GenAI4ELab/papercli-papers huggingface dataset:
DenseHybrid: Hybrid Anomaly Detection for Dense Open-Set Recognition
Authors: Matej Grcić; Petra Bevandić; Siniša Šegvić
Venue: ECCV 2022 (main)

Abstract: "Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches exhibit different failure modes. Consequently, hybrid algorithms present an attractive research goal. Unfortunately, dense anomaly detection requires translational equivariance and very large input resolutions. These requirements disqualify all previous hybrid approaches to the best of our knowledge. We therefore design a novel hybrid algorithm based on reinterpreting discriminative logits as a logarithm of the unnormalized joint distribution $\hat{p}(\mathbf{x},\mathbf{y})$. Our model builds on a shared convolutional representation from which we recover three dense predictions: i) the closed-set class posterior $P(\mathbf{y}|\mathbf{x})$, ii) the dataset posterior $P(\mathbf{d}_{in}|\mathbf{x})$, iii) unnormalized data likelihood $\hat{p}(\mathbf{x})$. The latter two predictions are trained both on the standard training data and on a generic negative dataset. We blend these two predictions into a hybrid anomaly score which allows dense open-set recognition on large natural images. We carefully design a custom loss for the data likelihood in order to avoid backpropagation through the untractable normalizing constant $Z(\theta)$. Experiments evaluate our contributions on standard dense anomaly detection benchmarks as well as in terms of open-mIoU - a novel metric for dense open-set performance. Our submissions achieve state-of-the-art performance despite neglectable computational overhead over the standard semantic segmentation baseline."
Rethinking Confidence Calibration for Failure Prediction
Authors: Fei Zhu; Zhen Cheng; Xu-Yao Zhang; Cheng-Lin Liu
Venue: ECCV 2022 (main)

Abstract: "Reliable confidence estimation for the predictions is important in many safety-critical applications. However, modern deep neural networks are often overconfident for their incorrect predictions. Recently, many calibration methods have been proposed to alleviate the overconfidence problem. With calibrated confidence, a primary and practical purpose is to detect misclassification errors by filtering out low-confidence predictions (known as failure prediction). In this paper, we find a general, widely-existed but actually-neglected phenomenon that most confidence calibration methods are useless or harmful for failure prediction. We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not. Finally, inspired by the natural connection between flat minima and confidence separation, we propose a simple hypothesis: flat minima is beneficial for failure prediction. We verify this hypothesis via extensive experiments and further boost the performance by combining two different flat minima techniques."
Uncertainty-Guided Source-Free Domain Adaptation
Authors: Subhankar Roy; Martin Trapp; Andrea Pilzer; Juho Kannala; Nicu Sebe; Elisa Ricci; Arno Solin
Venue: ECCV 2022 (main)

Abstract: "Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation. For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions. Uncertainties are estimated by employing a Laplace approximation and incorporated to identify target data points that do not lie in the source manifold and to down-weight them when maximizing the mutual information on the target data. Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source training or changes of the model architecture. We show the advantages of uncertainty-guided SFDA over traditional SFDA in the closed-set and open-set settings and provide empirical evidence that our approach is more robust to strong domain shifts even without tuning."
Should All Proposals Be Treated Equally in Object Detection?
Authors: Yunsheng Li; Yinpeng Chen; Xiyang Dai; Dongdong Chen; Mengchen Liu; Pei Yu; Ying Jin; Lu Yuan; Zicheng Liu; Nuno Vasconcelos
Venue: ECCV 2022 (main)

Abstract: "The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with efficient backbones. The impact on this trade-off of proposal processing by the detection head is investigated in this work. It is hypothesized that improved detection efficiency requires a paradigm shift, towards the unequal processing of proposals, assigning more computation to good proposals than poor ones. This results in better utilization of available computational budget, enabling higher accuracy for the same FLOPS. We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized. The key finding is that such matching can be learned as a function that maps each proposal embedding into a one-hot code over operators. While this function induces a complex dynamic network routing mechanism, it can be implemented by a simple MLP and learned end-to-end with off-the-shelf object detectors. This it dynamic proposal processing (DPP) is shown to outperform state-of-the-art end-to-end object detectors (DETR, Sparse R-CNN) by a clear margin for a given computational complexity."
VIP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers
Authors: Junbo Li; Huan Zhang; Cihang Xie
Venue: ECCV 2022 (main)

Abstract: "Patch attack, which introduces a perceptible but localized change to the input image, has gained significant momentum in recent years. In this paper, we propose a unified framework to analyze certified patch defense tasks (including both certified detection and certified recovery) using the recently emerged vision transformer. In addition to the existing patch defense setting where only one patch is considered, we provide the very first study on developing certified detection against the \emph{dual patch attack}, in which the attacker is allowed to adversarially manipulate pixels in two different regions. Benefiting from the recent progress in self-supervised vision transformers (\ie, masked autoencoder), our method achieves state-of-the-art performance in both certified detection and certified recovery of adversarial patches. For certified detection, we improve the performance by up to $\app16\%$ on ImageNet without additional training for a single adversarial patch, and for the first time, can also tackle the more challenging dual patch setting. Our method largely \emph{closes the gap} between detection-based certified robustness and clean image accuracy. For certified recovery, our approach improves certified accuracy by $\app2\%$ on ImageNet across all attack sizes, attaining the new state-of-the-art performance."
incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection
Authors: Amanda Rios; Nilesh Ahuja; Ibrahima Ndiour; Utku Genc; Laurent Itti; Omesh Tickoo
Venue: ECCV 2022 (main)

Abstract: "Novelty detection is a key capability for practical machine learning in the real world, where models operate in non-stationary conditions and are repeatedly exposed to new, unseen data. Yet, most current novelty detection approaches have been developed exclusively for static, offline use. They scale poorly under more realistic, continual learning regimes in which data distribution shifts occur. To address this critical gap, this paper proposes incDFM (incremental Deep Feature Modeling), a self-supervised continual novelty detector. The method builds a statistical model over the space of intermediate features produced by a deep network, and utilizes feature reconstruction errors as uncertainty scores to guide the detection of novel samples. Most importantly, incDFM estimates the statistical model incrementally (via several iterations within a task), instead of a single-shot. Each time it selects only the most confident novel samples which will then guide subsequent recruitment incrementally. For a certain task where the ML model encounters a mixture of old and novel data, the detector flags novel samples to incorporate them to old knowledge. Then the detector is updated with the flagged novel samples, in preparation for a next task. To quantify and benchmark performance, we adapted multiple datasets for continual learning: CIFAR-10, CIFAR-100, SVHN, iNaturalist, and the 8-dataset. Our experiments show that incDFM achieves state of the art continual novelty detection performance. Furthermore, when examined in the greater context of continual learning for classification, our method is successful in minimizing catastrophic forgetting and error propagation."
IGFormer: Interaction Graph Transformer for Skeleton-Based Human Interaction Recognition
Authors: Yunsheng Pang; Qiuhong Ke; Hossein Rahmani; James Bailey; Jun Liu
Venue: ECCV 2022 (main)

Abstract: "Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts, and enhances the representation of each person by aggregating the information of the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin."
PRIME: A Few Primitives Can Boost Robustness to Common Corruptions
Authors: Apostolos Modas; Rahul Rade; Guillermo Ortiz-Jiménez; Seyed-Mohsen Moosavi-Dezfooli; Pascal Frossard
Venue: ECCV 2022 (main)

Abstract: "Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multiple methods to enrich the training data. However, introducing intricate design choices or heuristics makes it hard to understand which elements of these methods are indeed crucial for improving robustness. In this work, we take a step back and follow a principled approach to achieve robustness to common corruptions. We propose PRIME, a general data augmentation scheme that relies on simple yet rich families of max-entropy image transformations. PRIME outperforms the prior art in terms of corruption robustness, while its simplicity and plug-and-play nature enable combination with other methods to further boost their robustness. We analyze PRIME to shed light on the importance of the mixing strategy on synthesizing corrupted images, and to reveal the robustness-accuracy trade-offs arising in the context of common corruptions. Finally, we show that the computational efficiency of our method allows it to be easily used in both on-line and off-line data augmentation schemes."
Rotation Regularization without Rotation
Authors: Takumi Kobayashi
Venue: ECCV 2022 (main)

Abstract: "In various visual classification tasks, we enjoy significant performance improvement by deep convolutional neural networks (CNNs). To further boost performance, it is effective to regularize feature representation learning of CNNs such as by considering margin to improve feature distribution across classes. In this paper, we propose a regularization method based on random rotation of feature vectors. Random rotation is derived from cone representation to describe angular margin of a sample. While it induces geometric regularization to randomly rotate vectors by means of rotation matrices, we theoretically formulate the regularization in a statistical form which excludes costly geometric rotation as well as effectively imposes rotation-based regularization on classification in training CNNs. In the experiments on classification tasks, the method is thoroughly evaluated from various aspects, while producing favorable performance compared to the other regularization methods. Codes are available at https://github.com/tk1980/StatRot."
Towards Accurate Open-Set Recognition via Background-Class Regularization
Authors: Wonwoo Cho; Jaegul Choo
Venue: ECCV 2022 (main)

Abstract: "In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or complicated network architectures. To conduct OSR via a simple inference process (without offline analyses) in standard classifier architectures, we use distance-based classifiers instead of conventional Softmax classifiers. Afterwards, we design a background-class regularization strategy, which uses background-class data as surrogates of unknown-class ones during training phase. Specifically, we formulate a novel regularization loss suitable for distance-based classifiers, which reserves sufficiently large class-wise latent feature spaces for known classes and forces background-class samples to be located far away from the limited spaces. Through our extensive experiments, we show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy."
In Defense of Image Pre-training for Spatiotemporal Recognition
Authors: Xianhang Li; Huiyu Wang; Chen Wei; Jieru Mei; Alan Yuille; Yuyin Zhou; Cihang Xie
Venue: ECCV 2022 (main)

Abstract: "Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is to directly train with spatiotemporal convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly, by taking a closer look at these from-scratch learned CNNs, we note there exist certain 3D kernels that exhibit much stronger appearance modeling ability than others, arguably suggesting appearance information is already well disentangled in learning. Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels. In addition, we propose Spatial-Temporal Separable (STS) convolution, which explicitly splits the feature channels into spatial and temporal groups, to further enable a more thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our experiments show that simply replacing 3D convolution with STS notably improves a wide range of 3D CNNs without increasing parameters and computation on both Kinetics-400 and Something-Something V2. Moreover, this new training pipeline consistently achieves better results on video recognition with significant speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with 4 GPUs. The code and models are available at https://github.com/UCSC-VLAA/Image-Pretraining-for-Video"
Augmenting Deep Classifiers with Polynomial Neural Networks
Authors: Grigorios G. Chrysos; Markos Georgopoulos; Jiankang Deng; Jean Kossaifi; Yannis Panagakis; Anima Anandkumar
Venue: ECCV 2022 (main)

Abstract: "Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed architectures, the majority of which are seemingly disconnected. In this work, we cast the study of deep classifiers under a unifying framework. In particular, we express state-of-the-art architectures (e.g., residual and non-local networks) in the form of different degree polynomials of the input. Our framework provides insights on the inductive biases of each model and enables natural extensions building upon their polynomial nature. The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks. The expressivity of the proposed models is highlighted both in terms of increased model performance as well as model compression. Lastly, the extensions allowed by this taxonomy showcase benefits in the presence of limited data and long-tailed data distributions. We expect this taxonomy to provide links between existing domain-specific architectures."
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Authors: Seong Min Kye; Kwanghee Choi; Joonyoung Yi; Buru Chang
Venue: ECCV 2022 (main)

Abstract: "Recent studies on learning with noisy labels have shown remarkable performance by exploiting a small clean dataset. In particular, model agnostic meta-learning-based label correction methods further improve performance by correcting noisy labels on the fly. However, there is no safeguard on the label miscorrection, resulting in unavoidable performance degradation. Moreover, every training step requires at least three back-propagations, significantly slowing down the training speed. To mitigate these issues, we propose a robust and efficient method, FasTEN, which learns a label transition matrix on the fly. Employing the transition matrix makes the classifier skeptical about all the corrected samples, which alleviates the miscorrection issue. We also introduce a two-head architecture to efficiently estimate the label transition matrix every iteration within a single back-propagation, so that the estimated matrix closely follows the shifting noise distribution induced by label correction. Extensive experiments demonstrate that our FasTEN shows the best performance in training efficiency while having comparable or better accuracy than existing methods, especially achieving state-of-the-art performance in a real-world noisy dataset, Clothing1M."
Online Task-Free Continual Learning with Dynamic Sparse Distributed Memory
Authors: Julien Pourcel; Ngoc-Son Vu; Robert M. French
Venue: ECCV 2022 (main)

Abstract: "This paper addresses the very challenging problem of online task-free continual learning in which a sequence of new tasks is learned from non-stationary data using each sample only once for training and without knowledge of task boundaries. We propose in this paper an efficient semi-distributed associative memory algorithm called Dynamic Sparse Distributed Memory (DSDM) where learning and evaluating can be carried out at any point of time. DSDM evolves dynamically and continually modeling the distribution of any non-stationary data streams. DSDM relies on locally distributed, but only partially overlapping clusters of representations to effectively eliminate catastrophic forgetting, while at the same time, maintaining the generalization capacities of distributed networks. In addition, a local density-based pruning technique is used to control the network’s memory footprint. DSDM significantly outperforms state-of-the-art continual learning methods on different image classification baselines, even in a low data regime. Code is publicly available: https://github.com/Julien-pour/Dynamic-Sparse-Distributed-Memory "
Contrastive Deep Supervision
Authors: Linfeng Zhang; Xin Chen; Junbo Zhang; Runpei Dong; Kaisheng Ma
Venue: ECCV 2022 (main)

Abstract: "The success of deep learning is usually accompanied by the growth in neural network depth. However, the traditional training method only supervises the neural network at its last layer and propagates the supervision layer-by-layer, which leads to hardship in optimizing the intermediate layers. Recently, deep supervision has been proposed to add auxiliary classifiers to the intermediate layers of deep neural networks. By optimizing these auxiliary classifiers with the supervised task loss, the supervision can be applied to the shallow layers directly. However, deep supervision conflicts with the well-known observation that the shallow layers learn low-level features instead of task-biased high-level semantic features. To address this issue, this paper proposes a novel training framework named Contrastive Deep Supervision, which supervises the intermediate layers with augmentation-based contrastive learning. Experimental results on nine popular datasets with eleven models demonstrate its effects on general image classification, fine-grained image classification and object detection in supervised learning, semi-supervised learning and knowledge distillation. Codes have been released in Github."
Discriminability-Transferability Trade-Off: An Information-Theoretic Perspective
Authors: Quan Cui; Bingchen Zhao; Zhao-Min Chen; Borui Zhao; Renjie Song; Boyan Zhou; Jiajun Liang; Osamu Yoshie
Venue: ECCV 2022 (main)

Abstract: "This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i.e., image classification. By a comprehensive temporal analysis, we observe a trade-off between these two properties. The discriminability keeps increasing with the training progressing while the transferability intensely diminishes in the later training period. From the perspective of information-bottleneck theory, we reveal that the incompatibility between discriminability and transferability is attributed to the over-compression of input information. More importantly, we investigate why and how the InfoNCE loss can alleviate the over-compression, and further present a learning framework, named contrastive temporal coding (CTC), to counteract the over-compression and alleviate the incompatibility. Extensive experiments validate that CTC successfully mitigates the incompatibility, yielding discriminative and transferable representations. Noticeable improvements are achieved on the image classification task and challenging transfer learning tasks. We hope that this work will raise the significance of the transferability property in the conventional supervised learning setting."