GenAI4ELab/papercli-papers huggingface dataset:
Visibility Constrained Wide-Band Illumination Spectrum Design for Seeing-in-the-Dark
Authors: Muyao Niu; Zhuoxiao Li; Zhihang Zhong; Yinqiang Zheng
Venue: CVPR 2023 (main)

Abstract: Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (e.g., low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (e.g., NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://github.com/MyNiuuu/VCSD.
PanelNet: Understanding 360 Indoor Environment via Panel Representation
Authors: Haozheng Yu; Lu He; Bing Jian; Weiwei Feng; Shan Liu
Venue: CVPR 2023 (main)

Abstract: Indoor 360 panoramas have two essential properties. (1) The panoramas are continuous and seamless in the horizontal direction. (2) Gravity plays an important role in indoor environment design. By leveraging these properties, we present PanelNet, a framework that understands indoor environments using a novel panel representation of 360 images. We represent an equirectangular projection (ERP) as consecutive vertical panels with corresponding 3D panel geometry. To reduce the negative impact of panoramic distortion, we incorporate a panel geometry embedding network that encodes both the local and global geometric features of a panel. To capture the geometric context in room design, we introduce Local2Global Transformer, which aggregates local information within a panel and panel-wise global context. It greatly improves the model performance with low training overhead. Our method outperforms existing methods on indoor 360 depth estimation and shows competitive results against state-of-the-art approaches on the task of indoor layout estimation and semantic segmentation.
Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking
Authors: Yuanpeng Tu; Boshen Zhang; Yuxi Li; Liang Liu; Jian Li; Jiangning Zhang; Yabiao Wang; Chengjie Wang; Cai Rong Zhao
Venue: CVPR 2023 (main)

Abstract: Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mislabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of deep models. The proposed method is simple and flexible, it is tested on both synthetic and real-world noisy datasets, where significant improvements are achieved over previous state-of-the-art methods.
PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation
Authors: Qihao Liu; Adam Kortylewski; Alan L. Yuille
Venue: CVPR 2023 (main)

Abstract: Human pose and shape (HPS) estimation methods achieve remarkable results. However, current HPS benchmarks are mostly designed to test models in scenarios that are similar to the training data. This can lead to critical situations in real-world applications when the observed data differs significantly from the training data and hence is out-of-distribution (OOD). It is therefore important to test and improve the OOD robustness of HPS methods. To address this fundamental problem, we develop a simulator that can be controlled in a fine-grained manner using interpretable parameters to explore the manifold of images of human pose, e.g. by varying poses, shapes, and clothes. We introduce a learning-based testing method, termed PoseExaminer, that automatically diagnoses HPS algorithms by searching over the parameter space of human pose images to find the failure modes. Our strategy for exploring this high-dimensional parameter space is a multi-agent reinforcement learning system, in which the agents collaborate to explore different parts of the parameter space. We show that our PoseExaminer discovers a variety of limitations in current state-of-the-art models that are relevant in real-world scenarios but are missed by current benchmarks. For example, it finds large regions of realistic human poses that are not predicted correctly, as well as reduced performance for humans with skinny and corpulent body shapes. In addition, we show that fine-tuning HPS methods by exploiting the failure modes found by PoseExaminer improve their robustness and even their performance on standard benchmarks by a significant margin. The code are available for research purposes.
GamutMLP: A Lightweight MLP for Color Loss Recovery
Authors: Hoang M. Le; Brian Price; Scott Cohen; Michael S. Brown
Venue: CVPR 2023 (main)

Abstract: Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image---the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.
Instance-Aware Domain Generalization for Face Anti-Spoofing
Authors: Qianyu Zhou; Ke-Yue Zhang; Taiping Yao; Xuequan Lu; Ran Yi; Shouhong Ding; Lizhuang Ma
Venue: CVPR 2023 (main)

Abstract: Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at this link: https://github.com/qianyuzqy/IADG.
GANHead: Towards Generative Animatable Neural Head Avatars
Authors: Sijing Wu; Yichao Yan; Yunhao Li; Yuhao Cheng; Wenhan Zhu; Ke Gao; Xiaobo Li; Guangtao Zhai
Venue: CVPR 2023 (main)

Abstract: To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting.
Towards Domain Generalization for Multi-View 3D Object Detection in Bird-Eye-View
Authors: Shuo Wang; Xinhai Zhao; Hai-Ming Xu; Zehui Chen; Dameng Yu; Jiahao Chang; Zhen Yang; Feng Zhao
Venue: CVPR 2023 (main)

Abstract: Multi-view 3D object detection (MV3D-Det) in Bird-Eye-View (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3D object detection have been continuously proposed, most of them may risk drastic performance degradation when the domain of input images differs from that of training. In this paper, we first analyze the causes of the domain gap for the MV3D-Det task. Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2D image's feature representation. To acquire a robust depth prediction, we propose to decouple the depth estimation from the intrinsic parameters of the camera (i.e. the focal length) through converting the prediction of metric depth to that of scale-invariant depth and perform dynamic perspective augmentation to increase the diversity of the extrinsic parameters (i.e. the camera poses) by utilizing homography. Moreover, we modify the focal length values to create multiple pseudo-domains and construct an adversarial training loss to encourage the feature representation to be more domain-agnostic. Without bells and whistles, our approach, namely DG-BEV, successfully alleviates the performance drop on the unseen target domain without impairing the accuracy of the source domain. Extensive experiments on Waymo, nuScenes, and Lyft, demonstrate the generalization and effectiveness of our approach.
Robust and Scalable Gaussian Process Regression and Its Applications
Authors: Yifan Lu; Jiayi Ma; Leyuan Fang; Xin Tian; Junjun Jiang
Venue: CVPR 2023 (main)

Abstract: This paper introduces a robust and scalable Gaussian process regression (GPR) model via variational learning. This enables the application of Gaussian processes to a wide range of real data, which are often large-scale and contaminated by outliers. Towards this end, we employ a mixture likelihood model where outliers are assumed to be sampled from a uniform distribution. We next derive a variational formulation that jointly infers the mode of data, i.e., inlier or outlier, as well as hyperparameters by maximizing a lower bound of the true log marginal likelihood. Compared to previous robust GPR, our formulation approximates the exact posterior distribution. The inducing variable approximation and stochastic variational inference are further introduced to our variational framework, extending our model to large-scale data. We apply our model to two challenging real-world applications, namely feature matching and dense gene expression imputation. Extensive experiments demonstrate the superiority of our model in terms of robustness and speed. Notably, when matching 4k feature points, its inference is completed in milliseconds with almost no false matches. The code is at https://github.com/YifanLu2000/Robust-Scalable-GPR.
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision
Authors: Qi Ming; Lingjuan Miao; Zhe Ma; Lin Zhao; Zhiqiang Zhou; Xuhui Huang; Yuanpei Chen; Yufei Guo
Venue: CVPR 2023 (main)

Abstract: Intersection-over-Union (IoU) is the most popular metric to evaluate regression performance in 3D object detection. Recently, there are also some methods applying IoU to the optimization of 3D bounding box regression. However, we demonstrate through experiments and mathematical proof that the 3D IoU loss suffers from abnormal gradient w.r.t. angular error and object scale, which further leads to slow convergence and suboptimal regression process, respectively. In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression. Specifically, a gradient correction strategy is designed to endow 3D IoU loss with a reasonable gradient. It ensures that the model converges quickly in the early stage of training, and helps to achieve fine-grained refinement of bounding boxes in the later stage. To solve suboptimal regression of 3D IoU loss for objects at different scales, we introduce a gradient rescaling strategy to adaptively optimize the step size. Finally, we integrate GCIoU Loss into multiple models to achieve stable performance gains and faster model convergence. Experiments on KITTI dataset demonstrate superiority of the proposed method. The code is available at https://github.com/ming71/GCIoU-loss.
Doubly Right Object Recognition: A Why Prompt for Visual Rationales
Authors: Chengzhi Mao; Revant Teotia; Amrutha Sundar; Sachit Menon; Junfeng Yang; Xin Wang; Carl Vondrick
Venue: CVPR 2023 (main)

Abstract: Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a "doubly right" object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a "why prompt," which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning
Authors: Jinwoo Kim; Janghyuk Choi; Ho-Jin Choi; Seon Joo Kim
Venue: CVPR 2023 (main)

Abstract: Object-centric learning (OCL) aspires general and com- positional understanding of scenes by representing a scene as a collection of object-centric representations. OCL has also been extended to multi-view image and video datasets to apply various data-driven inductive biases by utilizing geometric or temporal information in the multi-image data. Single-view images carry less information about how to disentangle a given scene than videos or multi-view im- ages do. Hence, owing to the difficulty of applying induc- tive biases, OCL for single-view images still remains chal- lenging, resulting in inconsistent learning of object-centric representation. To this end, we introduce a novel OCL framework for single-view images, SLot Attention via SHep- herding (SLASH), which consists of two simple-yet-effective modules on top of Slot Attention. The new modules, At- tention Refining Kernel (ARK) and Intermediate Point Pre- dictor and Encoder (IPPE), respectively, prevent slots from being distracted by the background noise and indicate lo- cations for slots to focus on to facilitate learning of object- centric representation. We also propose a weak- and semi- supervision approach for OCL, whilst our proposed frame- work can be used without any assistant annotation during the inference. Experiments show that our proposed method enables consistent learning of object-centric representa- tion and achieves strong performance across four datasets. Code is available at https://github.com/object- understanding/SLASH.
High-Fidelity Event-Radiance Recovery via Transient Event Frequency
Authors: Jin Han; Yuta Asano; Boxin Shi; Yinqiang Zheng; Imari Sato
Venue: CVPR 2023 (main)

Abstract: High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action
Authors: Kuan-Chieh Wang; Zhenzhen Weng; Maria Xenochristou; João Pedro Araújo; Jeffrey Gu; Karen Liu; Serena Yeung
Venue: CVPR 2023 (main)

Abstract: The task of reconstructing 3D human motion has wide-ranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware, and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view MoCap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motion-driven animation accessible to the general public. However, the performance of existing HMR methods degrades when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action, and show that NeMo achieves better 3D reconstruction compared to various baselines.
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
Authors: Han Liu; Yuhao Wu; Shixuan Zhai; Bo Yuan; Ning Zhang
Venue: CVPR 2023 (main)

Abstract: The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images. As this technology gains popularity, there is a growing concern about its potential security risks. However, there has been limited exploration into the robustness of these models from an adversarial perspective. Existing research has primarily focused on untargeted settings, and lacks holistic consideration for reliability (attack success rate) and stealthiness (imperceptibility). In this paper, we propose RIATIG, a reliable and imperceptible adversarial attack against text-to-image models via inconspicuous examples. By formulating the example crafting as an optimization process and solving it using a genetic-based method, our proposed attack can generate imperceptible prompts for text-to-image generation models in a reliable way. Evaluation of six popular text-to-image generation models demonstrates the efficiency and stealthiness of our attack in both white-box and black-box settings. To allow the community to build on top of our findings, we've made the artifacts available.
Distilling Neural Fields for Real-Time Articulated Shape Reconstruction
Authors: Jeff Tan; Gengshan Yang; Deva Ramanan
Venue: CVPR 2023 (main)

Abstract: We present a method for reconstructing articulated 3D models from videos in real-time, without test-time optimization or manual 3D supervision at training time. Prior work often relies on pre-built deformable models (e.g. SMAL/SMPL), or slow per-scene optimization through differentiable rendering (e.g. dynamic NeRFs). Such methods fail to support arbitrary object categories, or are unsuitable for real-time applications. To address the challenge of collecting large-scale 3D training data for arbitrary deformable object categories, our key insight is to use off-the-shelf video-based dynamic NeRFs as 3D supervision to train a fast feed-forward network, turning 3D shape and motion prediction into a supervised distillation task. Our temporal-aware network uses articulated bones and blend skinning to represent arbitrary deformations, and is self-supervised on video datasets without requiring 3D shapes or viewpoints as input. Through distillation, our network learns to 3D-reconstruct unseen articulated objects at interactive frame rates. Our method yields higher-fidelity 3D reconstructions than prior real-time methods for animals, with the ability to render realistic images at novel viewpoints and poses.