pwc-archive/papers-with-abstracts huggingface dataset:
Improving Position Encoding of Transformers for Multivariate Time Series Classification
Authors: Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb, Mahsa Salehi
Date: 2023-05-26 00:00:00
Tasks: Anomaly Detection, Position, Time Series, Time Series Analysis, Time Series Classification

Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is better to inject absolute position encoding or relative position encoding, or a combination of them. In order to clarify this, we first review existing absolute and relative position encoding methods when applied in time series classification. We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE). Our new method incorporates the series length and input embedding dimension in absolute position encoding. Additionally, we propose computationally Efficient implementation of Relative Position Encoding (eRPE) to improve generalisability for time series. We then propose a novel multivariate time series classification (MTSC) model combining tAPE/eRPE and convolution-based input encoding named ConvTran to improve the position and data embedding of time series data. The proposed absolute and relative position encoding methods are simple and efficient. They can be easily integrated into transformer blocks and used for downstream tasks such as forecasting, extrinsic regression, and anomaly detection. Extensive experiments on 32 multivariate time-series datasets show that our model is significantly more accurate than state-of-the-art convolution and transformer-based models. Code and models are open-sourced at \url{https://github.com/Navidfoumani/ConvTran}.
Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning
Authors: Sanjoy Kundu, Shubham Trehan, Sathyanarayanan N. Aakur
Date: 2023-05-26 00:00:00
Tasks: Object Recognition, Visual Commonsense Reasoning

Learning to infer labels in an open world, i.e., in an environment where the target ``labels'' are unknown, is an important characteristic for achieving autonomy. Foundation models, pre-trained on enormous amounts of data, have shown remarkable generalization skills through prompting, particularly in zero-shot inference. However, their performance is restricted to the correctness of the target label's search space, i.e., candidate labels provided in the prompt. This target search space can be unknown or exceptionally large in an open world, severely restricting their performance. To tackle this challenging problem, we propose a two-step, neuro-symbolic framework called ALGO - Action Learning with Grounded Object recognition that uses symbolic knowledge stored in large-scale knowledge bases to infer activities in egocentric videos with limited supervision. First, we propose a neuro-symbolic prompting approach that uses object-centric vision-language models as a noisy oracle to ground objects in the video through evidence-based reasoning. Second, driven by prior commonsense knowledge, we discover plausible activities through an energy-based symbolic pattern theory framework and learn to ground knowledge-based action (verb) concepts in the video. Extensive experiments on four publicly available datasets (EPIC-Kitchens, GTEA Gaze, GTEA Gaze Plus, and Charades-Ego) demonstrate its performance on open-world activity inference. We also show that ALGO can be extended to zero-shot inference and demonstrate its competitive performance on the Charades-Ego dataset.
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
Authors: Xue Zhang, Xiaohan Zhang, Jiangtao Wang, Jiacheng Ying, Zehua Sheng, Heng Yu, Chunguang Li, Hui-Liang Shen
Date: 2023-05-26 00:00:00
Tasks: Multispectral Object Detection, object-detection, Object Detection, Pedestrian Detection

Pedestrian detection plays a critical role in computer vision as it contributes to ensuring traffic safety. Existing methods that rely solely on RGB images suffer from performance degradation under low-light conditions due to the lack of useful information. To address this issue, recent multispectral detection approaches have combined thermal images to provide complementary information and have obtained enhanced performances. Nevertheless, few approaches focus on the negative effects of false positives caused by noisy fused feature maps. Different from them, we comprehensively analyze the impacts of false positives on the detection performance and find that enhancing feature contrast can significantly reduce these false positives. In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet. TFDet achieves state-of-the-art performance on two multispectral pedestrian benchmarks, KAIST and LLVIP. TFDet can easily extend to multi-class object detection scenarios. It outperforms the previous best approaches on two multispectral object detection benchmarks, FLIR and M3FD. Importantly, TFDet has comparable inference efficiency to the previous approaches, and has remarkably good detection performance even under low-light conditions, which is a significant advancement for ensuring road safety.
Structured Latent Variable Models for Articulated Object Interaction
Authors: Emily Liu, Michael Noseworthy, Nicholas Roy
Date: 2023-05-26 00:00:00
Tasks: Object

In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing. This representation can be used to infer door-related parameters and predict the outcomes of interacting with the door. Current machine learning based approaches in the doors domain are based primarily on labelled datasets. However, the large quantity of available door data suggests the feasibility of a semisupervised approach based on pretraining. To exploit the hierarchical structure of the dataset where each door has multiple associated images, we pretrain with a structured latent variable model known as a neural statistician. The neural satsitician enforces separation between shared context-level variables (common across all images associated with the same door) and instance-level variables (unique to each individual image). We first demonstrate that the neural statistician is able to learn an embedding that enables reconstruction and sampling of realistic door images. Then, we evaluate the correspondence of the learned embeddings to human-interpretable parameters in a series of supervised inference tasks. It was found that a pretrained neural statistician encoder outperformed analogous context-free baselines when predicting door handedness, size, angle location, and configuration from door images. Finally, in a visual bandit door-opening task with a variety of door configuration, we found that neural statistician embeddings achieve lower regret than context-free baselines.
Integrating Listwise Ranking into Pairwise-based Image-Text Retrieval
Authors: Zheng Li, Caili Guo, Xin Wang, Zerun Feng, Yanjun Wang
Date: 2023-05-26 00:00:00
Tasks: Image-text Retrieval, Retrieval, Text Retrieval, Triplet

Image-Text Retrieval (ITR) is essentially a ranking problem. Given a query caption, the goal is to rank candidate images by relevance, from large to small. The current ITR datasets are constructed in a pairwise manner. Image-text pairs are annotated as positive or negative. Correspondingly, ITR models mainly use pairwise losses, such as triplet loss, to learn to rank. Pairwise-based ITR increases positive pair similarity while decreasing negative pair similarity indiscriminately. However, the relevance between dissimilar negative pairs is different. Pairwise annotations cannot reflect this difference in relevance. In the current datasets, pairwise annotations miss many correlations. There are many potential positive pairs among the pairs labeled as negative. Pairwise-based ITR can only rank positive samples before negative samples, but cannot rank negative samples by relevance. In this paper, we integrate listwise ranking into conventional pairwise-based ITR. Listwise ranking optimizes the entire ranking list based on relevance scores. Specifically, we first propose a Relevance Score Calculation (RSC) module to calculate the relevance score of the entire ranked list. Then we choose the ranking metric, Normalized Discounted Cumulative Gain (NDCG), as the optimization objective. We transform the non-differentiable NDCG into a differentiable listwise loss, named Smooth-NDCG (S-NDCG). Our listwise ranking approach can be plug-and-play integrated into current pairwise-based ITR models. Experiments on ITR benchmarks show that integrating listwise ranking can improve the performance of current ITR models and provide more user-friendly retrieval results. The code is available at https://github.com/AAA-Zheng/Listwise_ITR.
CVB: A Video Dataset of Cattle Visual Behaviors
Authors: Ali Zia, Renuka Sharma, Reza Arablouei, Greg Bishop-hurley, Jody McNally, Neil Bagnall, Vivien Rolland, Brano Kusy, Lars Petersson, Aaron Ingham
Date: 2023-05-26 00:00:00
Tasks: Action Recognition

Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long, captured in natural lighting conditions, and annotated with eleven visually perceptible behaviors of grazing cattle. We use the Computer Vision Annotation Tool (CVAT) to collect our annotations. To make the procedure more efficient, we perform an initial detection and tracking of cattle in the videos using appropriate pre-trained models. The results are corrected by domain experts along with cattle behavior labeling in CVAT. The pre-hoc detection and tracking step significantly reduces the manual annotation time and effort. Moreover, we convert CVB to the atomic visual action (AVA) format and train and evaluate the popular SlowFast action recognition model on it. The associated preliminary results confirm that we can localize the cattle and recognize their frequently occurring behaviors with confidence. By creating and sharing CVB, our aim is to develop improved models capable of recognizing all important behaviors accurately and to assist other researchers and practitioners in developing and evaluating new ML models for cattle behavior classification using video data.
Extending Explainable Boosting Machines to Scientific Image Data
Authors: Daniel Schug, Sai Yerramreddy, Rich Caruana, Craig Greenberg, Justyna P. Zwolak
Date: 2023-05-25 00:00:00

As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data tabularized using Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. In doing so, we demonstrate the use of EBMs for image data for the first time and show that our approach provides explanations that are consistent with human intuition about the data.
Image Classification of Stroke Blood Clot Origin using Deep Convolutional Neural Networks and Visual Transformers
Authors: David Azatyan
Date: 2023-05-25 00:00:00
Tasks: Blocking, image-classification, Image Classification

Stroke is one of two main causes of death worldwide. Many individuals suffer from ischemic stroke every year. Only in US more over 700,000 individuals meet ischemic stroke due to blood clot blocking an artery to the brain every year. The paper describes particular approach how to apply Artificial Intelligence for purposes of separating two major acute ischemic stroke (AIS) etiology subtypes: cardiac and large artery atherosclerosis. Four deep neural network architectures and simple ensemble method are used in the approach.
SimHaze: game engine simulated data for real-world dehazing
Authors: Zhengyang Lou, Huan Xu, Fangzhou Mu, Yanli Liu, XiaoYu Zhang, Liang Shang, Jiang Li, Bochen Guan, Yin Li, Yu Hen Hu
Date: 2023-05-25 00:00:00
Tasks: Depth Estimation, Image Dehazing, Single Image Dehazing

Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due to inaccurate depth estimation, resulting in poor generalization of the trained models. In this paper, we explore an alternative approach for generating paired clean-hazy images by leveraging computer graphics. Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models. To this end, we present SimHaze: a new synthetic haze dataset. More importantly, we show that training with SimHaze alone allows the latest dehazing models to achieve significantly better performance in comparison to previous dehazing datasets. Our dataset and code will be made publicly available.
Pair-Variational Autoencoders (PairVAE) for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques
Authors: Shizhao Lu, Arthi Jayaraman
Date: 2023-05-25 00:00:00

In material research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of the synthesized material. Depending on the availability of and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other techniques(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, PairVAE, that works with data from Small Angle X-Ray Scattering (SAXS) that presents information about bulk morphology and images from Scanning Electron Microscopy (SEM) that presents two-dimensional local structural information of the sample. Using paired SAXS and SEM data of novel block copolymer assembled morphologies [open access data from Doerk G.S., et al. Science Advances. 2023 Jan 13;9(2): eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern, and vice versa. This method can be extended to other soft materials morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as creating a database for other downstream calculations of structure-property relationships.
An AI-Ready Multiplex Staining Dataset for Reproducible and Accurate Characterization of Tumor Immune Microenvironment
Authors: Parmida Ghahremani, Joseph Marino, Juan Hernandez-Prera, Janis V. de la Iglesia, Robbert JC Slebos, Christine H. Chung, Saad Nadeem
Date: 2023-05-25 00:00:00
Tasks: Style Transfer

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly-skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The dataset is available at \url{https://github.com/nadeemlab/DeepLIIF}.
Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts
Authors: Adnan Munir, Abdul Jabbar Siddiqui, Saeed Anwar
Date: 2023-05-25 00:00:00
Tasks: Benchmarking, object-detection, Object Detection

To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and deep learning approaches are evolving research areas. Interest in this problem has grown due to concerns regarding the possible hazards and misuse of employing UAVs in many applications. These include potential privacy violations. To address the concerns, vision-based object detection methods have been developed for UAV detection. However, UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied. Hence, for this purpose, we prepared two training datasets. The first dataset has the sky as its background and is called the Sky Background Dataset (SBD). The second training dataset has more complex scenes (with diverse backgrounds) and is named the Complex Background Dataset (CBD). Additionally, two test sets were prepared: one containing clear images and the other with images with three rain artifacts, named the Rainy Test Set (RTS). This work also focuses on benchmarking state-of-the-art object detection models, and to the best of our knowledge, it is the first to investigate the performance of recent and popular vision-based object detection methods for UAV detection under challenging conditions such as complex backgrounds, varying UAV sizes, and low-to-heavy rainy conditions. The findings presented in the paper shall help provide insights concerning the performance of the selected models for UAV detection under challenging conditions and pave the way to develop more robust UAV detection methods. The codes and datasets are available at: https://github.com/AdnanMunir294/UAVD-CBRA.
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?
Authors: Geraldine Jeckeln, Selin Yavuzcan, Kate A. Marquis, Prajay Sandipkumar Mehta, Amy N. Yates, P. Jonathon Phillips, Alice J. O'Toole
Date: 2023-05-25 00:00:00
Tasks: Face Recognition, Face Verification

Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to improve, it is important to continually assess their race bias relative to humans. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and two state-of-the-art face recognition systems. Pairs of same- and different-identity faces of White and Black individuals were selected to be difficult for humans and an open-source implementation of the ArcFace face recognition algorithm from 2019 (5). Human participants (54 Black; 51 White) judged whether face pairs showed the same identity or different identities on a 7-point Likert-type scale. Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7). By design, the test proved challenging for humans as a group, who performed above chance, but far less than perfect. Both state-of-the-art face recognition systems scored perfectly (no errors), consequently with equal accuracy for both races. We conclude that state-of-the-art systems for identity verification between two frontal face images of Black and White individuals can surpass the general population. Whether this result generalizes to challenging in-the-wild images is a pressing concern for deploying face recognition systems in unconstrained environments.
ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image
Authors: Zhenzhen Weng, Zeyu Wang, Serena Yeung
Date: 2023-05-25 00:00:00
Tasks: 3D Shape Generation, Image Generation, Image to 3D, NeRF, Text to Image Generation, Text-to-Image Generation

Recent advancements in text-to-image generation have enabled significant progress in zero-shot 3D shape generation. This is achieved by score distillation, a methodology that uses pre-trained text-to-image diffusion models to optimize the parameters of a 3D neural presentation, e.g. Neural Radiance Field (NeRF). While showing promising results, existing methods are often not able to preserve the geometry of complex shapes, such as human bodies. To address this challenge, we present ZeroAvatar, a method that introduces the explicit 3D human body prior to the optimization process. Specifically, we first estimate and refine the parameters of a parametric human body from a single image. Then during optimization, we use the posed parametric body as additional geometry constraint to regularize the diffusion model as well as the underlying density field. Lastly, we propose a UV-guided texture regularization term to further guide the completion of texture on invisible body parts. We show that ZeroAvatar significantly enhances the robustness and 3D consistency of optimization-based image-to-3D avatar generation, outperforming existing zero-shot image-to-3D methods.
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Authors: Ying Fan, Olivia Watkins, Yuqing Du, Hao liu, MoonKyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, Kimin Lee
Date: 2023-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and then improve the models based on the learned reward function. Even though relatively simple approaches (e.g., rejection sampling based on reward scores) have been investigated, fine-tuning text-to-image models with the reward function remains challenging. In this work, we propose using online reinforcement learning (RL) to fine-tune text-to-image models. We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward. Our approach, coined DPOK, integrates policy optimization with KL regularization. We conduct an analysis of KL regularization for both RL fine-tuning and supervised fine-tuning. In our experiments, we show that DPOK is generally superior to supervised fine-tuning with respect to both image-text alignment and image quality. Our code is available at https://github.com/google-research/google-research/tree/master/dpok.