training and testing video analysis systems. We provide summaries of the development of self-supervised learning, semi-supervised learning, active learning, and zero-shot learning for applications in video analysis. For each method, we provide representative examples.
High-Quality Live Video Streaming via Transcoding Time Prediction and Preset Selection
Authors: Zahra Nabizadeh Shahre-Babak, Nader Karimi, Krishna Rapaka, Tarek Amara, Shadrokh Samavi, Shahram Shirani
Date: 2023-12-08 00:00:00
Tasks: Prediction

Video streaming often requires transcoding content into different resolutions and bitrates to match the recipient's internet speed and screen capabilities. Video encoders like x264 offer various presets, each with different tradeoffs between transcoding time and rate-distortion performance. Choosing the best preset for video transcoding is difficult, especially for live streaming, as trying all the presets and choosing the best one is not feasible. One solution is to predict each preset's transcoding time and select the preset that ensures the highest quality while adhering to live streaming time constraints. Prediction of video transcoding time is also critical in minimizing streaming delays, deploying resource management algorithms, and load balancing. We propose a learning-based framework for predicting the transcoding time of videos across various presets. Our predictor's features for video transcoding time prediction are derived directly from the ingested stream, primarily from the header or metadata. As a result, only minimal additional delay is incurred for feature extraction, rendering our approach ideal for live-streaming applications. We evaluated our learning-based transcoding time prediction using a dataset of videos. The results demonstrate that our framework can accurately predict the transcoding time for different presets, with a mean absolute percentage error (MAPE) of nearly 5.0%. Leveraging these predictions, we then select the most suitable transcoding preset for live video streaming. Utilizing our transcoding time prediction-based preset selection improved Peak Signal-to-Noise Ratio (PSNR) of up to 5 dB.
ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging
Authors: Sulaiman Vesal, Indrani Bhattacharya, Hassan Jahanandish, Xinran Li, Zachary Kornberg, Steve Ran Zhou, Elijah Richard Sommer, Moon Hyung Choi, Richard E. Fan, Geoffrey A. Sonn, Mirabela Rusu
Date: 2023-12-08 00:00:00
Tasks: Diagnostic, Lesion Detection, Sensitivity, Specificity

Interpreting traditional B-mode ultrasound images can be challenging due to image artifacts (e.g., shadowing, speckle), leading to low sensitivity and limited diagnostic accuracy. While Magnetic Resonance Imaging (MRI) has been proposed as a solution, it is expensive and not widely available. Furthermore, most biopsies are guided by Transrectal Ultrasound (TRUS) alone and can miss up to 52% cancers, highlighting the need for improved targeting. To address this issue, we propose ProsDectNet, a multi-task deep learning approach that localizes prostate cancer on B-mode ultrasound. Our model is pre-trained using radiologist-labeled data and fine-tuned using biopsy-confirmed labels. ProsDectNet includes a lesion detection and patch classification head, with uncertainty minimization using entropy to improve model performance and reduce false positive predictions. We trained and validated ProsDectNet using a cohort of 289 patients who underwent MRI-TRUS fusion targeted biopsy. We then tested our approach on a group of 41 patients and found that ProsDectNet outperformed the average expert clinician in detecting prostate cancer on B-mode ultrasound images, achieving a patient-level ROC-AUC of 82%, a sensitivity of 74%, and a specificity of 67%. Our results demonstrate that ProsDectNet has the potential to be used as a computer-aided diagnosis system to improve targeted biopsy and treatment planning.
Multi-view Inversion for 3D-aware Generative Adversarial Networks
Authors: Florian Barthel, Anna Hilsmann, Peter Eisert
Date: 2023-12-08 00:00:00
Tasks: NeRF

Current 3D GAN inversion methods for human heads typically use only one single frontal image to reconstruct the whole 3D head model. This leaves out meaningful information when multi-view data or dynamic videos are available. Our method builds on existing state-of-the-art 3D GAN inversion techniques to allow for consistent and simultaneous inversion of multiple views of the same subject. We employ a multi-latent extension to handle inconsistencies present in dynamic face videos to re-synthesize consistent 3D representations from the sequence. As our method uses additional information about the target subject, we observe significant enhancements in both geometric accuracy and image quality, particularly when rendering from wide viewing angles. Moreover, we demonstrate the editability of our inverted 3D renderings, which distinguishes them from NeRF-based scene reconstructions.
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Authors: Ribana Roscher, Marc Rußwurm, Caroline Gevaert, Michael Kampffmeyer, Jefersson A. dos Santos, Maria Vakalopoulou, Ronny Hänsch, Stine Hansen, Keiller Nogueira, Jonathan Prexl, Devis Tuia
Date: 2023-12-08 00:00:00
Tasks: Earth Observation

Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models have been proposed, the majority of them have been solely developed on benchmark datasets that lack strong real-world relevance. Furthermore, the performance of many methods has already saturated on these datasets. We argue that a shift from a model-centric view to a complementary data-centric perspective is necessary for further improvements in accuracy, generalization ability, and real impact on end-user applications. Furthermore, considering the entire machine learning cycle-from problem definition to model deployment with feedback-is crucial for enhancing machine learning models that can be reliable in unforeseen situations. This work presents a definition as well as a precise categorization and overview of automated data-centric learning approaches for geospatial data. It highlights the complementary role of data-centric learning with respect to model-centric in the larger machine learning deployment cycle. We review papers across the entire geospatial field and categorize them into different groups. A set of representative experiments shows concrete implementation examples. These examples provide concrete steps to act on geospatial data with data-centric machine learning approaches.
360° Volumetric Portrait Avatar
Authors: Jalees Nehvi, Berna Kabadayi, Julien Valentin, Justus Thies
Date: 2023-12-08 00:00:00
Tasks: Human Parsing, Monocular Reconstruction

We propose 360{\deg} Volumetric Portrait (3VP) Avatar, a novel method for reconstructing 360{\deg} photo-realistic portrait avatars of human subjects solely based on monocular video inputs. State-of-the-art monocular avatar reconstruction methods rely on stable facial performance capturing. However, the common usage of 3DMM-based facial tracking has its limits; side-views can hardly be captured and it fails, especially, for back-views, as required inputs like facial landmarks or human parsing masks are missing. This results in incomplete avatar reconstructions that only cover the frontal hemisphere. In contrast to this, we propose a template-based tracking of the torso, head and facial expressions which allows us to cover the appearance of a human subject from all sides. Thus, given a sequence of a subject that is rotating in front of a single camera, we train a neural volumetric representation based on neural radiance fields. A key challenge to construct this representation is the modeling of appearance changes, especially, in the mouth region (i.e., lips and teeth). We, therefore, propose a deformation-field-based blend basis which allows us to interpolate between different appearance states. We evaluate our approach on captured real-world data and compare against state-of-the-art monocular reconstruction methods. In contrast to those, our method is the first monocular technique that reconstructs an entire 360{\deg} avatar.
Disentangled Clothed Avatar Generation from Text Descriptions
Authors: Jionghao Wang, YuAn Liu, Zhiyang Dou, Zhengming Yu, Yongqing Liang, Cheng Lin, Xin Li, Wenping Wang, Rong Xie, Li Song
Date: 2023-12-08 00:00:00
Tasks: Virtual Try-on

In this paper, we introduce a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar. While recent advancements in text-to-avatar generation have yielded diverse human avatars from text prompts, these methods typically combine all elements-clothes, hair, and body-into a single 3D representation. Such an entangled approach poses challenges for downstream tasks like editing or animation. To overcome these limitations, we propose a novel disentangled 3D avatar representation named Sequentially Offset-SMPL (SO-SMPL), building upon the SMPL model. SO-SMPL represents the human body and clothes with two separate meshes but associates them with offsets to ensure the physical alignment between the body and the clothes. Then, we design a Score Distillation Sampling (SDS)-based distillation framework to generate the proposed SO-SMPL representation from text prompts. Our approach not only achieves higher texture and geometry quality and better semantic alignment with text prompts, but also significantly improves the visual quality of character animation, virtual try-on, and avatar editing. Project page: https://shanemankiw.github.io/SO-SMPL/.
Noise Adaptor in Spiking Neural Networks
Authors: Chen Li, Bipin Rajendran
Date: 2023-12-08 00:00:00

Recent strides in low-latency spiking neural network (SNN) algorithms have drawn significant interest, particularly due to their event-driven computing nature and fast inference capability. One of the most efficient ways to construct a low-latency SNN is by converting a pre-trained, low-bit artificial neural network (ANN) into an SNN. However, this conversion process faces two main challenges: First, converting SNNs from low-bit ANNs can lead to ``occasional noise" -- the phenomenon where occasional spikes are generated in spiking neurons where they should not be -- during inference, which significantly lowers SNN accuracy. Second, although low-latency SNNs initially show fast improvements in accuracy with time steps, these accuracy growths soon plateau, resulting in their peak accuracy lagging behind both full-precision ANNs and traditional ``long-latency SNNs'' that prioritize precision over speed. In response to these two challenges, this paper introduces a novel technique named ``noise adaptor.'' Noise adaptor can model occasional noise during training and implicitly optimize SNN accuracy, particularly at high simulation times $T$. Our research utilizes the ResNet model for a comprehensive analysis of the impact of the noise adaptor on low-latency SNNs. The results demonstrate that our method outperforms the previously reported quant-ANN-to-SNN conversion technique. We achieved an accuracy of 95.95\% within 4 time steps on CIFAR-10 using ResNet-18, and an accuracy of 74.37\% within 64 time steps on ImageNet using ResNet-50. Remarkably, these results were obtained without resorting to any noise correction methods during SNN inference, such as negative spikes or two-stage SNN simulations. Our approach significantly boosts the peak accuracy of low-latency SNNs, bringing them on par with the accuracy of full-precision ANNs. Code will be open source.