GenAI4ELab/papercli-papers huggingface dataset:
Language-Free Training for Zero-Shot Video Grounding
Authors: Dahye Kim; Jungin Park; Jiyoung Lee; Seongheon Park; Kwanghoon Sohn
Venue: WACV 2023 (main)

Abstract: Given an untrimmed video and a language query depicting a specific temporal moment in the video, video grounding aims to localize the time interval by understanding the text and video simultaneously. One of the most challenging issues is an extremely time- and cost-consuming annotation collection, including video captions in a natural language form and their corresponding temporal regions. In this paper, we present a simple yet novel training framework for video grounding in the zero-shot setting, which learns a network with only video data without any annotation. Inspired by the recent language-free paradigm, i.e. training without language data, we train the network without compelling the generation of fake (pseudo) text queries into a natural language form. Specifically, we propose a method for learning a video grounding model by selecting a temporal interval as a hypothetical correct answer and considering the visual feature selected by our method in the interval as a language feature, with the help of the well-aligned visual-language space of CLIP. Extensive experiments demonstrate the prominence of our language-free training framework, outperforming the existing zero-shot video grounding method and even several weakly-supervised approaches with large margins on two standard datasets.
Weakly-Supervised Point Cloud Instance Segmentation With Geometric Priors
Authors: Heming Du; Xin Yu; Farookh Hussain; Mohammad Ali Armin; Lars Petersson; Weihao Li
Venue: WACV 2023 (main)

Abstract: This paper investigates how to leverage more readily acquired annotations, i.e., 3D bounding boxes instead of dense point-wise labels, for instance segmentation. We propose a Weakly-supervised point cloud Instance Segmentation framework with Geometric Priors (WISGP) that allows segmentation models to be trained with 3D bounding boxes of instances. Considering intersections among bounding boxes in a scene would result in ambiguous labels, we first group points into two sets, i.e., univocal and equivocal sets, indicating the certainty of a 3D point belonging to an instance, respectively. Specifically, 3D points with clear labels belong to the univocal set while the rest are grouped into the equivocal set. To assign reliable labels to points in the equivocal set, we design a Geometry-guided Label Propagation (GLP) scheme that progressively propagates labels to linked points based on geometric structure, e.g., polygon meshes and superpoints. Afterwards, we train an instance segmentation model with the univocal points and equivocal points labeled by GLP, and then employ it to assign pseudo labels for the remainder of the unlabeled points. Lastly, we retrain the model with all the labeled points to achieve better instance segmentation performance. Experiments on large-scale datasets ScanNet-v2 and S3DIS demonstrate that WISGP is superior to competing weakly-supervised algorithms and even on par with a few fully-supervised ones.
Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer
Authors: Junming Chen; Meirui Jiang; Qi Dou; Qifeng Chen
Venue: WACV 2023 (main)

Abstract: Domain generalization (DG) has been a hot topic in image recognition, with a goal to train a general model that can perform well on unseen domains. Recently, federated learning (FL), an emerging machine learning paradigm to train a global model from multiple decentralized clients without compromising data privacy, brings new challenges, also new possibilities, to DG. In the FL scenario, many existing state-of-the-art (SOTA) DG methods become ineffective, because they require the centralization of data from different domains during training. In this paper, we propose a novel domain generalization method for image recognition under federated learning through cross-client style transfer (CCST) without exchanging data samples. Our CCST method can lead to more uniform distributions of source clients, and thus make each local model learn to fit the image styles of all the clients to avoid the different model biases. Two types of style (single image style and overall domain style) with corresponding mechanisms are proposed to be chosen according to different scenarios. Our style representation is exceptionally lightweight and can hardly be used for the reconstruction of the dataset. The level of diversity is also flexible to be controlled with a hyper-parameter. Our method outperforms recent SOTA DG methods on two DG benchmarks (PACS, OfficeHome) and a large-scale medical image dataset (Camelyon17) in the FL setting. Last but not least, our method is orthogonal to many classic DG methods, achieving additive performance by combined utilization.
BEVSegFormer: Bird's Eye View Semantic Segmentation From Arbitrary Camera Rigs
Authors: Lang Peng; Zhirong Chen; Zhangjie Fu; Pengpeng Liang; Erkang Cheng
Venue: WACV 2023 (main)

Abstract: Semantic segmentation in bird's eye view (BEV) is an important task for autonomous driving. Though this task has attracted a large amount of research efforts, it is still challenging to flexibly cope with arbitrary (single or multiple) camera sensors equipped on the autonomous vehicle. In this paper, we present BEVSegFormer, an effective transformer-based method for BEV semantic segmentation from arbitrary camera rigs. Specifically, our method first encodes image features from arbitrary cameras with a shared backbone. These image features are then enhanced by a deformable transformer-based encoder. Moreover, we introduce a BEV transformer decoder module to parse BEV semantic segmentation results. An efficient multi-camera deformable attention unit is designed to carry out the BEV-to-image view transformation. Finally, the queries are reshaped according the layout of grids in the BEV, and upsampled to produce the semantic segmentation result in a supervised manner. We evaluate the proposed algorithm on the public nuScenes dataset and a self-collected dataset. Experimental results show that our method achieves promising performance on BEV semantic segmentation from arbitrary camera rigs. We also demonstrate the effectiveness of each component via ablation study.
Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion
Authors: Srikrishna Jaganathan; Maximilian Kukla; Jian Wang; Karthik Shetty; Andreas Maier
Venue: WACV 2023 (main)

Abstract: Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training. However, the need for paired CT volume and X-ray images with ground truth registration limits the applicability in interventional scenarios. An alternative is to use simulated X-ray projections from CT volumes, thus removing the need for paired annotated datasets. Deep Neural Networks trained exclusively on simulated X-ray projections can perform significantly worse on real X-ray images due to the domain gap. We propose a self-supervised 2D/3D registration framework combining simulated training with unsupervised feature and pixel space domain adaptation to overcome the domain gap and eliminate the need for paired annotated datasets. Our framework achieves a registration accuracy of 1.83 +-1.16 mm with a high success ratio of 90.1% on real X-ray images showing a 23.9% increase in success ratio compared to reference annotation-free algorithms.
Trans4Map: Revisiting Holistic Bird's-Eye-View Mapping From Egocentric Images to Allocentric Semantics With Vision Transformers
Authors: Chang Chen; Jiaming Zhang; Kailun Yang; Kunyu Peng; Rainer Stiefelhagen
Venue: WACV 2023 (main)

Abstract: Humans have an innate ability to sense their surroundings, as they can extract the spatial representation from the egocentric perception and form an allocentric semantic map via spatial transformation and memory updating. However, endowing mobile agents with such a spatial sensing ability is still a challenge, due to two difficulties: (1) the previous convolutional models are limited by the local receptive field, thus, struggling to capture holistic long-range dependencies during observation; (2) the excessive computational budgets required for success, often lead to a separation of the mapping pipeline into stages, resulting the entire mapping process inefficient. To address these issues, we propose an end-to-end one-stage Transformer-based framework for Mapping, termed Trans4Map. Our egocentric-to-allocentric mapping process includes three steps: (1) the efficient transformer extracts the contextual features from a batch of egocentric images; (2) the proposed Bidirectional Allocentric Memory (BAM) module projects egocentric features into the allocentric memory; (3) the map decoder parses the accumulated memory and predicts the top-down semantic segmentation map. In contrast, Trans4Map achieves state-of-the-art results, reducing 67.2% parameters, yet gaining a +3.25% mIoU and a +4.09% mBF1 improvements on the Matterport3D dataset.
Dense Voxel Fusion for 3D Object Detection
Authors: Anas Mahmoud; Jordan S. K. Hu; Steven L. Waslander
Venue: WACV 2023 (main)

Abstract: Camera and LiDAR sensor modalities provide complementary appearance and geometric information useful for detecting 3D objects for autonomous vehicle applications. However, current end-to-end fusion methods are challenging to train and underperform state-of-the-art LiDAR-only detectors. Sequential fusion methods suffer from a limited number of pixel and point correspondences due to point cloud sparsity, or their performance is strictly capped by the detections of one of the modalities. Our proposed solution, Dense Voxel Fusion (DVF) is a sequential fusion method that generates multi-scale dense voxel feature representations, improving expressiveness in low point density regions. To enhance multi-modal learning, we train directly with projected ground truth 3D bounding box labels, avoiding noisy, detector-specific 2D predictions. Both DVF and the multi-modal training approach can be applied to any voxel-based LiDAR backbone. DVF ranks 3rd among published fusion methods on KITTI's 3D car detection benchmark without introducing additional trainable parameters, nor requiring stereo images or dense depth labels. In addition, DVF significantly improves 3D vehicle detection performance of voxel-based methods on the Waymo Open Dataset.
Effective Invertible Arbitrary Image Rescaling
Authors: Zhihong Pan; Baopu Li; Dongliang He; Wenhao Wu; Errui Ding
Venue: WACV 2023 (main)

Abstract: Great successes have been achieved using deep learning techniques for image super-resolution (SR) with fixed scales. To increase its real world applicability, numerous models have also been proposed to restore SR images with arbitrary scale factors, including asymmetric ones where images are resized to different scales along horizontal and vertical directions. Though most models are only optimized for the unidirectional upscaling task while assuming a predefined downscaling kernel for low-resolution (LR) inputs, recent models based on Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. However, limited by the INN architecture, it is constrained to fixed integer scale factors and requires one model for each scale. Without increasing model complexity, a simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. Using innovative components like position-aware scale encoding and preemptive channel splitting, the network is optimized to convert the non-invertible rescaling cycle to an effectively invertible process. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs. It is also demonstrated to perform well on tests with asymmetric scales using the same network architecture.
FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
Authors: Felix Rosberg; Eren Erdal Aksoy; Fernando Alonso-Fernandez; Cristofer Englund
Venue: WACV 2023 (main)

Abstract: In this work, we present a new single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. We have two major contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and features conditioned on identity information without requiring any additional facial segmentation process. In IFSR, we leverage the intermediate features in an identity encoder to preserve important attributes such as head pose, facial expression, lighting, and occlusion in the target face, while still transferring the identity of the source face with high fidelity. We conduct extensive quantitative and qualitative experiments on various datasets and show that the proposed FaceDancer outperforms other state-of-the-art networks in terms of identity transfer, while having significantly better pose preservation than most of the previous methods.
Few-Shot Medical Image Segmentation With Cycle-Resemblance Attention
Authors: Hao Ding; Changchang Sun; Hao Tang; Dawen Cai; Yan Yan
Venue: WACV 2023 (main)

Abstract: Recently, due to the increasing requirements of medical imaging applications and the professional requirements of annotating medical images, few-shot learning has gained increasing attention in the medical image semantic segmentation field. To perform segmentation with limited number of labeled medical images, most existing studies use Prototypical Networks (PN) and have obtained compelling success. However, these approaches overlook the query image features extracted from the proposed representation network, failing to preserving the spatial connection between query and support images. In this paper, we propose a novel self-supervised few-shot medical image segmentation network and introduce a novel Cycle-Resemblance Attention (CRA) module to fully leverage the pixel-wise relation between query and support medical images. Notably, we first line up multiple attention blocks to refine more abundant relation information. Then, we present CRAPNet by integrating the CRA module with a classic prototype network, where pixel-wise relations between query and support features are well recaptured for segmentation. Extensive experiments on two different medical image datasets, e.g., abdomen MRI and abdomen CT, demonstrate the superiority of our model over existing state-of-the-art methods.
Dataset Condensation With Distribution Matching
Authors: Bo Zhao; Hakan Bilen
Venue: WACV 2023 (main)

Abstract: Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising direction for reducing training cost is dataset condensation that aims to replace the original large training set with a significantly smaller learned synthetic set while preserving the original information. While training deep models on the small set of condensed images can be extremely fast, their synthesis remains computationally expensive due to the complex bi-level optimization and second-order derivative computation. In this work, we propose a simple yet effective method that synthesizes condensed images by matching feature distributions of the synthetic and original training images in many sampled embedding spaces. Our method significantly reduces the synthesis cost while achieving comparable or better performance. Thanks to its efficiency, we apply our method to more realistic and larger datasets with sophisticated neural architectures and obtain a significant performance boost. We also show promising practical benefits of our method in continual learning and neural architecture search.
Can Shadows Reveal Biometric Information?
Authors: Safa C. Medin; Amir Weiss; Frédo Durand; William T. Freeman; Gregory W. Wornell
Venue: WACV 2023 (main)

Abstract: We study the problem of extracting biometric information of individuals by looking at shadows of objects cast on diffuse surfaces. We show that the biometric information leakage from shadows can be sufficient for reliable identity inference under representative scenarios via a maximum likelihood analysis. We then develop a learning-based method that demonstrates this phenomenon in real settings, exploiting the subtle cues in the shadows that are the source of the leakage without requiring any labeled real data. In particular, our approach relies on building synthetic scenes composed of 3D face models obtained from a single photograph of each identity. We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way. Our model is able to generalize well to the real domain and is robust to several variations in the scenes. We report high classification accuracies in an identity classification task that takes place in a scene with unknown geometry and occluding objects.
Neural Weight Search for Scalable Task Incremental Learning
Authors: Jian Jiang; Oya Celiktutan
Venue: WACV 2023 (main)

Abstract: Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting. One promising approach is to build an individual network or sub-network for future tasks. However, this leads to an ever-growing memory due to saving extra weights for new tasks and how to address this issue has remained an open problem in task incremental learning. In this paper, we introduce a novel Neural Weight Search technique that designs a fixed search space where the optimal combinations of frozen weights can be searched to build new models for novel tasks in an end-to-end manner, resulting in a scalable and controllable memory growth. Extensive experiments on two benchmarks, i.e., Split-CIFAR-100 and CUB-to-Sketches, show our method achieves state-of-the-art performance with respect to both average inference accuracy and total memory cost.
ReEnFP: Detail-Preserving Face Reconstruction by Encoding Facial Priors
Authors: Yasheng Sun; Jiangke Lin; Hang Zhou; Zhiliang Xu; Dongliang He; Hideki Koike
Venue: WACV 2023 (main)

Abstract: We address the problem of face modeling, which is still challenging in achieving high-quality reconstruction results efficiently. Neither previous regression-based nor optimization-based frameworks could well balance between the facial reconstruction fidelity and efficiency. We notice that the large amount of in-the-wild facial images contain diverse appearance information, however, their underlying knowledge is not fully exploited for face modeling. To this end, we propose our Reconstruction by Encoding Facial Priors (ReEnFP) pipeline to exploit the potential of unconstrained facial images for further improvement. Our key is to encode generative priors learned by a style-based texture generator on unconstrained data for fast and detail-preserving face reconstruction. With our texture generator pre-trained using a differentiable renderer, faces could be encoded to its latent space as opposed to the time-consuming optimization-based inversion. Our generative prior encoding is further enhanced with a pyramid fusion block for adaptive integration of input spatial information. Extensive experiments show that our method reconstructs photo-realistic facial textures and geometric details with precise identity recovery.
Certified Defense for Content Based Image Retrieval
Authors: Kazuya Kakizaki; Kazuto Fukuchi; Jun Sakuma
Venue: WACV 2023 (main)

Abstract: This paper develops a certified defense for deep neural network (DNN) based content based image retrieval (CBIR) against adversarial examples (AXs). Previous works put their effort into certified defense for classification to improve certified robustness, which guarantees that no AX to cause misclassification exists around the sample. Such certified defense, however, could not be applied to CBIR directly because the goals of adversarial attack against classification and CBIR are completely different. To develop the certified defense for CBIR, we first define new certified robustness of CBIR, which guarantees that no AX that changes the ranking of CBIR exists around the query or candidate images. Then, we propose computationally tractable verification algorithms that verify whether the certified robustness of CBIR is achieved by utilizing upper and lower bounds of distances between feature representations of perturbed and non-perturbed images. Finally, we propose new objective functions for training feature extraction DNNs that increases the number of inputs that satisfy the certified robustness of CBIR by tightening the upper and lower bounds. Experimental results show that our objective functions significantly improve the certified robustness of CBIR than existing methods.
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Authors: David Brüggemann; Christos Sakaridis; Prune Truong; Luc Van Gool
Venue: WACV 2023 (main)

Abstract: Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images. UDA adapts models trained on normal conditions to the target adverse-condition domains. Meanwhile, multiple datasets with driving scenes provide corresponding images of the same scenes across multiple conditions, which can serve as a form of weak supervision for domain adaptation. We propose Refign, a generic extension to self-training-based UDA methods which leverages these cross-domain correspondences. Refign consists of two steps: (1) aligning the normal-condition image to the corresponding adverse-condition image using an uncertainty-aware dense matching network, and (2) refining the adverse prediction with the normal prediction using an adaptive label correction mechanism. We design custom modules to streamline both steps and set the new state of the art for domain-adaptive semantic segmentation on several adverse-condition benchmarks, including ACDC and Dark Zurich. The approach introduces no extra training parameters, minimal computational overhead--during training only--and can be used as a drop-in extension to improve any given self-training-based UDA method. Code is available at https://github.com/brdav/refign.
ETR: An Efficient Transformer for Re-Ranking in Visual Place Recognition
Authors: Hao Zhang; Xin Chen; Heming Jing; Yingbin Zheng; Yuan Wu; Cheng Jin
Venue: WACV 2023 (main)

Abstract: Visual place recognition is to estimate the geographical location of a given image, which is usually addressed by recognizing its similar reference images from a database. The reference images are usually retrieved via similarity search using global descriptor, and the local descriptors are used to re-rank the initial retrieved candidates. The local descriptors re-ranking can significantly improve the accuracy of global retrieval but comes at a high computational cost. To achieve a good trade-off between accuracy and efficiency, we propose an Efficient Transformer for Re-ranking (ETR), utilizing both global and local descriptors to re-rank the top candidates in a single shot. In contrast to traditional re-ranking methods, we leverage self-attention to capture relationships between local descriptors in a single image and cross-attention to explore the similarity of the image pairs. We show that the proposed model can be regarded as a general re-ranking algorithm for significantly boosting the performance of other global-only retrieval methods. Extensive experimental results show that our method outperforms state-of-the-arts and is orders of magnitude faster in terms of computational efficiency.