GenAI4ELab/papercli-papers huggingface dataset:
On Quantizing Implicit Neural Representations
Authors: Cameron Gordon; Shin-Fang Chng; Lachlan MacDonald; Simon Lucey
Venue: WACV 2023 (main)

Abstract: The role of quantization within implicit/coordinate neural networks is still not fully understood. We note that using a canonical fixed quantization scheme during training produces poor performance at low bit-rates due to the network weight distributions changing over the course of training. In this work, we show that a non-uniform quantization of neural weights can lead to significant improvements. Specifically, we demonstrate that a clustered quantization enables improved reconstruction. Finally, by characterising a trade-off between quantization and network capacity, we demonstrate that it is possible (while memory inefficient) to reconstruct signals using binary neural networks. We demonstrate our findings experimentally on 2D image reconstruction and 3D radiance fields; and show that simple quantization methods and architecture search can achieve compression of NeRF to less than 16kb with minimal loss in performance (323x smaller than the original NeRF).
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
Authors: Arthur Moreau; Thomas Gilles; Nathan Piasco; Dzmitry Tsishkou; Bogdan Stanciulescu; Arnaud de La Fortelle
Venue: WACV 2023 (main)

Abstract: We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments. Visual localization algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or a 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Very large environments force competitors to store gigabytes of map data, whereas our method is very compact independently of the reference database size. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.
A Protocol for Evaluating Model Interpretation Methods From Visual Explanations
Authors: Hamed Behzadi-Khormouji; José Oramas
Venue: WACV 2023 (main)

Abstract: With the continuous development of Convolutional Neural Networks (CNNs), there is an increasing requirement towards the understanding of the representations they internally encode. The task of studying such encoded representations is referred to as model interpretation. Efforts along this direction, despite being proved efficient, stand with two weaknesses. First, there is low semanticity on the feedback they provide which leads toward subjective visualizations. Second, there is no unified protocol for the quantitative evaluation of interpretation methods which makes the comparison between current and future methods complex.\nTo address these issues, we propose a unified evaluation protocol for the quantitative evaluation of interpretation methods. This is achieved by enhancing existing interpretation methods to be capable of generating visual explanations and then linking these explanations with a semantic label. To achieve this, we introduce the Weighted Average Intersection-over-Union (WAIoU) metric to estimate the coverage rate between explanation heatmaps and semantic annotations. This is complemented with an analysis of several binarization techniques for heatmaps, necessary when measuring coverage. Experiments considering several interpretation methods covering different CNN architectures pre-trained on multiple datasets show the effectiveness of the proposed protocol.
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
Authors: Gustav Bredell; Ertunc Erdil; Bruno Weber; Ender Konukoglu
Venue: WACV 2023 (main)

Abstract: Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. Its ill-posed nature demands adequate priors and initialization to arrive at a desirable solution. Recently, it has been shown that deep networks can serve as an image generation prior (DIP) during unsupervised blind deconvolution optimization, however, DIP's high frequency artifact suppression ability is not explicitly exploited. We propose to use Wiener-deconvolution to guide DIP during optimization in order to better leverage DIP's ability for blind image deconvolution. Wiener-deconvolution sharpens an image while introducing high-frequency artifacts, which are reproduced by DIP with a delay compared to low-frequency features and sharp edges, similar to what has been observed for noise. We embed the computational process in a constrained optimization problem together with an automatic kernel initialization method and show that the proposed method yields higher performance and stability across multiple datasets.
GEMS: Scene Expansion Using Generative Models of Graphs
Authors: Rishi Agarwal; Tirupati Saketh Chandra; Vaidehi Patil; Aniruddha Mahapatra; Kuldeep Kulkarni; Vishwa Vinay
Venue: WACV 2023 (main)

Abstract: Applications based on image retrieval require editing and associating in intermediate spaces that are representative of the high-level concepts like objects and their relationships rather than dense, pixel-level representations like RGB images or semantic-label maps. We focus on one such representation, scene graphs, and propose a novel scene expansion task where we enrich an input seed graph by adding new nodes (objects) and the corresponding relationships. To this end, we formulate scene graph expansion as a sequential prediction task involving multiple iterations of first predicting a new node and then predicting the set of relationships between the newly predicted node and previously chosen nodes in the graph. We propose and evaluate a sequencing strategy that retains the clustering patterns amongst nodes. In addition, we leverage external knowledge to train our graph generation model, enabling greater generalization of node predictions. Due to the inefficiency of existing maximum mean discrepancy (MMD) based metrics standard for graph generation problems, we design novel metrics that comprehensively evaluate different aspects of node and relation predictions. We conduct extensive experiments on Visual Genome and VRD datasets to evaluate the expanded scene graphs using the standard MMD based metrics and our proposed metrics. We observe that the graphs generated by our method, GEMS, better represent the real distribution of the scene graphs compared with baseline methods like GraphRNN.
Uncertainty-Aware Label Distribution Learning for Facial Expression Recognition
Authors: Nhat Le; Khanh Nguyen; Quang Tran; Erman Tjiputra; Bac Le; Anh Nguyen
Venue: WACV 2023 (main)

Abstract: Despite significant progress over the past few years, ambiguity is still a key challenge in Facial Expression Recognition (FER). It can lead to noisy and inconsistent annotation, which hinders the performance of deep learning models in real-world scenarios. In this paper, we propose a new uncertainty-aware label distribution learning method to improve the robustness of deep models against uncertainty and ambiguity. We leverage neighborhood information in the valence-arousal space to adaptively construct emotiona distributions for training samples. We also consider the uncertainty of provided labels when incorporating them into the label distributions. Our method can be easily integrated into a deep network to obtain more training supervision and improve recognition accuracy. Intensive experiments on several datasets under various noisy and ambiguous settings show that our method achieves competitive results and outperforms recent state-of-the-art approaches. Our code and models are available at https://github.com/minhnhatvt/label-distribution-learning-fer-tf.
Self-Supervised Monocular Depth Estimation From Thermal Images via Adversarial Multi-Spectral Adaptation
Authors: Ukcheol Shin; Kwanyong Park; Byeong-Uk Lee; Kyunghyun Lee; In So Kweon
Venue: WACV 2023 (main)

Abstract: Recently, thermal image based 3D understanding is gradually attracting attention for an illumination condition agnostic machine vision. However, the difficulty of the thermal image lies in insufficient training supervision due to its low-contrast and textureless properties. Also, introducing additional modality requires further constraints such as complicated multi-sensor calibration and synchronized data acquisition. To leverage additional modality information without such constraints, we propose a novel training framework that consists of self-supervised learning of unpaired multi-spectral images and feature-level adversarial adaptation. In the training stage, we utilize unpaired RGB/thermal video and partially shared network architecture consisting of modality-specific feature extractors and modality-independent decoder. Through the shared network design, the depth decoder can leverage the self-supervised signal of the unpaired RGB images. Feature-level adversarial adaptation minimizes the gap between RGB and thermal features and eventually makes the thermal encoder extract representative and informative features. Based on the proposed method, the trained depth network shows outperformed results than previous state-of-the-art methods.
Single-Image HDR Reconstruction by Multi-Exposure Generation
Authors: Phuoc-Hieu Le; Quynh Le; Rang Nguyen; Binh-Son Hua
Venue: WACV 2023 (main)

Abstract: High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstruction. Recent attempts at single-image HDR reconstruction show a promising alternative: by learning to map pixel values to their irradiance using a neural network, one can bypass the align-and-merge pipeline completely yet still obtain a high-quality HDR image. In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image. Our neural network can invert the camera response to reconstruct pixel irradiance before synthesizing multiple exposures and hallucinating details in under- and over-exposed regions from a single input image. To train the network, we propose a representation loss, a reconstruction loss, and a perceptual loss applied on pairs of under- and over-exposure images and thus do not require HDR images for training. Our experiments show that our proposed model can effectively reconstruct HDR images. Our qualitative and quantitative results show that our method achieves state-of-the-art performance on the DrTMO dataset. Our code is available at https://github.com/VinAIResearch/single_image_hdr.
Scaling Novel Object Detection With Weakly Supervised Detection Transformers
Authors: Tyler LaBonte; Yale Song; Xin Wang; Vibhav Vineet; Neel Joshi
Venue: WACV 2023 (main)

Abstract: A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect. Weakly supervised object detection (WSOD) offers an appealing alternative, where object detectors can be trained using image-level labels. However, the practical application of current WSOD models is limited, as they only operate at small data scales and require multiple rounds of training and refinement. To address this, we propose the Weakly Supervised Detection Transformer, which enables efficient knowledge transfer from a large-scale pretraining dataset to WSOD finetuning on hundreds of novel objects. Additionally, we leverage pretrained knowledge to improve the multiple instance learning (MIL) framework often used in WSOD methods. Our experiments show that our approach outperforms previous state-of-the-art models on large-scale novel object detection datasets, and our scaling study reveals that class quantity is more important than image quantity for WSOD pretraining.
Auxiliary Task-Guided CycleGAN for Black-Box Model Domain Adaptation
Authors: Michael Essich; Markus Rehmann; Cristóbal Curio
Venue: WACV 2023 (main)

Abstract: The research area of domain adaptation investigates methods that enable the transfer of existing models across different domains, e.g., addressing environmental changes or the transfer from synthetic to real data. Especially unsupervised domain adaptation is beneficial because it does not require any labeled target domain data. Usually, existing methods are targeted at specific tasks and require access or even modifications to the source model and its parameters which is a major drawback when only a black-box model is available. Therefore, we propose a CycleGAN-based approach suitable for black-box source models to translate target domain data into the source domain on which the source model can operate. Inspired by multi-task learning, we extend CycleGAN with an additional auxiliary task that can be arbitrarily chosen to support the transfer of task-related information across domains without the need for having access to a differentiable source model or its parameters. In this work, we focus on the regression task of 2D human pose estimation and compare our results in four different domain adaptation settings to CycleGAN and RegDA, a state-of-the-art method for unsupervised domain adaptation for keypoint detection.
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
Authors: Siamul Karim Khan; Patrick Tinsley; Adam Czajka
Venue: WACV 2023 (main)

Abstract: Nonlinear iris texture deformations due to pupil size variations are one of the main factors responsible for within-class variance of genuine comparison scores in iris recognition. In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle, used further in encoding and matching. However, the biological complexity of the iris sphincter and dilator muscles causes the movements of iris features to be nonlinear in a function of pupil size, and not solely organized along radial paths. Alternatively to the existing theoretical models based on the biomechanics of iris musculature, in this paper we propose a novel deep autoencoder-based model that can effectively learn complex movements of iris texture features directly from the data. The proposed model takes two inputs, (a) an ISO-compliant near-infrared iris image with initial pupil size, and (b) the binary mask defining the target shape of the iris. The model makes all the necessary nonlinear deformations to the iris texture to match the shape of the iris in an image (a) with the shape provided by the target mask (b). The identity-preservation component of the loss function helps the model in finding deformations that preserve identity and not only the visual realism of the generated samples. We also demonstrate two immediate applications of this model: better compensation for iris texture deformations in iris recognition algorithms, compared to linear models, and the creation of a generative algorithm that can aid human forensic examiners, who may need to compare iris images with a large difference in pupil dilation. We offer the source codes and model weights available along with this paper.
Task Agnostic and Post-Hoc Unseen Distribution Detection
Authors: Radhika Dua; Seongjun Yang; Yixuan Li; Edward Choi
Venue: WACV 2023 (main)

Abstract: Despite the recent advances in out-of-distribution(OOD) detection, anomaly detection, and uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach. To address this limitation, we design a novel clustering-based ensembling method, called Task Agnostic and Post-hoc Unseen Distribution Detection (TAPUDD) that utilizes the features extracted from the model trained on a specific task. Explicitly, it comprises of TAP-Mahalanobis, which clusters the training datasets' features and determines the minimum Mahalanobis distance of the test sample from all clusters. Further, we propose the Ensembling module that aggregates the computation of iterative TAP-Mahalanobis for a different number of clusters to provide reliable and efficient cluster computation. Through extensive experiments on synthetic and real-world datasets, we observe that our task-agnostic approach can detect unseen samples effectively across diverse tasks and performs better or on-par with the existing task-specific baselines. We also demonstrate that our method is more viable even for large-scale classification tasks.
SeCo: Separating Unknown Musical Visual Sounds With Consistency Guidance
Authors: Xinchi Zhou; Dongzhan Zhou; Wanli Ouyang; Hang Zhou; Di Hu
Venue: WACV 2023 (main)

Abstract: Recent years have witnessed the success of deep learning on the visual sound separation task. However, existing works follow similar settings where the training and testing datasets share the same musical instrument categories, which to some extent limits the versatility of this task. In this work, we focus on a more general and challenging scenario, namely the separation of unknown musical instruments, where the categories in training and testing phases have no overlap with each other. To tackle this new setting, we propose the "Separation-with-Consistency" (SeCo) framework, which can accomplish the separation on unknown categories by exploiting the consistency constraints. Furthermore, to capture richer characteristics of the novel melodies, we devise an online matching strategy, which can bring stable enhancements with no cost of extra parameters. Experiments demonstrate that our SeCo framework exhibits strong adaptation ability on the novel musical categories and outperforms the baseline methods by a notable margin.
More Than Just Attention: Improving Cross-Modal Attentions With Contrastive Constraints for Image-Text Matching
Authors: Yuxiao Chen; Jianbo Yuan; Long Zhao; Tianlang Chen; Rui Luo; Larry Davis; Dimitris N. Metaxas
Venue: WACV 2023 (main)

Abstract: Cross-modal attention mechanisms have been widely applied to the image-text matching task and have achieved remarkable improvements thanks to their capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Re-sourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supervise the training of cross-modal attention models in a contrastive learning manner without requiring explicit attention annotations. They are plug-in training strategies and can be generally integrated into existing cross-modal attention models. Additionally, we introduce three metrics, including Attention Precision, Recall, and F1-Score, to quantitatively measure the quality of learned attention models. We evaluate the proposed constraints by incorporating them into four state-of-the-art cross-modal attention-based image-text matching models. Experimental results on both Flickr30k and MS-COCO datasets demonstrate that integrating these constraints generally improves the model performance in terms of both retrieval performance and attention metrics.
Autoencoder-Based Background Reconstruction and Foreground Segmentation With Background Noise Estimation
Authors: Bruno Sauvalle; Arnaud de La Fortelle
Venue: WACV 2023 (main)

Abstract: Even after decades of research, dynamic scene background reconstruction and foreground object segmentation are still considered as open problems due to various challenges such as illumination changes, camera movements, or background noise caused by air turbulence or moving trees. We propose in this paper to model the background of a frame sequence as a low dimensional manifold using an autoencoder and compare the reconstructed background provided by this autoencoder with the original image to compute the foreground/background segmentation masks. The main novelty of the proposed model is that the autoencoder is also trained to predict the background noise, which allows to compute for each frame a pixel-dependent threshold to perform the foreground segmentation. Although the proposed model does not use any temporal or motion information, it exceeds the state of the art for unsupervised background subtraction on the CDnet 2014 and LASIESTA datasets, with a significant improvement on videos where the camera is moving. It is also able to perform background reconstruction on some non-video image datasets.
Learning Few-Shot Segmentation From Bounding Box Annotations
Authors: Byeolyi Han; Tae-Hyun Oh
Venue: WACV 2023 (main)

Abstract: We present a new weakly-supervised few-shot semantic segmentation setting and a meta-learning method for tackling the new challenge. Different from existing settings, we leverage bounding box annotations as weak supervision signals during the meta-training phase, i.e., more label-efficient. Bounding box provides a cheaper label representation than segmentation mask but contains both an object of interest and a disturbing background. We first show that meta-training with bounding boxes degrades recent few-shot semantic segmentation methods, which are typically meta-trained with full semantic segmentation supervision. We postulate that this challenge is originated from the impure information of bounding box representation. We propose a pseudo trimap estimator and trimap-attention based prototype learning to extract clearer supervision signals from bounding boxes. These developments robustify and generalize our method well to noisy support masks at test time. We empirically show that our method consistently improves performance. Our method gains 1.4% and 3.6% mean-IoU over the competing one in full and weak test supervision cases, respectively, in the 1-way 5-shot setting on Pascal-5i.
AudioViewer: Learning To Visualize Sounds
Authors: Chunjin Song; Yuchi Zhang; Willis Peng; Parmis Mohaghegh; Bastian Wandt; Helge Rhodin
Venue: WACV 2023 (main)

Abstract: A long-standing goal in the field of sensory substitution is enabling sound perception for deaf and hard of hearing (DHH) people by visualizing audio content. Different from existing models that translate to hand sign language, between speech and text, or text and images, we target immediate and low-level audio to video translation that applies to generic environment sounds as well as human speech. Since such a substitution is artificial, without labels for supervised learning, our core contribution is to build a mapping from audio to video that learns from unpaired examples via high-level constraints. For speech, we additionally disentangle content from style, such as gender and dialect. Qualitative and quantitative results, including a human study, demonstrate that our unpaired translation approach maintains important audio features in the generated video and that videos of faces and numbers are well suited for visualizing high-dimensional audio features that can be parsed by humans to match and distinguish between sounds and words. Project website: https://chunjinsong.github.io/audioviewer