GenAI4ELab/papercli-papers huggingface dataset:
Avoiding Lingering in Learning Active Recognition by Adversarial Disturbance
Authors: Lei Fan; Ying Wu
Venue: WACV 2023 (main)

Abstract: This paper considers the active recognition scenario, where the agent is empowered to intelligently acquire observations for better recognition. The agents usually compose two modules, i.e., the policy and the recognizer, to select actions and predict the category. While using ground-truth class labels to supervise the recognizer, the policy is typically updated with rewards determined by the current in-training recognizer, like whether achieving correct predictions. However, this joint learning process could lead to unintended solutions, like a collapsed policy that only visits views that the recognizer is already sufficiently trained to obtain rewards, which harms the generalization ability. We call this phenomenon lingering to depict the agent being reluctant to explore challenging views during training. Existing approaches to tackle the exploration-exploitation trade-off could be ineffective as they usually assume reliable feedback during exploration to update the estimate of rarely-visited states. This assumption is invalid here as the reward from the recognizer could be insufficiently trained. To this end, our approach integrates another adversarial policy to constantly disturb the recognition agent during training, forming a competing game to promote active explorations and avoid lingering. The reinforced adversary, rewarded when the recognition fails, contests the recognition agent by turning the camera to challenging observations. Extensive experiments across two datasets validate the effectiveness of the proposed approach regarding its recognition performances, learning efficiencies, and especially robustness in managing environmental noises.
Segmentation-Free Direct Iris Localization Networks
Authors: Takahiro Toizumi; Koichi Takahashi; Masato Tsukada
Venue: WACV 2023 (main)

Abstract: This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. Conventional iris localization methods first extract iris regions by using semantic segmentation methods such as U-Net. Afterward, the inner and outer iris circles are localized using the traditional circle fitting algorithm. However, this approach requires high-resolution encoder-decoder networks for iris segmentation, so it causes computational costs to be high. In addition, traditional circle fitting tends to be sensitive to noise in input images and fitting parameters, causing the iris recognition performance to be poor. To solve these problems, we propose an iris localization network (ILN), that can directly localize pupil and iris circles with eyelid points from a low-resolution iris image. We also introduce a pupil refinement network (PRN) to improve the accuracy of pupil localization. Experimental results show that the combination of ILN and PRN works in 34.5 ms for one iris image on a CPU, and its localization performance outperforms conventional iris segmentation methods. In addition, generalized evaluation results show that the proposed method has higher robustness for datasets in different domain than other segmentation methods. Furthermore, we also confirm that the proposed ILN and PRN improve the iris recognition accuracy.
Semantic Segmentation With Active Semi-Supervised Learning
Authors: Aneesh Rangnekar; Christopher Kanan; Matthew Hoffman
Venue: WACV 2023 (main)

Abstract: Using deep learning, we now have the ability to create exceptionally good semantic segmentation systems; however, collecting the prerequisite pixel-wise annotations for training images remains expensive and time-consuming. Therefore, it would be ideal to minimize the number of human annotations needed when creating a new dataset. Here, we address this problem by proposing a novel algorithm that combines active learning and semi-supervised learning. Active learning is an approach for identifying the best unlabeled samples to annotate. While there has been work on active learning for segmentation, most methods require annotating all pixel objects in each image, rather than only the most informative regions. We argue that this is inefficient. Instead, our active learning approach aims to minimize the number of annotations per image. Our method is enriched with semi-supervised learning, where we use pseudo labels generated with a teacher-student framework to identify image regions that help disambiguate confused classes. We also integrate mechanisms that enable better performance on imbalanced label distributions, which have not been studied previously for active learning in semantic segmentation. In experiments on the CamVid and CityScapes datasets, our method obtains over 95% of the network's performance on the full-training set using less than 17% of the training data, whereas the previous state of the art required 40% of the training data.
Do Adaptive Active Attacks Pose Greater Risk Than Static Attacks?
Authors: Nathan Drenkow; Max Lennon; I-Jeng Wang; Philippe Burlina
Venue: WACV 2023 (main)

Abstract: In contrast to perturbation-based attacks, patch-based attacks are physically realizable, and are therefore increasingly studied. However, prior work neglects the possibility of adaptive attacks optimized for 3D pose. For the first time, to our knowledge, we consider the challenge of designing and evaluating attacks on image sequences using 3D optimization along entire 3D kinematic trajectories. In this context, we study a type of dynamic attack, referred to as "adaptive active attacks" (AAA), that takes into consideration the pose of the observer being targeted. To better address the threat and risk posed by AAA attacks, we develop several novel risk-based and trajectory-based metrics. These are designed to capture the risk of attack success for attacking earlier in the trajectory to derail autonomous driving systems as well as tradeoffs that may arise given the possibility of additional detection. We evaluate performance of white-box targeted attacks using a subset of ImageNet classes, and demonstrate, in aggregate, that AAA attacks can pose threats beyond static attacks in kinematic settings in situations of predominantly looming motion (i.,e., a prevalent use case in automated vehicular navigation). Results demonstrate that AAA attacks can exhibit targeted attack success exceeding 10% in aggregate, and for some specific classes, up to 15% over their static counterparts. However, taking into consideration the probability of detection by the defender shows a more nuanced risk pattern. These new insights are important for guiding future adversarial machine learning studies and suggest researchers should consider defense against novel threats posed by dynamic attacks for full trajectories and videos.
Improving Diversity With Adversarially Learned Transformations for Domain Generalization
Authors: Tejas Gokhale; Rushil Anirudh; Jayaraman J. Thiagarajan; Bhavya Kailkhura; Chitta Baral; Yezhou Yang
Venue: WACV 2023 (main)

Abstract: To be successful in single source domain generalization (SSDG), maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Recent success in SSDG comes from methods that pre-specify diversity inducing image augmentations during training, so that it may lead to better generalization on new domains. However, naive pre-specified augmentations are not always effective, either because they cannot model large domain shift, or because the specific choice of transforms may not cover the types of shifts commonly occurring in domain generalization. To address this issue, we present a novel framework called ALT: adversarially learned transformations, that uses an adversary neural network to model plausible, yet hard image transformations that fool the classifier. ALT learns image transformations by randomly initializing the adversary network for each batch and optimizing it for a fixed number of steps to maximize classification error. The classifier is trained by enforcing a consistency between its predictions on the clean and transformed images. With extensive empirical analysis, we find that this new form of adversarial transformations achieves both objectives of diversity and hardness simultaneously, outperforming all existing techniques on competitive benchmarks for SSDG. We also show that ALT can seamlessly work with existing diversity modules to produce highly distinct, and large transformations of the source domain leading to state-of-the-art performance. Code: https://github.com/tejas-gokhale/ALT
Heatmap-Based Out-of-Distribution Detection
Authors: Julia Hornauer; Vasileios Belagiannis
Venue: WACV 2023 (main)

Abstract: Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizing in- and out-of-distribution image regions at the same time. Given a trained and fixed classifier, we train a decoder neural network to produce heatmaps with zero response for in-distribution samples and high response heatmaps for OOD samples, based on the classifier features and the class prediction. Our main innovation lies in the heatmap definition for an OOD sample, as the normalized difference from the closest in-distribution sample. The heatmap serves as a margin to distinguish between in- and out-of-distribution samples. Our approach generates the heatmaps not only for OOD detection, but also to indicates in- and out-of-distribution regions of the input image. In our evaluations, our approach mostly outperforms the prior work on fixed classifiers, trained on CIFAR-10, CIFAR-100 and Tiny ImageNet. The code is publicly available at: https://github.com/jhornauer/heatmap_ood.
Efficient Few-Shot Learning for Pixel-Precise Handwritten Document Layout Analysis
Authors: Axel De Nardin; Silvia Zottin; Matteo Paier; Gian Luca Foresti; Emanuela Colombi; Claudio Piciarelli
Venue: WACV 2023 (main)

Abstract: Layout analysis is a task of uttermost importance in ancient handwritten document analysis and represents a fundamental step toward the simplification of subsequent tasks such as optical character recognition and automatic transcription. However, many of the approaches adopted to solve this problem rely on a fully supervised learning paradigm. While these systems achieve very good performance on this task, the drawback is that pixel-precise text labeling of the entire training set is a very time-consuming process, which makes this type of information rarely available in a real-world scenario. In the present paper, we address this problem by proposing an efficient few-shot learning framework that achieves performances comparable to current state-of-the-art fully supervised methods on the publicly available DIVA-HisDB dataset
Meta-Learning for Adaptation of Deep Optical Flow Networks
Authors: Chaerin Min; Taehyun Kim; Jongwoo Lim
Venue: WACV 2023 (main)

Abstract: In this paper, we propose an instance-wise meta-learning algorithm for optical flow domain adaptation. Typical optical flow algorithms with deep learning suffer from weak cross-domain performance since their trainings largely rely on synthetic datasets in specific domains. This prevents optical flow performance on different scenes from carrying similar performance in practice. Meanwhile, test-time domain adaptation approaches for optical flow estimation are yet to be studied. Our proposed method, with some training data, learns to adapt more sensitively to incoming inputs in the target domain. During the inference process, our method readily exploits the information only accessible in the test-time. Since our algorithm adapts to each input image, we incorporate traditional unsupervised losses for optical flow estimation. Moreover, with the observation that optical flows in a single domain typically contain many similar motions, we show that our method demonstrates high performance with only a small number of training data. This allows to save labeling efforts. Through the experiments on KITTI and MPI-Sintel datasets, our algorithm significantly outperforms the results without adaptation and shows consistently better performance in comparison to typical fine-tuning with the same amount of data. Also qualitatively our proposed method demonstrates more accurate results for the images with high errors in the original networks.
Encouraging Disentangled and Convex Representation With Controllable Interpolation Regularization
Authors: Yunhao Ge; Zhi Xu; Yao Xiao; Gan Xin; Yunkui Pang; Laurent Itti
Venue: WACV 2023 (main)

Abstract: We focus on controllable disentangled representation learning (C-Dis-RL), where users can control the partition of the disentangled latent space to factorize dataset attributes (concepts) for downstream tasks. Two general problems remain under-explored in current methods: (1) They lack comprehensive disentanglement constraints, especially missing the minimization of mutual information between different attributes across latent and observation domains. (2) They lack convexity constraints, which is important for meaningfully manipulating specific attributes for downstream tasks. To encourage both comprehensive C-Dis-RL and convexity simultaneously, we propose a simple yet efficient method: Controllable Interpolation Regularization (CIR), which creates a positive loop where disentanglement and convexity can help each other. Specifically, we conduct controlled interpolation in latent space during training, and we reuse the encoder to help form a 'perfect disentanglement' regularization. In that case, (a) disentanglement loss implicitly enlarges the potential understandable distribution to encourage convexity; (b) convexity can in turn improve robust and precise disentanglement. CIR is a general module and we merge CIR with three different algorithms: ELEGANT, I2I-Dis, and GZS-Net to show the compatibility and effectiveness. Qualitative and quantitative experiments show improvement in C-Dis-RL and latent convexity by CIR. This further improves downstream tasks: controllable image synthesis, cross-modality image translation and zero-shot synthesis.
Generative Alignment of Posterior Probabilities for Source-Free Domain Adaptation
Authors: Sachin Chhabra; Hemanth Venkateswara; Baoxin Li
Venue: WACV 2023 (main)

Abstract: Existing domain adaptation literature comprises multiple techniques that align the labeled source and unlabeled target domains at different stages, and predict the target labels. In a source-free domain adaptation setting, the source data is not available for alignment. We present a source-free generative paradigm that captures the relations between the source categories and enforces them onto the unlabeled target data, thereby circumventing the need for source data without introducing any new hyper-parameters. The adaptation is performed through the adversarial alignment of the posterior probabilities of the source and target categories. The proposed approach demonstrates competitive performance against other source-free domain adaptation techniques and can also be used for source-present settings.
DyStyle: Dynamic Neural Network for Multi-Attribute-Conditioned Style Editings
Authors: Bingchuan Li; Shaofei Cai; Wei Liu; Peng Zhang; Qian He; Miao Hua; Zili Yi
Venue: WACV 2023 (main)

Abstract: The semantic controllability of StyleGAN is enhanced by unremitting research. Although the existing weak supervision methods work well in manipulating the style codes along one attribute, the accuracy of manipulating multiple attributes is neglected. Multi-attribute representations are prone to entanglement in the StyleGAN latent space, while sequential editing leads to error accumulation. To address these limitations, we design a Dynamic Style Manipulation Network (DyStyle) whose structure and parameters vary by input samples, to perform nonlinear and adaptive manipulation of latent codes for flexible and precise attribute control. In order to efficient and stable optimization of the DyStyle network, we propose a Dynamic Multi-Attribute Contrastive Learning (DmaCL) method: including dynamic multi-attribute contrastor and dynamic multi-attribute contrastive loss, which simultaneously disentangle a variety of attributes from the generative image and latent space of model. As a result, our approach demonstrates fine-grained disentangled edits along multiple numeric and binary attributes. Qualitative and quantitative comparisons with existing style manipulation methods verify the superiority of our method in terms of the multi-attribute control accuracy and identity preservation without compromising photorealism.
Self-Attention Message Passing for Contrastive Few-Shot Learning
Authors: Ojas Kishorkumar Shirekar; Anuj Singh; Hadi Jamali-Rad
Venue: WACV 2023 (main)

Abstract: Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision. Deep learning models, however, require an abundance of data and supervision to perform at a satisfactory level. Unsupervised few-shot learning (U-FSL) is the pursuit of bridging this gap between machines and humans. Inspired by the capacity of graph neural networks (GNNs) in discovering complex inter-sample relationships, we propose a novel self-attention based message passing contrastive learning approach (coined as SAMP-CLR) for U-FSL pre-training. We also propose an optimal transport (OT) based fine-tuning strategy (we call OpT-Tune) to efficiently induce task awareness into our novel end-to-end unsupervised few-shot classification framework (SAMPTransfer). Our extensive experimental results corroborate the efficacy of SAMPTransfer in a variety of downstream few-shot classification scenarios, setting a new state-of-the-art for U-FSL on both miniImageNet and tieredImageNet benchmarks, offering up to 7%+ and 5%+ improvements, respectively. Our further investigations also confirm that SAMPTransfer remains on-par with some supervised baselines on miniImageNet and outperforms all existing U-FSL baselines in a challenging cross-domain scenario.
Composite Relationship Fields With Transformers for Scene Graph Generation
Authors: George Adaimi; David Mizrahi; Alexandre Alahi
Venue: WACV 2023 (main)

Abstract: Scene graph generation (SGG) methods extract relationships between objects. While most methods focus on improving top-down approaches, which build a scene graph based on detected objects from an off-the-shelf object detector, there is a limited amount of work on bottom-up approaches, which jointly detect objects and their relationships in a single stage. In this work, we present a novel bottom-up SGG approach by representing relationships using Composite Relationship Fields (CoRF). CoRF turns relationship detection into a dense regression and classification task, where each cell of the output feature map identifies surrounding objects and their relationships. Furthermore, we propose a refinement head that leverages Transformers for global scene reasoning, resulting in more meaningful relationship predictions. By combining both contributions, our method outperforms previous bottom-up methods on the Visual Genome dataset by 26% while preserving real-time performance.
ElliPose: Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids
Authors: Christian Grund; Julian Tanke; Jürgen Gall
Venue: WACV 2023 (main)

Abstract: One of the most relevant tasks for augmented and virtual reality applications is the interaction of virtual objects with real humans which requires accurate 3D human pose predictions. Obtaining accurate 3D human poses requires careful camera calibration which is difficult for non-technical personal or in a pop-up scenario. Recent markerless motion capture approaches require accurate camera calibration at least for the final triangulation step. Instead, we solve this problem by presenting ElliPose, Stereoscopic 3D Human Pose Estimation by Fitting Ellipsoids, where we jointly estimate the 3D human as well as the camera pose. We exploit the fact that bones do not change in length over the course of a sequence and thus their relative trajectories have to lie on the surface of a sphere which we can utilize to iteratively correct the camera and 3D pose estimation. As another use-case we demonstrate that our approach can be used as replacement for ground-truth 3D poses to train monocular 3D pose estimators. We show that our method produces competitive results even when comparing with state-of-the-art methods that use more cameras or ground-truth camera extrinsics.
Is Bigger Always Better? An Empirical Study on Efficient Architectures for Style Transfer and Beyond
Authors: Jie An; Tao Li; Haozhi Huang; Jinwen Ma; Jiebo Luo
Venue: WACV 2023 (main)

Abstract: Network architecture plays a pivotal role in the performance of style transfer algorithms. Most existing algorithms use VGG19 as the feature extractor, which incurs a high computational cost. In this work, we conduct an empirical study on the popular network architectures and find that some more efficient networks can replace VGG19 while having comparable style transfer performance. Beyond that, we show that an efficient network can be further accelerated by removing its empty channels via a simple channel pruning method tweaked for style transfer. To prevent the potential performance drop due to using a more lightweight network and obtain better style transfer results, we introduce a more accurate deep feature alignment strategy to improve existing style transfer modules. Taking GoogLeNet as an exemplary efficient network, the pruned GoogLeNet with the improved style transfer module is 2.3   107.4x faster than the state-of-the-art approaches and can achieve 68.03 FPS on 512 x 512 images. Extensive experiments demonstrate that VGG19 can be replaced by a more lightweight network with significantly improved efficiency and comparable style transfer quality.
Watching the News: Towards VideoQA Models That Can Read
Authors: Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; C. V. Jawahar
Venue: WACV 2023 (main)

Abstract: Video Question Answering methods focus on common-sense reasoning and visual cognition of objects or persons and their interactions over time. Current VideoQA approaches ignore the textual information present in the video. Instead, we argue that textual information is complementary to the action and provides essential contextualisation cues to the reasoning process. To this end, we propose a novel VideoQA task that requires reading and understanding the text in the video. To explore this direction, we focus on news videos and require QA systems to comprehend and answer questions about the topics presented by combining visual and textual cues in the video. We introduce the "NewsVideoQA" dataset that comprises more than 8,600 QA pairs on 3, 000+ news videos obtained from diverse news channels from around the world. We demonstrate the limitations of current Scene Text VQA and VideoQA methods and propose ways to incorporate scene text information into VideoQA methods.