pwc-archive/papers-with-abstracts huggingface dataset:
GeoNeRF: Generalizing NeRF with Geometry Priors
Authors: Mohammad Mahdi Johari, Yann Lepoittevin, François Fleuret
Date: 2021-11-26 00:00:00
Proceeding: CVPR 2022 1
Tasks: NeRF, Neural Rendering, Novel View Synthesis

We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Transformer-based attention mechanism and the cascaded cost volumes, the renderer infers geometry and appearance, and renders detailed images via classical volume rendering techniques. This architecture, in particular, allows sophisticated occlusion reasoning, gathering information from consistent source views. Moreover, our method can easily be fine-tuned on a single scene, and renders competitive results with per-scene optimized neural rendering methods with a fraction of computational cost. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets. Lastly, with a slight modification to the geometry reasoner, we also propose an alternative model that adapts to RGBD images. This model directly exploits the depth information often available thanks to depth sensors. The implementation code is available at https://www.idiap.ch/paper/geonerf.
A model of semantic completion in generative episodic memory
Authors: Zahra Fayyaz, Aya Altamimi, Sen Cheng, Laurenz Wiskott
Date: 2021-11-26 00:00:00
Tasks: Hippocampus

Many different studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this work, we propose a computational model for generative episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing information based on general semantic information in a process we call semantic completion. As episodes we use images of digits (MNIST) augmented by different backgrounds representing context. Our model is based on a VQ-VAE which generates a compressed latent representation in form of an index matrix, which still has some spatial resolution. We assume that attention selects some part of the index matrix while others are discarded, this then represents the gist of the episode and is stored as a memory trace. At recall the missing parts are filled in by a PixelCNN, modeling semantic completion, and the completed index matrix is then decoded into a full image by the VQ-VAE. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch. Due to the combinatorics in the index matrix, the model generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong.
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation
Authors: Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen
Date: 2021-11-26 00:00:00
Proceeding: CVPR 2022 1
Tasks: All, Graph Generation, Informativeness, Scene Graph Generation, Scene Understanding

Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained on the entire set of relations fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. Learning on trivial relations that indicate generic spatial configuration like 'on' instead of informative relations such as 'parked on' does not enforce this complex reasoning, harming generalization. To address this problem, we propose a novel framework for SGG training that exploits relation labels based on their informativeness. Our model-agnostic training procedure imputes missing informative relations for less informative samples in the training data and trains a SGG model on the imputed labels along with existing annotations. We show that this approach can successfully be used in conjunction with state-of-the-art SGG methods and improves their performance significantly in multiple metrics on the standard Visual Genome benchmark. Furthermore, we obtain considerable improvements for unseen triplets in a more challenging zero-shot setting.
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Authors: Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, Zongwei Zhou
Date: 2021-11-26 00:00:00
Proceeding: CVPR 2023 1
Tasks: Anatomy, Anomaly Detection, Unsupervised Anomaly Detection

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods.
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Authors: Shantanu Jaiswal, Basura Fernando, Cheston Tan
Date: 2021-11-26 00:00:00
Tasks: feature selection, Image Classification, Multi-Label Classification, Object Localization, Object Recognition, Weakly-Supervised Object Localization

Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks. While existing methods appropriately model channel-, spatial- and self-attention, they primarily operate in a feedforward bottom-up manner. Consequently, the attention mechanism strongly depends on the local information of a single input feature map and does not incorporate relatively semantically-richer contextual information available at higher layers that can specify "what and where to look" in lower-level feature maps through top-down information flow. Accordingly, in this work, we propose a lightweight top-down attention module (TDAM) that iteratively generates a "visual searchlight" to perform channel and spatial modulation of its inputs and outputs more contextually-relevant feature maps at each computation step. Our experiments indicate that TDAM enhances the performance of CNNs across multiple object-recognition benchmarks and outperforms prominent attention modules while being more parameter and memory efficient. Further, TDAM-based models learn to "shift attention" by localizing individual objects or features at each computation step without any explicit supervision resulting in a 5% improvement for ResNet50 on weakly-supervised object localization. Source code and models are publicly available at: https://github.com/shantanuj/TDAM_Top_down_attention_module .
Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions
Authors: Omar Alfarisi, Aikifa Raza, Djamel Ouzzane, Hongxia Li, Mohamed Sassi, Tiejun Zhang
Date: 2021-11-26 00:00:00
Tasks: BIG-bench Machine Learning, Decoder

Permeability has a dominant influence on the flow properties of a natural fluid. Lattice Boltzmann simulator determines permeability from the nano and micropore network. The simulator holds millions of flow dynamics calculations with its accumulated errors and high consumption of computing power. To efficiently and consistently predict permeability, we propose a morphology decoder, a parallel and serial flow reconstruction of machine learning segmented heterogeneous Cretaceous texture from 3D micro computerized tomography and nuclear magnetic resonance images. For 3D vision, we introduce controllable-measurable-volume as new supervised segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies boundaries in a novel way to produce permeability. Morphology decoder method consists of five novel processes, which describes in this paper, these novel processes are: (1) Geometrical 3D Permeability, (2) Machine Learning guided 3D Properties Recognition of Rock Morphology, (3) 3D Image Properties Integration Model for Permeability, (4) MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).
Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network
Authors: Esther Dietrich, Patrick Fuhlert, Anne Ernst, Guido Sauter, Maximilian Lennartz, H. Siegfried Stiehl, Marina Zimmermann, Stefan Bonn
Date: 2021-11-26 00:00:00
Tasks: Multiple Instance Learning, Prediction, Survival Prediction

Clinical decision support for histopathology image data mainly focuses on strongly supervised annotations, which offers intuitive interpretability, but is bound by expert performance. Here, we propose an explainable cancer relapse prediction network (eCaReNet) and show that end-to-end learning without strong annotations offers state-of-the-art performance while interpretability can be included through an attention mechanism. On the use case of prostate cancer survival prediction, using 14,479 images and only relapse times as annotations, we reach a cumulative dynamic AUC of 0.78 on a validation set, being on par with an expert pathologist (and an AUC of 0.77 on a separate test set). Our model is well-calibrated and outputs survival curves as well as a risk score and group per patient. Making use of the attention weights of a multiple instance learning layer, we show that malignant patches have a higher influence on the prediction than benign patches, thus offering an intuitive interpretation of the prediction. Our code is available at www.github.com/imsb-uke/ecarenet.
Confounder Identification-free Causal Visual Feature Learning
Authors: Xin Li, Zhizheng Zhang, Guoqiang Wei, Cuiling Lan, Wenjun Zeng, Xin Jin, Zhibo Chen
Date: 2021-11-26 00:00:00
Tasks: Domain Generalization, Meta-Learning

Confounders in deep learning are in general detrimental to model's generalization where they infiltrate feature representations. Therefore, learning causal features that are free of interference from confounders is important. Most previous causal learning based approaches employ back-door criterion to mitigate the adverse effect of certain specific confounder, which require the explicit identification of confounder. However, in real scenarios, confounders are typically diverse and difficult to be identified. In this paper, we propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions from the perspective of optimization. In this way, we aim to find a reliable optimization direction, which avoids the intervening effects of confounders, to learn causal features. Furthermore, we uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective of causal learning for the first time. Thanks to the effective learning of causal features, our CICF enables models to have superior generalization capability. Extensive experiments on domain generalization benchmark datasets demonstrate the effectiveness of our CICF, which achieves the state-of-the-art performance.
Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation
Authors: Zehui Liao, Shishuai Hu, Yutong Xie, Yong Xia
Date: 2021-11-26 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Manual annotation of medical images is highly subjective, leading to inevitable and huge annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators' preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution Learning (PADL) framework to address it from the perspective of disentangling an annotator's preference from stochastic errors using distribution learning so as to produce not only a meta segmentation but also the segmentation possibly made by each annotator. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator's segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks.
Reinforcement Explanation Learning
Authors: Siddhant Agarwal, Owais Iqbal, Sree Aditya Buridi, Madda Manjusha, Abir Das
Date: 2021-11-26 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Reinforcement Learning (RL)

Deep Learning has become overly complicated and has enjoyed stellar success in solving several classical problems like image classification, object detection, etc. Several methods for explaining these decisions have been proposed. Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the internals of the model to explain the decision. Most black-box methods perturb the input and observe the changes in the output. We formulate saliency map generation as a sequential search problem and leverage upon Reinforcement Learning (RL) to accumulate evidence from input images that most strongly support decisions made by a classifier. Such a strategy encourages to search intelligently for the perturbations that will lead to high-quality explanations. While successful black box explanation approaches need to rely on heavy computations and suffer from small sample approximation, the deterministic policy learned by our method makes it a lot more efficient during the inference. Experiments on three benchmark datasets demonstrate the superiority of the proposed approach in inference time over state-of-the-arts without hurting the performance. Project Page: https://cvir.github.io/projects/rexl.html
POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
Authors: Sheng Xu, Yanjing Li, Junhe Zhao, Baochang Zhang, Guodong Guo
Date: 2021-11-26 00:00:00

Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address this issue, we implement XNOR-Net-based binary neural networks (BNNs) for an efficient point cloud processing, but its performance is severely suffered due to two main drawbacks, Gaussian-distributed weights and non-learnable scale factor. In this paper, we introduce point-wise operations based on Expectation-Maximization (POEM) into BNNs for efficient point cloud processing. The EM algorithm can efficiently constrain weights for a robust bi-modal distribution. We lead a well-designed reconstruction loss to calculate learnable scale factors to enhance the representation capacity of 1-bit fully-connected (Bi-FC) layers. Extensive experiments demonstrate that our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7 %.
PicArrange -- Visually Sort, Search, and Explore Private Images on a Mac Computer
Authors: Klaus Jung, Kai Uwe Barthel, Nico Hezel, Konstantin Schall
Date: 2021-11-26 00:00:00
Tasks: Management

The native macOS application PicArrange integrates state-of-the-art image sorting and similarity search to enable users to get a better overview of their images. Many file and image management features have been added to make it a tool that addresses a full image management workflow. A modification of the Self Sorting Map algorithm enables a list-like image arrangement without loosing the visual sorting. Efficient calculation and storage of visual features as well as the use of many macOS APIs result in an application that is fluid to use.
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Authors: Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Date: 2021-11-26 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its importance in mission-critical systems and broader applicability over its supervised counterpart. Despite this increase in attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most popular state-of-the-art methods are unable to consistently outperform a simple anomaly detector based on pre-trained features and the Mahalanobis distance (MahaAD). A key reason for the inconsistencies of these methods is the lack of a formal description of U-OOD. Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset. We show how this characterization is unknowingly embodied in the top-scoring MahaAD method, thereby explaining its quality. Furthermore, our approach can be used to interpret predictions of U-OOD detectors and provides insights into good practices for evaluating future U-OOD methods.
Neural Collaborative Graph Machines for Table Structure Recognition
Authors: Hao liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren
Date: 2021-11-26 00:00:00
Proceeding: CVPR 2022 1
Tasks: Table Recognition

Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually reasoning in terms of multiple modalities can be appropriate for all varieties of table structures with great diversity. Instead, different modalities are expected to collaborate with each other in different patterns for different table cases. In the community, the importance of intra-inter modality interactions for table structure reasoning is still unexplored. In this paper, we define it as heterogeneous table structure recognition (Hetero-TSR) problem. With the aim of filling this gap, we present a novel Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative blocks, which alternatively extracts intra-modality context and models inter-modality interactions in a hierarchical way. It can represent the intra-inter modality relationships of tabular elements more robustly, which significantly improves the recognition performance. We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases. Experimental results on benchmarks demonstrate our proposed NCGM achieves state-of-the-art performance and beats other contemporary methods by a large margin especially under challenging scenarios.
Contrastive Vicinal Space for Unsupervised Domain Adaptation
Authors: Jaemin Na, Dongyoon Han, Hyung Jin Chang, Wonjun Hwang
Date: 2021-11-26 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two subspaces through the solution of the minimax problem: contrastive space and consensus space. In the contrastive space, inter-domain discrepancy is mitigated by constraining instances to have contrastive views and labels, and the consensus space reduces the confusion between intra-domain categories. The effectiveness of our method is demonstrated on public benchmarks, including Office-31, Office-Home, and VisDA-C, achieving state-of-the-art performances. We further show that our method outperforms the current state-of-the-art methods on PACS, which indicates that our instance-wise approach works well for multi-source domain adaptation as well. Code is available at https://github.com/NaJaeMin92/CoVi.
Jointly Learning Agent and Lane Information for Multimodal Trajectory Prediction
Authors: Jie Wang, Caili Guo, Minan Guo, Jiujiu Chen
Date: 2021-11-26 00:00:00
Tasks: Autonomous Vehicles, Trajectory Prediction

Predicting the plausible future trajectories of nearby agents is a core challenge for the safety of Autonomous Vehicles and it mainly depends on two external cues: the dynamic neighbor agents and static scene context. Recent approaches have made great progress in characterizing the two cues separately. However, they ignore the correlation between the two cues and most of them are difficult to achieve map-adaptive prediction. In this paper, we use lane as scene data and propose a staged network that Jointly learning Agent and Lane information for Multimodal Trajectory Prediction (JAL-MTP). JAL-MTP use a Social to Lane (S2L) module to jointly represent the static lane and the dynamic motion of the neighboring agents as instance-level lane, a Recurrent Lane Attention (RLA) mechanism for utilizing the instance-level lanes to predict the map-adaptive future trajectories and two selectors to identify the typical and reasonable trajectories. The experiments conducted on the public Argoverse dataset demonstrate that JAL-MTP significantly outperforms the existing models in both quantitative and qualitative.