pwc-archive/papers-with-abstracts huggingface dataset:
RECIST Weakly Supervised Lesion Segmentation via Label-Space Co-Training
Authors: Lianyu Zhou, Dong Wei, Donghuan Lu, Wei Xue, Liansheng Wang, Yefeng Zheng
Date: 2023-03-01 00:00:00
Tasks: Lesion Segmentation, Weakly supervised segmentation

As an essential indicator for cancer progression and treatment response, tumor size is often measured following the response evaluation criteria in solid tumors (RECIST) guideline in CT slices. By marking each lesion with its longest axis and the longest perpendicular one, laborious pixel-wise manual annotation can be avoided. However, such a coarse substitute cannot provide a rich and accurate base to allow versatile quantitative analysis of lesions. To this end, we propose a novel weakly supervised framework to exploit the existing rich RECIST annotations for pixel-wise lesion segmentation. Specifically, a pair of under- and over-segmenting masks are constructed for each lesion based on its RECIST annotation and served as the label for co-training a pair of subnets, respectively, along with the proposed label-space perturbation induced consistency loss to bridge the gap between the two subnets and enable effective co-training. Extensive experiments are conducted on a public dataset to demonstrate the superiority of the proposed framework regarding the RECIST-based weakly supervised segmentation task and its universal applicability to various backbone networks.
DMSA: Dynamic Multi-scale Unsupervised Semantic Segmentation Based on Adaptive Affinity
Authors: Kun Yang, Jun Lu
Date: 2023-03-01 00:00:00
Tasks: Semantic Segmentation, Unsupervised Semantic Segmentation

The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-scale context information. Secondly, a Pixel-Adaptive Refinement (PAR) module is introduced, which can adaptively refine the initial pseudo labels after feature fusion to obtain high quality pseudo labels. Experiments show that the proposed DSMA framework is superior to the existing methods on the saliency dataset. On the COCO 80 dataset, the MIoU is improved by 2.0, and the accuracy is improved by 5.39. On the Pascal VOC 2012 Augmented dataset, the MIoU is improved by 4.9, and the accuracy is improved by 3.4. In addition, the convergence speed of the model is also greatly improved after the introduction of the PAR module.
Convolutional Visual Prompt for Robust Visual Perception
Date: 2023-03-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Test-time Adaptation

Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale vision models, they rely on a high-dimensional additive vector and labeled data. This leads to overfitting when adapting models in a self-supervised test-time setting without labels. We introduce convolutional visual prompts (CVP) for label-free test-time adaptation for robust visual perception. The structured nature of CVP demands fewer trainable parameters, less than 1\% compared to standard visual prompts, combating overfitting. Extensive experiments and analysis on a wide variety of OOD visual perception tasks show that our approach is effective, improving robustness by up to 5.87% over several large-scale models.
CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition
Authors: Hanting Li, Hongjing Niu, Zhaoqing Zhu, Feng Zhao
Date: 2023-03-01 00:00:00
Tasks: Dynamic Facial Expression Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

Facial expression recognition (FER) is an essential task for understanding human behaviors. As one of the most informative behaviors of humans, facial expressions are often compound and variable, which is manifested by the fact that different people may express the same expression in very different ways. However, most FER methods still use one-hot or soft labels as the supervision, which lack sufficient semantic descriptions of facial expressions and are less interpretable. Recently, contrastive vision-language pre-training (VLP) models (e.g., CLIP) use text as supervision and have injected new vitality into various computer vision tasks, benefiting from the rich semantics in text. Therefore, in this work, we propose CLIPER, a unified framework for both static and dynamic facial Expression Recognition based on CLIP. Besides, we introduce multiple expression text descriptors (METD) to learn fine-grained expression representations that make CLIPER more interpretable. We conduct extensive experiments on several popular FER benchmarks and achieve state-of-the-art performance, which demonstrates the effectiveness of CLIPER.
Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching
Authors: Zheng Li, Caili Guo, Xin Wang, Zerun Feng, Zhongtian Du
Date: 2023-03-01 00:00:00
Tasks: Image-text matching, Text Matching, Triplet

Recently, a series of Image-Text Matching (ITM) methods achieve impressive performance. However, we observe that most existing ITM models suffer from gradients vanishing at the beginning of training, which makes these models prone to falling into local minima. Most ITM models adopt triplet loss with Hard Negative mining (HN) as the optimization objective. We find that optimizing an ITM model using only the hard negative samples can easily lead to gradient vanishing. In this paper, we derive the condition under which the gradient vanishes during training. When the difference between the positive pair similarity and the negative pair similarity is close to 0, the gradients on both the image and text encoders will approach 0. To alleviate the gradient vanishing problem, we propose a Selectively Hard Negative Mining (SelHN) strategy, which chooses whether to mine hard negative samples according to the gradient vanishing condition. SelHN can be plug-and-play applied to existing ITM models to give them better training behavior. To further ensure the back-propagation of gradients, we construct a Residual Visual Semantic Embedding model with SelHN, denoted as RVSE++. Extensive experiments on two ITM benchmarks demonstrate the strength of RVSE++, achieving state-of-the-art performance.
MMA-MRNNet: Harnessing Multiple Models of Affect and Dynamic Masked RNN for Precise Facial Expression Intensity Estimation
Authors: Dimitrios Kollias, Andreas Psaroudakis, Anastasios Arsenos, Paraskevi Theofilou, Chunchang Shao, Guanyu Hu, Ioannis Patras
Date: 2023-03-01 00:00:00
Tasks: Ensemble Learning, Multi-Task Learning

This paper presents MMA-MRNNet, a novel deep learning architecture for dynamic multi-output Facial Expression Intensity Estimation (FEIE) from video data. Traditional approaches to this task often rely on complex 3-D CNNs, which require extensive pre-training and assume that facial expressions are uniformly distributed across all frames of a video. These methods struggle to handle videos of varying lengths, often resorting to ad-hoc strategies that either discard valuable information or introduce bias. MMA-MRNNet addresses these challenges through a two-stage process. First, the Multiple Models of Affect (MMA) extractor component is a Multi-Task Learning CNN that concurrently estimates valence-arousal, recognizes basic facial expressions, and detects action units in each frame. These representations are then processed by a Masked RNN component, which captures temporal dependencies and dynamically updates weights according to the true length of the input video, ensuring that only the most relevant features are used for the final prediction. The proposed unimodal non-ensemble learning MMA-MRNNet was evaluated on the Hume-Reaction dataset and demonstrated significantly superior performance, surpassing state-of-the-art methods by a wide margin, regardless of whether they were unimodal, multimodal, or ensemble approaches. Finally, we demonstrated the effectiveness of the MMA component of our proposed method across multiple in-the-wild datasets, where it consistently outperformed all state-of-the-art methods across various metrics.
A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging
Authors: Dimitrios Kollias, Anastasios Arsenos, Stefanos Kollias
Date: 2023-03-01 00:00:00
Tasks: COVID-19 Diagnosis, Decision Making

Harmonizing the analysis of data, especially of 3-D image volumes, consisting of different number of slices and annotated per volume, is a significant problem in training and using deep neural networks in various applications, including medical imaging. Moreover, unifying the decision making of the networks over different input datasets is crucial for the generation of rich data-driven knowledge and for trusted usage in the applications. This paper presents a new deep neural architecture, named RACNet, which includes routing and feature alignment steps and effectively handles different input lengths and single annotations of the 3-D image inputs, whilst providing highly accurate decisions. In addition, through latent variable extraction from the trained RACNet, a set of anchors are generated providing further insight on the network's decision making. These can be used to enrich and unify data-driven knowledge extracted from different datasets. An extensive experimental study illustrates the above developments, focusing on COVID-19 diagnosis through analysis of 3-D chest CT scans from databases generated in different countries and medical centers.
Sketch2Cloth: Sketch-based 3D Garment Generation with Unsigned Distance Fields
Authors: Yi He, Haoran Xie, Kazunori Miyata
Date: 2023-03-01 00:00:00
Tasks: Model Editing

3D model reconstruction from a single image has achieved great progress with the recent deep generative models. However, the conventional reconstruction approaches with template mesh deformation and implicit fields have difficulty in reconstructing non-watertight 3D mesh models, such as garments. In contrast to image-based modeling, the sketch-based approach can help users generate 3D models to meet the design intentions from hand-drawn sketches. In this study, we propose Sketch2Cloth, a sketch-based 3D garment generation system using the unsigned distance fields from the user's sketch input. Sketch2Cloth first estimates the unsigned distance function of the target 3D model from the sketch input, and extracts the mesh from the estimated field with Marching Cubes. We also provide the model editing function to modify the generated mesh. We verified the proposed Sketch2Cloth with quantitative evaluations on garment generation and editing with a state-of-the-art approach.
Semi-supervised Parametric Real-world Image Harmonization
Authors: Ke Wang, Michaël Gharbi, He Zhang, Zhihao Xia, Eli Shechtman
Date: 2023-03-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Harmonization

Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo. This simulated data does not model many of the important appearance mismatches (illumination, object boundaries, etc.) between foreground and background in real composites, leading to models that do not generalize well and cannot model complex local changes. We propose a new semi-supervised training strategy that addresses this problem and lets us learn complex local appearance harmonization from unpaired real composites, where foreground and background come from different images. Our model is fully parametric. It uses RGB curves to correct the global colors and tone and a shading map to model local variations. Our method outperforms previous work on established benchmarks and real composites, as shown in a user study, and processes high-resolution images interactively.
Neural inverse procedural modeling of knitting yarns from images
Authors: Elena Trunz, Jonathan Klein, Jan Müller, Lukas Bode, Ralf Sarlette, Michael Weinmann, Reinhard Klein
Date: 2023-03-01 00:00:00

We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity of yarn structures in terms of twisting and migration characteristics of the involved fibers can be better encountered in terms of ensembles of networks that focus on individual characteristics. We analyze the effect of different loss functions including a parameter loss to penalize the deviation of inferred parameters to ground truth annotations, a reconstruction loss to enforce similar statistics of the image generated for the estimated parameters in comparison to training images as well as an additional regularization term to explicitly penalize deviations between latent codes of synthetic images and the average latent code of real images in the latent space of the encoder. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference when solely trained on synthetic data. Since our approach relies on the availability of a yarn database with parameter annotations and we are not aware of such a respectively available dataset, we additionally provide, to the best of our knowledge, the first dataset of yarn images with annotations regarding the respective yarn parameters. For this purpose, we use a novel yarn generator that improves the realism of the produced results over previous approaches.
Texture-Based Input Feature Selection for Action Recognition
Authors: Yalong Jiang
Date: 2023-02-28 00:00:00
Tasks: Action Recognition, Anomaly Detection, feature selection, Graph Matching, Human Parsing, Motion Estimation, Multi-Person Pose Estimation, Multi-Person Pose Estimation and Tracking, Optical Flow Estimation, Pose Estimation, Temporal Action Localization

The performance of video action recognition has been significantly boosted by using motion representations within a two-stream Convolutional Neural Network (CNN) architecture. However, there are a few challenging problems in action recognition in real scenarios, e.g., the variations in viewpoints and poses, and the changes in backgrounds. The domain discrepancy between the training data and the test data causes the performance drop. To improve the model robustness, we propose a novel method to determine the task-irrelevant content in inputs which increases the domain discrepancy. The method is based on a human parsing model (HP model) which jointly conducts dense correspondence labelling and semantic part segmentation. The predictions from the HP model also function as re-rendering the human regions in each video using the same set of textures to make humans appearances in all classes be the same. A revised dataset is generated for training and testing and makes the action recognition model exhibit invariance to the irrelevant content in the inputs. Moreover, the predictions from the HP model are used to enrich the inputs to the AR model during both training and testing. Experimental results show that our proposed model is superior to existing models for action recognition on the HMDB-51 dataset and the Penn Action dataset.
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
Authors: Yichen Sheng, Jianming Zhang, Julien Philip, Yannick Hold-Geoffroy, Xin Sun, He Zhang, Lu Ling, Bedrich Benes
Date: 2023-02-28 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D geometry

Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections. However, the lack of geometry limits the quality of the generated soft shadows and constrain reflections to pure specular ones. We introduce PixHt-Lab, a system leveraging an explicit mapping from pixel height representation to 3D space. Using this mapping, PixHt-Lab reconstructs both the cutout and background geometry and renders realistic, diverse, lighting effects for image compositing. Given a surface with physically-based materials, we can render reflections with varying glossiness. To generate more realistic soft shadows, we further propose to use 3D-aware buffer channels to guide a neural renderer. Both quantitative and qualitative evaluations demonstrate that PixHt-Lab significantly improves soft shadow generation.
A study on the use of perceptual hashing to detect manipulation of embedded messages in images
Authors: Sven-Jannik Wöhnert, Kai Hendrik Wöhnert, Eldar Almamedov, Carsten Frank, Volker Skwarek
Date: 2023-02-28 00:00:00
Tasks: Image Compression

Typically, metadata of images are stored in a specific data segment of the image file. However, to securely detect changes, data can also be embedded within images. This follows the goal to invisibly and robustly embed as much information as possible to, ideally, even survive compression. This work searches for embedding principles which allow to distinguish between unintended changes by lossy image compression and malicious manipulation of the embedded message based on the change of its perceptual or robust hash. Different embedding and compression algorithms are compared. The study shows that embedding a message via integer wavelet transform and compression with Karhunen-Loeve-transform yields the best results. However, it was not possible to distinguish between manipulation and compression in all cases.
Applying Plain Transformers to Real-World Point Clouds
Authors: Lanxiao Li, Michael Heizmann
Date: 2023-02-28 00:00:00
Tasks: Inductive Bias, object-detection, Object Detection, Semantic Segmentation

To apply transformer-based models to point cloud understanding, many previous works modify the architecture of transformers by using, e.g., local attention and down-sampling. Although they have achieved promising results, earlier works on transformers for point clouds have two issues. First, the power of plain transformers is still under-explored. Second, they focus on simple and small point clouds instead of complex real-world ones. This work revisits the plain transformers in real-world point cloud understanding. We first take a closer look at some fundamental components of plain transformers, e.g., patchifier and positional embedding, for both efficiency and performance. To close the performance gap due to the lack of inductive bias and annotated data, we investigate self-supervised pre-training with masked autoencoder (MAE). Specifically, we propose drop patch, which prevents information leakage and significantly improves the effectiveness of MAE. Our models achieve SOTA results in semantic segmentation on the S3DIS dataset and object detection on the ScanNet dataset with lower computational costs. Our work provides a new baseline for future research on transformers for point clouds.
Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface
Authors: Decai Chen, Haofei Lu, Ingo Feldmann, Oliver Schreer, Peter Eisert
Date: 2023-02-28 00:00:00
Tasks: Novel View Synthesis, Surface Reconstruction

Reconstructing general dynamic scenes is important for many computer vision and graphics applications. Recent works represent the dynamic scene with neural radiance fields for photorealistic view synthesis, while their surface geometry is under-constrained and noisy. Other works introduce surface constraints to the implicit neural representation to disentangle the ambiguity of geometry and appearance field for static scene reconstruction. To bridge the gap between rendering dynamic scenes and recovering static surface geometry, we propose a template-free method to reconstruct surface geometry and appearance using neural implicit representations from multi-view videos. We leverage topology-aware deformation and the signed distance field to learn complex dynamic surfaces via differentiable volume rendering without scene-specific prior knowledge like template models. Furthermore, we propose a novel mask-based ray selection strategy to significantly boost the optimization on challenging time-varying regions. Experiments on different multi-view video datasets demonstrate that our method achieves high-fidelity surface reconstruction as well as photorealistic novel view synthesis.
Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training
Authors: Dezhao Luo, Jiabo Huang, Shaogang Gong, Hailin Jin, Yang Liu
Date: 2023-02-28 00:00:00
Proceeding: CVPR 2023 1
Tasks: Moment Retrieval, Retrieval

The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments. In this work, we explore multi-modal correlations derived from large-scale image-text data to facilitate generalisable VMR. To address the limitations of image-text pre-training models on capturing the video changes, we propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments. Whilst existing VMR methods are focusing on building temporal-aware video features, being aware of the text descriptions about the temporal changes is also critical but originally overlooked in pre-training by matching static images with sentences. Therefore, we extract visual context and spatial dynamic information from video frames and explicitly enforce their alignments with the phrases describing video changes (e.g. verb). By doing so, the potentially relevant visual and motion patterns in videos are encoded in the corresponding text embeddings (injected) so to enable more accurate video-text alignments. We conduct extensive experiments on two VMR benchmark datasets (Charades-STA and ActivityNet-Captions) and achieve state-of-the-art performances. Especially, VDI yields notable advantages when being tested on the out-of-distribution splits where the testing samples involve novel scenes and vocabulary.
Valid Information Guidance Network for Compressed Video Quality Enhancement
Authors: Xuan Sun, Ziyue Zhang, Guannan Chen, Dan Zhu
Date: 2023-02-28 00:00:00
Tasks: valid

In recent years deep learning methods have shown great superiority in compressed video quality enhancement tasks. Existing methods generally take the raw video as the ground truth and extract practical information from consecutive frames containing various artifacts. However, they do not fully exploit the valid information of compressed and raw videos to guide the quality enhancement for compressed videos. In this paper, we propose a unique Valid Information Guidance scheme (VIG) to enhance the quality of compressed videos by mining valid information from both compressed videos and raw videos. Specifically, we propose an efficient framework, Compressed Redundancy Filtering (CRF) network, to balance speed and enhancement. After removing the redundancy by filtering the information, CRF can use the valid information of the compressed video to reconstruct the texture. Furthermore, we propose a progressive Truth Guidance Distillation (TGD) strategy, which does not need to design additional teacher models and distillation loss functions. By only using the ground truth as input to guide the model to aggregate the correct spatio-temporal correspondence across the raw frames, TGD can significantly improve the enhancement effect without increasing the extra training cost. Extensive experiments show that our method achieves the state-of-the-art performance of compressed video quality enhancement in terms of accuracy and efficiency.