pwc-archive/papers-with-abstracts huggingface dataset:
Skeleton-Guided Instance Separation for Fine-Grained Segmentation in Microscopy
Authors: Jun Wang, Chengfeng Zhou, Zhaoyan Ming, Lina Wei, Xudong Jiang, Dahong Qian
Date: 2024-01-18 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

One of the fundamental challenges in microscopy (MS) image analysis is instance segmentation (IS), particularly when segmenting cluster regions where multiple objects of varying sizes and shapes may be connected or even overlapped in arbitrary orientations. Existing IS methods usually fail in handling such scenarios, as they rely on coarse instance representations such as keypoints and horizontal bounding boxes (h-bboxes). In this paper, we propose a novel one-stage framework named A2B-IS to address this challenge and enhance the accuracy of IS in MS images. Our approach represents each instance with a pixel-level mask map and a rotated bounding box (r-bbox). Unlike two-stage methods that use box proposals for segmentations, our method decouples mask and box predictions, enabling simultaneous processing to streamline the model pipeline. Additionally, we introduce a Gaussian skeleton map to aid the IS task in two key ways: (1) It guides anchor placement, reducing computational costs while improving the model's capacity to learn RoI-aware features by filtering out noise from background regions. (2) It ensures accurate isolation of densely packed instances by rectifying erroneous box predictions near instance boundaries. To further enhance the performance, we integrate two modules into the framework: (1) An Atrous Attention Block (A2B) designed to extract high-resolution feature maps with fine-grained multiscale information, and (2) A Semi-Supervised Learning (SSL) strategy that leverages both labeled and unlabeled images for model training. Our method has been thoroughly validated on two large-scale MS datasets, demonstrating its superiority over most state-of-the-art approaches.
Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Authors: Songhe Deng, Wei Zhuo, Jinheng Xie, Linlin Shen
Date: 2024-01-18 00:00:00
Tasks: Contrastive Learning, Prompt Engineering, Question Answering, Semantic Segmentation, Visual Question Answering, Visual Question Answering (VQA), Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels. However, existing CAM methods suffer from under-activation of target object regions and false-activation of background regions due to the fact that a lack of detailed supervision can hinder the model's ability to understand the image as a whole. In this paper, we propose a novel Question-Answer Cross-Language-Image Matching framework for WSSS (QA-CLIMS), leveraging the vision-language foundation model to maximize the text-based understanding of images and guide the generation of activation maps. First, a series of carefully designed questions are posed to the VQA (Visual Question Answering) model with Question-Answer Prompt Engineering (QAPE) to generate a corpus of both foreground target objects and backgrounds that are adaptive to query images. We then employ contrastive learning in a Region Image Text Contrastive (RITC) network to compare the obtained foreground and background regions with the generated corpus. Our approach exploits the rich textual information from the open vocabulary as additional supervision, enabling the model to generate high-quality CAMs with a more complete object region and reduce false-activation of background regions. We conduct extensive analysis to validate the proposed method and show that our approach performs state-of-the-art on both PASCAL VOC 2012 and MS COCO datasets. Code is available at: https://github.com/CVI-SZU/QA-CLIMS
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention
Authors: Li Guo, Haoming Liu, Yuxuan Xia, Chengyu Zhang, Xiaochen Lu
Date: 2024-01-18 00:00:00
Tasks: Data Augmentation, Segmentation, Transfer Learning

Few-shot segmentation aims to train a segmentation model that can fast adapt to a novel task for which only a few annotated images are provided. Most recent models have adopted a prototype-based paradigm for few-shot inference. These approaches may have limited generalization capacity beyond the standard 1- or 5-shot settings. In this paper, we closely examine and reevaluate the fine-tuning based learning scheme that fine-tunes the classification layer of a deep segmentation network pre-trained on diverse base classes. To improve the generalizability of the classification layer optimized with sparsely annotated samples, we introduce an instance-aware data augmentation (IDA) strategy that augments the support images based on the relative sizes of the target objects. The proposed IDA effectively increases the support set's diversity and promotes the distribution consistency between support and query images. On the other hand, the large visual difference between query and support images may hinder knowledge transfer and cripple the segmentation performance. To cope with this challenge, we introduce the local consensus guided cross attention (LCCA) to align the query feature with support features based on their dense correlation, further improving the model's generalizability to the query image. The significant performance improvements on the standard few-shot segmentation benchmarks PASCAL-$5^i$ and COCO-$20^i$ verify the efficacy of our proposed method.
Exploring Latent Cross-Channel Embedding for Accurate 3D Human Pose Reconstruction in a Diffusion Framework
Authors: Junkun Jiang, Jie Chen
Date: 2024-01-18 00:00:00
Tasks: 3D Human Pose Estimation, Graph Attention, Monocular 3D Human Pose Estimation, Pose Estimation

Monocular 3D human pose estimation poses significant challenges due to the inherent depth ambiguities that arise during the reprojection process from 2D to 3D. Conventional approaches that rely on estimating an over-fit projection matrix struggle to effectively address these challenges and often result in noisy outputs. Recent advancements in diffusion models have shown promise in incorporating structural priors to address reprojection ambiguities. However, there is still ample room for improvement as these methods often overlook the exploration of correlation between the 2D and 3D joint-level features. In this study, we propose a novel cross-channel embedding framework that aims to fully explore the correlation between joint-level features of 3D coordinates and their 2D projections. In addition, we introduce a context guidance mechanism to facilitate the propagation of joint graph attention across latent channels during the iterative diffusion process. To evaluate the effectiveness of our proposed method, we conduct experiments on two benchmark datasets, namely Human3.6M and MPI-INF-3DHP. Our results demonstrate a significant improvement in terms of reconstruction accuracy compared to state-of-the-art methods. The code for our method will be made available online for further reference.
Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery
Authors: Zhili Zhang, Xiangyun Hu, Jiabo Xu
Date: 2024-01-18 00:00:00
Tasks: Decoder, Segmentation

In this research, we introduce the enhanced automated quality assessment network (IBS-AQSNet), an innovative solution for assessing the quality of interactive building segmentation within high-resolution remote sensing imagery. This is a new challenge in segmentation quality assessment, and our proposed IBS-AQSNet allievate this by identifying missed and mistaken segment areas. First of all, to acquire robust image features, our method combines a robust, pre-trained backbone with a lightweight counterpart for comprehensive feature extraction from imagery and segmentation results. These features are then fused through a simple combination of concatenation, convolution layers, and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale differential quality assessment decoder, proficient in pinpointing areas where segmentation result is either missed or mistaken. Experiments on a newly-built EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the superiority of the proposed method in automating segmentation quality assessment, thereby setting a new benchmark in the field.
Boosting Few-Shot Semantic Segmentation Via Segment Anything Model
Authors: Chen-Bin Feng, Qi Lai, Kangdao Liu, Houcheng Su, Chi-Man Vong
Date: 2024-01-18 00:00:00
Tasks: Few-Shot Semantic Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

In semantic segmentation, accurate prediction masks are crucial for downstream tasks such as medical image analysis and image editing. Due to the lack of annotated data, few-shot semantic segmentation (FSS) performs poorly in predicting masks with precise contours. Recently, we have noticed that the large foundation model segment anything model (SAM) performs well in processing detailed features. Inspired by SAM, we propose FSS-SAM to boost FSS methods by addressing the issue of inaccurate contour. The FSS-SAM is training-free. It works as a post-processing tool for any FSS methods and can improve the accuracy of predicted masks. Specifically, we use predicted masks from FSS methods to generate prompts and then use SAM to predict new masks. To avoid predicting wrong masks with SAM, we propose a prediction result selection (PRS) algorithm. The algorithm can remarkably decrease wrong predictions. Experiment results on public datasets show that our method is superior to base FSS methods in both quantitative and qualitative aspects.
Enhancing Small Object Encoding in Deep Neural Networks: Introducing Fast&Focused-Net with Volume-wise Dot Product Layer
Authors: Ali Tofik, Roy Partha Pratim
Date: 2024-01-18 00:00:00
Tasks: image-classification, Image Classification

In this paper, we introduce Fast&Focused-Net, a novel deep neural network architecture tailored for efficiently encoding small objects into fixed-length feature vectors. Contrary to conventional Convolutional Neural Networks (CNNs), Fast&Focused-Net employs a series of our newly proposed layer, the Volume-wise Dot Product (VDP) layer, designed to address several inherent limitations of CNNs. Specifically, CNNs often exhibit a smaller effective receptive field than their theoretical counterparts, limiting their vision span. Additionally, the initial layers in CNNs produce low-dimensional feature vectors, presenting a bottleneck for subsequent learning. Lastly, the computational overhead of CNNs, particularly in capturing diverse image regions by parameter sharing, is significantly high. The VDP layer, at the heart of Fast&Focused-Net, aims to remedy these issues by efficiently covering the entire image patch information with reduced computational demand. Experimental results demonstrate the prowess of Fast&Focused-Net in a variety of applications. For small object classification tasks, our network outperformed state-of-the-art methods on datasets such as CIFAR-10, CIFAR-100, STL-10, SVHN-Cropped, and Fashion-MNIST. In the context of larger image classification, when combined with a transformer encoder (ViT), Fast&Focused-Net produced competitive results for OpenImages V6, ImageNet-1K, and Places365 datasets. Moreover, the same combination showcased unparalleled performance in text recognition tasks across SVT, IC15, SVTP, and HOST datasets. This paper presents the architecture, the underlying motivation, and extensive empirical evidence suggesting that Fast&Focused-Net is a promising direction for efficient and focused deep learning.
Efficient Training for Multilingual Visual Speech Recognition: Pre-training with Discretized Visual Speech Representation
Authors: Minsu Kim, Jeong Hun Yeo, Se Jin Park, Hyeongseop Rha, Yong Man Ro
Date: 2024-01-18 00:00:00
Tasks: Sentence, speech-recognition, Speech Recognition, Visual Speech Recognition

This paper explores sentence-level multilingual Visual Speech Recognition (VSR) that can recognize different languages with a single trained model. As the massive multilingual modeling of visual data requires huge computational costs, we propose a novel training strategy, processing with visual speech units. Motivated by the recent success of the audio speech unit, we propose to use a visual speech unit that can be obtained by discretizing the visual speech features extracted from the self-supervised visual speech model. Through analysis, we verify that the visual speech units mainly contain viseme information while suppressing non-linguistic information. By using the visual speech units as the inputs of our system, we propose to pre-train a VSR model to predict corresponding text outputs on multilingual data constructed by merging several VSR databases. As both the inputs (i.e., visual speech units) and outputs (i.e., text) are discrete, we can greatly improve the training efficiency compared to the standard VSR training. Specifically, the input data size is reduced to 0.016% of the original video inputs. In order to complement the insufficient visual information in speech recognition, we apply curriculum learning where the inputs of the system begin with audio-visual speech units and gradually change to visual speech units. After pre-training, the model is finetuned on continuous features. We set new state-of-the-art multilingual VSR performances by achieving comparable performances to the previous language-specific VSR models, with a single trained model.
Wavelet-Guided Acceleration of Text Inversion in Diffusion-Based Image Editing
Authors: Gwanhyeong Koo, Sunjae Yoon, Chang D. Yoo
Date: 2024-01-18 00:00:00
Tasks: Text-based Image Editing

In the field of image editing, Null-text Inversion (NTI) enables fine-grained editing while preserving the structure of the original image by optimizing null embeddings during the DDIM sampling process. However, the NTI process is time-consuming, taking more than two minutes per image. To address this, we introduce an innovative method that maintains the principles of the NTI while accelerating the image editing process. We propose the WaveOpt-Estimator, which determines the text optimization endpoint based on frequency characteristics. Utilizing wavelet transform analysis to identify the image's frequency characteristics, we can limit text optimization to specific timesteps during the DDIM sampling process. By adopting the Negative-Prompt Inversion (NPI) concept, a target prompt representing the original image serves as the initial text value for optimization. This approach maintains performance comparable to NTI while reducing the average editing time by over 80% compared to the NTI method. Our method presents a promising approach for efficient, high-quality image editing based on diffusion models.
BreastRegNet: A Deep Learning Framework for Registration of Breast Faxitron and Histopathology Images
Authors: Negar Golestani, Aihui Wang, Gregory R Bean, Mirabela Rusu
Date: 2024-01-18 00:00:00
Tasks: Breast Cancer Detection, Image Registration

A standard treatment protocol for breast cancer entails administering neoadjuvant therapy followed by surgical removal of the tumor and surrounding tissue. Pathologists typically rely on cabinet X-ray radiographs, known as Faxitron, to examine the excised breast tissue and diagnose the extent of residual disease. However, accurately determining the location, size, and focality of residual cancer can be challenging, and incorrect assessments can lead to clinical consequences. The utilization of automated methods can improve the histopathology process, allowing pathologists to choose regions for sampling more effectively and precisely. Despite the recognized necessity, there are currently no such methods available. Training such automated detection models require accurate ground truth labels on ex-vivo radiology images, which can be acquired through registering Faxitron and histopathology images and mapping the extent of cancer from histopathology to x-ray images. This study introduces a deep learning-based image registration approach trained on mono-modal synthetic image pairs. The models were trained using data from 50 women who received neoadjuvant chemotherapy and underwent surgery. The results demonstrate that our method is faster and yields significantly lower average landmark error ($2.1\pm1.96$ mm) over the state-of-the-art iterative ($4.43\pm4.1$ mm) and deep learning ($4.02\pm3.15$ mm) approaches. Improved performance of our approach in integrating radiology and pathology information facilitates generating large datasets, which allows training models for more accurate breast cancer detection.
On the Audio Hallucinations in Large Audio-Video Language Models
Authors: Taichi Nishimura, Shota Nakada, Masayoshi Kondo
Date: 2024-01-18 00:00:00
Tasks: Hallucination, Sentence

Large audio-video language models can generate descriptions for both video and audio. However, they sometimes ignore audio content, producing audio descriptions solely reliant on visual information. This paper refers to this as audio hallucinations and analyzes them in large audio-video language models. We gather 1,000 sentences by inquiring about audio information and annotate them whether they contain hallucinations. If a sentence is hallucinated, we also categorize the type of hallucination. The results reveal that 332 sentences are hallucinated with distinct trends observed in nouns and verbs for each hallucination type. Based on this, we tackle a task of audio hallucination classification using pre-trained audio-text models in the zero-shot and fine-tuning settings. Our experimental results reveal that the zero-shot models achieve higher performance (52.2% in F1) than the random (40.3%) and the fine-tuning models achieve 87.9%, outperforming the zero-shot models.
SlideAVSR: A Dataset of Paper Explanation Videos for Audio-Visual Speech Recognition
Authors: Hao Wang, Shuhei Kurita, Shuichiro Shimizu, Daisuke Kawahara
Date: 2024-01-18 00:00:00
Tasks: Audio-Visual Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Image Comprehension, speech-recognition, Speech Recognition, Visual Speech Recognition

Audio-visual speech recognition (AVSR) is a multimodal extension of automatic speech recognition (ASR), using video as a complement to audio. In AVSR, considerable efforts have been directed at datasets for facial features such as lip-readings, while they often fall short in evaluating the image comprehension capabilities in broader contexts. In this paper, we construct SlideAVSR, an AVSR dataset using scientific paper explanation videos. SlideAVSR provides a new benchmark where models transcribe speech utterances with texts on the slides on the presentation recordings. As technical terminologies that are frequent in paper explanations are notoriously challenging to transcribe without reference texts, our SlideAVSR dataset spotlights a new aspect of AVSR problems. As a simple yet effective baseline, we propose DocWhisper, an AVSR model that can refer to textual information from slides, and confirm its effectiveness on SlideAVSR.
Image Translation as Diffusion Visual Programmers
Authors: Cheng Han, James C. Liang, Qifan Wang, Majid Rabbani, Sohail Dianat, Raghuveer Rao, Ying Nian Wu, Dongfang Liu
Date: 2024-01-18 00:00:00
Tasks: Style Transfer, Translation

We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion model within the GPT architecture, orchestrating a coherent sequence of visual programs (i.e., computer vision models) for various pro-symbolic steps, which span RoI identification, style transfer, and position manipulation, facilitating transparent and controllable image translation processes. Extensive experiments demonstrate DVP's remarkable performance, surpassing concurrent arts. This success can be attributed to several key features of DVP: First, DVP achieves condition-flexible translation via instance normalization, enabling the model to eliminate sensitivity caused by the manual guidance and optimally focus on textual descriptions for high-quality content generation. Second, the framework enhances in-context reasoning by deciphering intricate high-dimensional concepts in feature spaces into more accessible low-dimensional symbols (e.g., [Prompt], [RoI object]), allowing for localized, context-free editing while maintaining overall coherence. Last but not least, DVP improves systemic controllability and explainability by offering explicit symbolic representations at each programming stage, empowering users to intuitively interpret and modify results. Our research marks a substantial step towards harmonizing artificial image translation processes with cognitive intelligence, promising broader applications.
DirDist: A Metric for Comparing 3D Shapes Using Directional Distance Fields
Authors: Siyu Ren, Junhui Hou, Xiaodong Chen, Hongkai Xiong, Wenping Wang
Date: 2024-01-18 00:00:00
Tasks: 3D geometry, Scene Flow Estimation

Qualifying the discrepancy between 3D geometric models, which could be represented with either point clouds or triangle meshes, is a pivotal issue with board applications. Existing methods mainly focus on directly establishing the correspondence between two models and then aggregating point-wise distance between corresponding points, resulting in them being either inefficient or ineffective. In this paper, we propose DirDist, an efficient, effective, robust, and differentiable distance metric for 3D geometry data. Specifically, we construct DirDist based on the proposed implicit representation of 3D models, namely directional distance field (DDF), which defines the directional distances of 3D points to a model to capture its local surface geometry. We then transfer the discrepancy between two 3D geometric models as the discrepancy between their DDFs defined on an identical domain, naturally establishing model correspondence. To demonstrate the advantage of our DirDist, we explore various distance metric-driven 3D geometric modeling tasks, including template surface fitting, rigid registration, non-rigid registration, scene flow estimation and human pose optimization. Extensive experiments show that our DirDist achieves significantly higher accuracy under all tasks. As a generic distance metric, DirDist has the potential to advance the field of 3D geometric modeling. The source code is available at https://github.com/rsy6318/DirDist.