pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Class Incremental Learning via Robust Transformer Approach
Authors: Naeem Paeedeh, Mahardhika Pratama, Sunu Wibirama, Wolfgang Mayer, Zehong Cao, Ryszard Kowalczyk
Date: 2024-05-08 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Data Augmentation, Few-Shot Class-Incremental Learning, Incremental Learning

Few-Shot Class-Incremental Learning presents an extension of the Class Incremental Learning problem where a model is faced with the problem of data scarcity while addressing the catastrophic forgetting problem. This problem remains an open problem because all recent works are built upon the convolutional neural networks performing sub-optimally compared to the transformer approaches. Our paper presents Robust Transformer Approach built upon the Compact Convolution Transformer. The issue of overfitting due to few samples is overcome with the notion of the stochastic classifier, where the classifier's weights are sampled from a distribution with mean and variance vectors, thus increasing the likelihood of correct classifications, and the batch-norm layer to stabilize the training process. The issue of CF is dealt with the idea of delta parameters, small task-specific trainable parameters while keeping the backbone networks frozen. A non-parametric approach is developed to infer the delta parameters for the model's predictions. The prototype rectification approach is applied to avoid biased prototype calculations due to the issue of data scarcity. The advantage of ROBUSTA is demonstrated through a series of experiments in the benchmark problems where it is capable of outperforming prior arts with big margins without any data augmentation protocols.
Real-Time Pill Identification for the Visually Impaired Using Deep Learning
Authors: Bo Dang, Wenchao Zhao, Yufeng Li, Danqing Ma, Qixuan Yu, Elly Yijun Zhu
Date: 2024-05-08 00:00:00
Tasks: Deep Learning, Management, text-to-speech, Text to Speech

The prevalence of mobile technology offers unique opportunities for addressing healthcare challenges, especially for individuals with visual impairments. This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. Utilizing the YOLO framework, the application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices. The system incorporates Text-to- Speech (TTS) to provide immediate auditory feedback, enhancing usability and independence for visually impaired users. Our study evaluates the application's effectiveness in terms of detection accuracy and user experience, highlighting its potential to improve medication management and safety among the visually impaired community. Keywords-Deep Learning; YOLO Framework; Mobile Application; Visual Impairment; Pill Identification; Healthcare
Multi-Object Tracking in the Dark
Authors: Xinzhe Wang, Kang Ma, Qiankun Liu, Yunhao Zou, Ying Fu
Date: 2024-05-10 00:00:00
Proceeding: CVPR 2024 1
Tasks: Autonomous Driving, Multi-Object Tracking, Object, Object Tracking

Low-light scenes are prevalent in real-world applications (e.g. autonomous driving and surveillance at night). Recently, multi-object tracking in various practical use cases have received much attention, but multi-object tracking in dark scenes is rarely considered. In this paper, we focus on multi-object tracking in dark scenes. To address the lack of datasets, we first build a Low-light Multi-Object Tracking (LMOT) dataset. LMOT provides well-aligned low-light video pairs captured by our dual-camera system, and high-quality multi-object tracking annotations for all videos. Then, we propose a low-light multi-object tracking method, termed as LTrack. We introduce the adaptive low-pass downsample module to enhance low-frequency components of images outside the sensor noises. The degradation suppression learning strategy enables the model to learn invariant information under noise disturbance and image quality degradation. These components improve the robustness of multi-object tracking in dark scenes. We conducted a comprehensive analysis of our LMOT dataset and proposed LTrack. Experimental results demonstrate the superiority of the proposed method and its competitiveness in real night low-light scenes. Dataset and Code: https: //github.com/ying-fu/LMOT
A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning
Authors: Dongwei Sun, Yajie Bao, Junmin Liu, Xiangyong Cao
Date: 2024-05-10 00:00:00
Tasks: Decoder

Remote sensing image change captioning (RSICC) aims to automatically generate sentences that describe content differences in remote sensing bitemporal images. Recently, attention-based transformers have become a prevalent idea for capturing the features of global change. However, existing transformer-based RSICC methods face challenges, e.g., high parameters and high computational complexity caused by the self-attention operation in the transformer encoder component. To alleviate these issues, this paper proposes a Sparse Focus Transformer (SFT) for the RSICC task. Specifically, the SFT network consists of three main components, i.e. a high-level features extractor based on a convolutional neural network (CNN), a sparse focus attention mechanism-based transformer encoder network designed to locate and capture changing regions in dual-temporal images, and a description decoder that embeds images and words to generate sentences for captioning differences. The proposed SFT network can reduce the parameter number and computational complexity by incorporating a sparse attention mechanism within the transformer encoder network. Experimental results on various datasets demonstrate that even with a reduction of over 90\% in parameters and computational complexity for the transformer encoder, our proposed network can still obtain competitive performance compared to other state-of-the-art RSICC methods. The code is available at \href{https://github.com/sundongwei/SFT_chag2cap}{Lite\_Chag2cap}.
Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters
Authors: Thomas Manzini, Priyankari Perali, Raisa Karnik, Mihir Godbole, Hasnat Abdullah, Robin Murphy
Date: 2024-05-10 00:00:00

This work presents the first quantitative study of alignment errors between small uncrewed aerial systems (sUAS) geospatial imagery and a priori building polygons and finds that alignment errors are non-uniform and irregular. The work also introduces a publicly available dataset of imagery, building polygons, and human-generated and curated adjustments that can be used to evaluate existing strategies for aligning building polygons with sUAS imagery. There are no efforts that have aligned pre-existing spatial data with sUAS imagery, and thus, there is no clear state of practice. However, this effort and analysis show that the translational alignment errors present in this type of data, averaging 82px and an intersection over the union of 0.65, which would induce further errors and biases in downstream machine learning systems unless addressed. This study identifies and analyzes the translational alignment errors of 21,619 building polygons in fifty-one orthomosaic images, covering 16787.2 Acres (26.23 square miles), constructed from sUAS raw imagery from nine wide-area disasters (Hurricane Ian, Hurricane Harvey, Hurricane Michael, Hurricane Ida, Hurricane Idalia, Hurricane Laura, the Mayfield Tornado, the Musset Bayou Fire, and the Kilauea Eruption). The analysis finds no uniformity among the angle and distance metrics of the building polygon alignments as they present an average degree variance of 0.4 and an average pixel distance variance of 0.45. This work alerts the sUAS community to the problem of spatial alignment and that a simple linear transform, often used to align satellite imagery, will not be sufficient to align spatial data in sUAS orthomosaic imagery.
Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach
Authors: Elham Ravanbakhsh, Cheng Niu, Yongqing Liang, J. Ramanujam, Xin Li
Date: 2024-05-10 00:00:00
Tasks: Pseudo Label, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Semantic segmentation is a core computer vision problem, but the high costs of data annotation have hindered its wide application. Weakly-Supervised Semantic Segmentation (WSSS) offers a cost-efficient workaround to extensive labeling in comparison to fully-supervised methods by using partial or incomplete labels. Existing WSSS methods have difficulties in learning the boundaries of objects leading to poor segmentation results. We propose a novel and effective framework that addresses these issues by leveraging visual foundation models inside the bounding box. Adopting a two-stage WSSS framework, our proposed network consists of a pseudo-label generation module and a segmentation module. The first stage leverages Segment Anything Model (SAM) to generate high-quality pseudo-labels. To alleviate the problem of delineating precise boundaries, we adopt SAM inside the bounding box with the help of another pre-trained foundation model (e.g., Grounding-DINO). Furthermore, we eliminate the necessity of using the supervision of image labels, by employing CLIP in classification. Then in the second stage, the generated high-quality pseudo-labels are used to train an off-the-shelf segmenter that achieves the state-of-the-art performance on PASCAL VOC 2012 and MS COCO 2014.
Deep video representation learning: a survey
Authors: Elham Ravanbakhsh, Yongqing Liang, J. Ramanujam, Xin Li
Date: 2024-05-10 00:00:00
Tasks: Representation Learning, Survey

This paper provides a review on representation learning for videos. We classify recent spatiotemporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies.
OneTo3D: One Image to Re-editable Dynamic 3D Model and Video Generation
Authors: Jinwei Lin
Date: 2024-05-10 00:00:00
Tasks: 3D Reconstruction, Image to 3D, Video Generation

One image to editable dynamic 3D model and video generation is novel direction and change in the research area of single image to 3D representation or 3D reconstruction of image. Gaussian Splatting has demonstrated its advantages in implicit 3D reconstruction, compared with the original Neural Radiance Fields. As the rapid development of technologies and principles, people tried to used the Stable Diffusion models to generate targeted models with text instructions. However, using the normal implicit machine learning methods is hard to gain the precise motions and actions control, further more, it is difficult to generate a long content and semantic continuous 3D video. To address this issue, we propose the OneTo3D, a method and theory to used one single image to generate the editable 3D model and generate the targeted semantic continuous time-unlimited 3D video. We used a normal basic Gaussian Splatting model to generate the 3D model from a single image, which requires less volume of video memory and computer calculation ability. Subsequently, we designed an automatic generation and self-adaptive binding mechanism for the object armature. Combined with the re-editable motions and actions analyzing and controlling algorithm we proposed, we can achieve a better performance than the SOTA projects in the area of building the 3D model precise motions and actions control, and generating a stable semantic continuous time-unlimited 3D video with the input text instructions. Here we will analyze the detailed implementation methods and theories analyses. Relative comparisons and conclusions will be presented. The project code is open source.
Mesh Denoising Transformer
Authors: Wenbo Zhao, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji
Date: 2024-05-10 00:00:00
Tasks: Denoising

Mesh denoising, aimed at removing noise from input meshes while preserving their feature structures, is a practical yet challenging task. Despite the remarkable progress in learning-based mesh denoising methodologies in recent years, their network designs often encounter two principal drawbacks: a dependence on single-modal geometric representations, which fall short in capturing the multifaceted attributes of meshes, and a lack of effective global feature aggregation, hindering their ability to fully understand the mesh's comprehensive structure. To tackle these issues, we propose SurfaceFormer, a pioneering Transformer-based mesh denoising framework. Our first contribution is the development of a new representation known as Local Surface Descriptor, which is crafted by establishing polar systems on each mesh face, followed by sampling points from adjacent surfaces using geodesics. The normals of these points are organized into 2D patches, mimicking images to capture local geometric intricacies, whereas the poles and vertex coordinates are consolidated into a point cloud to embody spatial information. This advancement surmounts the hurdles posed by the irregular and non-Euclidean characteristics of mesh data, facilitating a smooth integration with Transformer architecture. Next, we propose a dual-stream structure consisting of a Geometric Encoder branch and a Spatial Encoder branch, which jointly encode local geometry details and spatial information to fully explore multimodal information for mesh denoising. A subsequent Denoising Transformer module receives the multimodal information and achieves efficient global feature aggregation through self-attention operators. Our experimental evaluations demonstrate that this novel approach outperforms existing state-of-the-art methods in both objective and subjective assessments, marking a significant leap forward in mesh denoising.
Controllable Image Generation With Composed Parallel Token Prediction
Authors: Jamie Stirling, Noura Al-Moubayed
Date: 2024-05-10 00:00:00
Tasks: Image Generation, Prediction, Text to Image Generation, Text-to-Image Generation

Compositional image generation requires models to generalise well in situations where two or more input concepts do not necessarily appear together in training (compositional generalisation). Despite recent progress in compositional image generation via composing continuous sampling processes such as diffusion and energy-based models, composing discrete generative processes has remained an open challenge, with the promise of providing improvements in efficiency, interpretability and simplicity. To this end, we propose a formulation for controllable conditional generation of images via composing the log-probability outputs of discrete generative models of the latent space. Our approach, when applied alongside VQ-VAE and VQ-GAN, achieves state-of-the-art generation accuracy in three distinct settings (FFHQ, Positional CLEVR and Relational CLEVR) while attaining competitive Fr\'echet Inception Distance (FID) scores. Our method attains an average generation accuracy of $80.71\%$ across the studied settings. Our method also outperforms the next-best approach (ranked by accuracy) in terms of FID in seven out of nine experiments, with an average FID of $24.23$ (an average improvement of $-9.58$). Furthermore, our method offers a $2.3\times$ to $12\times$ speedup over comparable continuous compositional methods on our hardware. We find that our method can generalise to combinations of input conditions that lie outside the training data (e.g. more objects per image) in addition to offering an interpretable dimension of controllability via concept weighting. We further demonstrate that our approach can be readily applied to an open pre-trained discrete text-to-image model without any fine-tuning, allowing for fine-grained control of text-to-image generation.
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Authors: Xiaowen Ma, ZhenLiang Ni, Xinghao Chen
Date: 2024-05-10 00:00:00
Tasks: Semantic Segmentation

Vanilla pixel-level classifiers for semantic segmentation are based on a certain paradigm, involving the inner product of fixed prototypes obtained from the training set and pixel features in the test image. This approach, however, encounters significant limitations, \ie, feature deviation in the semantic domain and information loss in the spatial domain. The former struggles with large intra-class variance among pixel features from different images, while the latter fails to utilize the structured information of semantic objects effectively. This leads to blurred mask boundaries as well as a deficiency of fine-grained recognition capability. In this paper, we propose a novel Semantic and Spatial Adaptive Classifier (SSA-Seg) to address the above challenges. Specifically, we employ the coarse masks obtained from the fixed prototypes as a guide to adjust the fixed prototype towards the center of the semantic and spatial domains in the test image. The adapted prototypes in semantic and spatial domains are then simultaneously considered to accomplish classification decisions. In addition, we propose an online multi-domain distillation learning strategy to improve the adaption process. Experimental results on three publicly available benchmarks show that the proposed SSA-Seg significantly improves the segmentation performance of the baseline models with only a minimal increase in computational cost. Code is available at https://github.com/xwmaxwma/SSA-Seg.
Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data
Authors: Yonghao Xu, Pedram Ghamisi, Yannis Avrithis
Date: 2024-05-10 00:00:00
Tasks: Domain Adaptation, Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Multi-target unsupervised domain adaptation (UDA) aims to learn a unified model to address the domain shift between multiple target domains. Due to the difficulty of obtaining annotations for dense predictions, it has recently been introduced into cross-domain semantic segmentation. However, most existing solutions require labeled data from the source domain and unlabeled data from multiple target domains concurrently during training. Collectively, we refer to this data as "external". When faced with new unlabeled data from an unseen target domain, these solutions either do not generalize well or require retraining from scratch on all data. To address these challenges, we introduce a new strategy called "multi-target UDA without external data" for semantic segmentation. Specifically, the segmentation model is initially trained on the external data. Then, it is adapted to a new unseen target domain without accessing any external data. This approach is thus more scalable than existing solutions and remains applicable when external data is inaccessible. We demonstrate this strategy using a simple method that incorporates self-distillation and adversarial learning, where knowledge acquired from the external data is preserved during adaptation through "one-way" adversarial learning. Extensive experiments in several synthetic-to-real and real-to-real adaptation settings on four benchmark urban driving datasets show that our method significantly outperforms current state-of-the-art solutions, even in the absence of external data. Our source code is available online (https://github.com/YonghaoXu/UT-KD).
Autonomous Driving with a Deep Dual-Model Solution for Steering and Braking Control
Authors: Ana Petra Jukić, Ana Šelek, Marija Seder, Ivana Podnar Žarko
Date: 2024-05-10 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Steering Control

The technology of autonomous driving is currently attracting a great deal of interest in both research and industry. In this paper, we present a deep learning dual-model solution that uses two deep neural networks for combined braking and steering in autonomous vehicles. Steering control is achieved by applying the NVIDIA's PilotNet model to predict the steering wheel angle, while braking control relies on the use of MobileNet SSD. Both models rely on a single front-facing camera for image input. The MobileNet SSD model is suitable for devices with constrained resources, whereas PilotNet struggles to operate efficiently on smaller devices with limited resources. To make it suitable for such devices, we modified the PilotNet model using our own original network design and reduced the number of model parameters and its memory footprint by approximately 60%. The inference latency has also been reduced, making the model more suitable to operate on resource-constrained devices. The modified PilotNet model achieves similar loss and accuracy compared to the original PilotNet model. When evaluated in a simulated environment, both autonomous driving systems, one using the modified PilotNet model and the other using the original PilotNet model for steering, show similar levels of autonomous driving performance.
Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification
Authors: Yaoqin Ye, Junjie Zhang, Hongwei Shi
Date: 2024-05-10 00:00:00
Tasks: Decoder, image-classification, Image Classification, Medical Image Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-label zero-shot learning, Prompt Learning, Text Generation, zero-shot-classification, Zero-Shot Learning

The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels. This complexity underlines the need for computer-aided diagnosis methods employing multi-label zero-shot learning. Recent advancements in pre-trained vision-language models (VLMs) have showcased notable zero-shot classification abilities on medical images. However, these methods have limitations on leveraging extensive pre-trained knowledge from broader image datasets, and often depend on manual prompt construction by expert radiologists. By automating the process of prompt tuning, prompt learning techniques have emerged as an efficient way to adapt VLMs to downstream tasks. Yet, existing CoOp-based strategies fall short in performing class-specific prompts on unseen categories, limiting generalizability in fine-grained scenarios. To overcome these constraints, we introduce a novel prompt generation approach inspirited by text generation in natural language processing (NLP). Our method, named Pseudo-Prompt Generating (PsPG), capitalizes on the priori knowledge of multi-modal features. Featuring a RNN-based decoder, PsPG autoregressively generates class-tailored embedding vectors, i.e., pseudo-prompts. Comparative evaluations on various multi-label chest radiograph datasets affirm the superiority of our approach against leading medical vision-language and multi-label prompt learning methods. The source code is available at https://github.com/fallingnight/PsPG