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Dong_Fast_Monocular_Scene_Reconstruction_With_Global-Sparse_Local-Dense_Grids_CVPR_2023 | Abstract Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representa-tions and monocular priors have led to remarkable re-sults in scene-level surface reconstructions. The reliance on Multilayer Perceptrons (MLP), ... | 1. Introduction Reconstructing indoor spaces into 3D representations is a key requirement for many real-world applications, includ-ing robot navigation, immersive virtual/augmented reality experiences, and architectural design. Particularly useful is reconstruction from monocular cameras which are the most prevalent an... |
Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023 | Abstract Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision appli-cations. We propose a physics-guided material classifica-tion framework that relies on thermal properties of the ob-ject. Our key observation is that the rate of heating and cooling of an object ... | 1. Introduction Material classification is an important task pertinent to a diverse set of fields including but not limited to medicine and biology [1], chip manufacturing, recycling [2, 3], land and weather monitoring using satellites, and vision and robotics. Robust material classification is particularly crit-ical i... |
Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023 | Abstract We present ESLAM, an efficient implicit neural represen-tation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown cam-era poses in a sequential manner and incrementally recon-structs the scene representation while estimating the cur-rent camera position in the scene.... | 1. Introduction Dense visual Simultaneous Localization and Mapping (SLAM) is a fundamental challenge in 3D computer vi-sion with several applications such as autonomous driving, robotics, and virtual/augmented reality. It is defined as con-structing a 3D map of an unknown environment while si-multaneously approximating... |
Gan_CNVid-3.5M_Build_Filter_and_Pre-Train_the_Large-Scale_Public_Chinese_Video-Text_CVPR_2023 | Abstract Owing to well-designed large-scale video-text datasets, recent years have witnessed tremendous progress in video-text pre-training. However, existing large-scale video-text datasets are mostly English-only. Though there are certain methods studying the Chinese video-text pre-training, they pre-train their mode... | 1. Introduction Owing to well-designed large-scale datasets, video-text pre-training [15, 17, 19] has achieved superior performance in various downstream tasks, such as video-text retrieval [4, 10, 36], video question answering [27, 34, 42], and video captioning [1, 22, 30]. However, recent large-scale video-*Equal con... |
Chen_iQuery_Instruments_As_Queries_for_Audio-Visual_Sound_Separation_CVPR_2023 | Abstract Current audio-visual separation methods share a stan-dard architecture design where an audio encoder-decoder network is fused with visual encoding features at the en-coder bottleneck. This design confounds the learning of multi-modal feature encoding with robust sound decod-ing for audio separation. To general... | 1. Introduction Humans use multi-modal perception to understand com-plex activities. To mimic this skill, researchers have studied audio-visual learning [3, 17, 33] by exploiting the synchro-nization and correlation between auditory and visual infor-mation. In this paper, we focus on the sound source sepa-ration task, ... |
Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023 | Abstract Weakly-supervised Video Anomaly Detection is the task of detecting frame-level anomalies using video-level labeled training data. It is difficult to explore class representative features using minimal supervision of weak labels with asingle backbone branch. Furthermore, in real-world sce-narios, the boundary be... | 1. Introduction Video anomaly detection (V AD) in surveillance systems refers to the identification of undefined, unusual, or unseenabnormal events (e.g., traffic accidents, robberies, and otherunforeseeable events) from amongst normal situations withtemporal intervals. Currently, numerous CCTVs installedin public places ... |
Chang_Depth_Estimation_From_Indoor_Panoramas_With_Neural_Scene_Representation_CVPR_2023 | Abstract Depth estimation from indoor panoramas is challenging due to the equirectangular distortions of panoramas and inaccurate matching. In this paper, we propose a prac-tical framework to improve the accuracy and efficiency of depth estimation from multi-view indoor panoramic images with the Neural Radiance Field t... | 1. Introduction Panoramic imaging has emerged as an attractive imag-ing technique in many fields, such as computer visionand robotics. Different from traditional imaging devices, panoramic cameras capture a holistic scene and present it as a 2D image with equirectangular projection. Indoor panora-mas, captured in the i... |
Cai_MARLIN_Masked_Autoencoder_for_Facial_Video_Representation_LearnINg_CVPR_2023 | Abstract This paper proposes a self-supervised approach to learn universal facial representations from videos, that can trans-fer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchroniza-tion (LS). Our propos... | 1. Introduction Facial analysis tasks [34, 43, 70, 85] provide essential cues for human non-verbal behavior analysis, and help un-fold meaningful insights regarding social interaction [36], communication [40], cognition [68] with potential appli-cations in Human-Computer Interaction (HCI) and Affec-tive Computing domai... |
Dong_The_Enemy_of_My_Enemy_Is_My_Friend_Exploring_Inverse_CVPR_2023 | Abstract Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversar-ial training and its variants have been shown to be the most effective approaches to defend against adversarial exam-ples. A particular c... | 1. Introduction Deep learning has achieved revolutionary progress in nu-merous computer vision tasks [24, 40, 55] and has emerged as a promising technique for fundamental research in mul-tiple disciplines [31, 35, 52]. However, a well-established study has demonstrated that Deep Neural Networks (DNNs) are extremely vul... |
Chen_Boundary_Unlearning_Rapid_Forgetting_of_Deep_Networks_via_Shifting_the_CVPR_2023 | Abstract The practical needs of the “right to be forgotten” and poisoned data removal call for efficient machine unlearn-ing techniques, which enable machine learning models to unlearn, or to forget a fraction of training data and its lin-eage. Recent studies on machine unlearning for deep neural networks (DNNs) attempt... | 1. Introduction Suppose a company trains a face recognition model with your photos and deploys it as an opened API. Your photos could be stolen or inferenced by attackers via model inver-sion attack [6,18]. With the increasing awareness of protect-ing user’s privacy, a lot of privacy regulations take effect to This wor... |
Hui_Bridging_Search_Region_Interaction_With_Template_for_RGB-T_Tracking_CVPR_2023 | Abstract RGB-T tracking aims to leverage the mutual enhance-ment and complement ability of RGB and TIR modalities for improving the tracking process in various scenarios, where cross-modal interaction is the key component. Some previ-ous methods concatenate the RGB and TIR search region features directly to perform a c... | 1. Introduction Given the initial state of a single target object in the first frame, the goal of single object tracking (SOT) is to local-ize the target object in successive frames. As a fundamen-tal task in the computer vision community, SOT has drawn the great attention of researchers. However, current SOT methods b... |
Girdhar_ImageBind_One_Embedding_Space_To_Bind_Them_All_CVPR_2023 | Abstract We present IMAGE BIND, an approach to learn a joint embedding across six different modalities -images, text, au-dio, depth, thermal, and IMU data. We show that all combi-nations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities tog... | gs are not directly applicable for text-based tasks while (image, text) embeddings cannot be applied for audio tasks. Zero-shot image classification using text prompts. CLIP [59] popularized a ‘zero-shot’ classification task based on an aligned (image, text) embedding space. This involves constructing a list of text de... |
Cai_Orthogonal_Annotation_Benefits_Barely-Supervised_Medical_Image_Segmentation_CVPR_2023 | Abstract Recent trends in semi-supervised learning have signifi-cantly boosted the performance of 3D semi-supervised med-ical image segmentation. Compared with 2D images, 3D medical volumes involve information from different direc-tions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complem... | 1. Introduction Medical image segmentation is one of the most critical vision tasks in medical image analysis field. Thanks to the development of deep learning-based methods [8,11,28,32], segmentation performance has now been substantially im-proved. However, the current promising performance is at *Corresponding autho... |
Guo_Knowledge_Distillation_for_6D_Pose_Estimation_by_Aligning_Distributions_of_CVPR_2023 | Abstract Knowledge distillation facilitates the training of a com-pact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose esti-mation. In this work, we introduce the first knowledge dis-tillation method driven ... | 1. Introduction Estimating the 3D position and 3D orientation, a.k.a. 6D pose, of an object relative to the camera from a single 2D image has a longstanding history in computer vision, with many real-world applications, such as robotics, autonomous navigation, and virtual and augmented reality. Modern methods that tack... |
Cao_Three_Guidelines_You_Should_Know_for_Universally_Slimmable_Self-Supervised_Learning_CVPR_2023 | Abstract We propose universally slimmable self-supervised learn-ing (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across dif-ferent devices. We observe that direct adaptation of self-supervised learning (SSL) to universally slimmable networks misbehaves as the tr... | 1. Introduction Deep supervised learning has achieved great success in the last decade, but the drawback is that it relies heavily on a large set of annotated training data. Self-supervised learning (SSL) has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. Since the emerge... |
Fan_PointListNet_Deep_Learning_on_3D_Point_Lists_CVPR_2023 | Abstract Deep neural networks on regular 1D lists ( e.g., natural languages) and irregular 3D sets ( e.g., point clouds) have made tremendous achievements. The key to natural lan-guage processing is to model words and their regular or-der dependency in texts. For point cloud understanding, the challenge is to understan... | 1. Introduction The essence of deep learning is to capture the structure of a certain kind of data via artificial neural networks. Usu-ally, an element of data includes a position part and a feature part. According to the type of element position, data exhibit different structures. Various deep neural networks are pro-... |
Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023 | Abstract In the field of out-of-distribution (OOD) detection, a pre-vious method that use auxiliary data as OOD data has shown promising performance. However, the method pro-vides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a gene... | 1. Introduction Deep neural networks are used in a variety of fields such as image classification [22] and semantic segmenta-tion [11]. However, there is a challenge in the practical use of deep neural networks in areas where safety is crucial, such as autonomous driving and medical diagnosis [20,25]. In particular, de... |
Deitke_Phone2Proc_Bringing_Robust_Robots_Into_Our_Chaotic_World_CVPR_2023 | Abstract Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world envi-ronments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan ... | 1. Introduction The embodied AI research community has increasingly relied on visual simulators [ 30,49,61] to train embodied agents, with the expectation that the resulting policies can be transferred onto robots in the physical world. While agents trained within simulated environments have shown increased capabilitie... |
Girase_Latency_Matters_Real-Time_Action_Forecasting_Transformer_CVPR_2023 | Abstract We present RAFTformer, a real-time action forecasting transformer for latency-aware real-world action forecast-ing. RAFTformer is a two-stage fully transformer based architecture comprising of a video transformer backbone that operates on high resolution, short-range clips, and a head transformer encoder that ... | 1. Introduction Latency matters. It is a crucial system design consid-eration for countless applications that operate in real-time from hardware design [65], network engineering [63], and satellite communications [30] to capital trading [32], human vision [59] and COVID transmission patterns [54]. How-ever, it has not ... |
Ashutosh_HierVL_Learning_Hierarchical_Video-Language_Embeddings_CVPR_2023 | Abstract Video-language embeddings are a promising avenue for injecting semantics into visual representations, but exist-ing methods capture only short-term associations between seconds-long video clips and their accompanying text. We propose HierVL, a novel hierarchical video-language em-bedding that simultaneously ac... | 1. Introduction Understanding human activity in video is a fundamental vision problem with abundant applications in augmented re-ality, robotics, and information retrieval. The field has made exciting advances, from new models for recognition [24, 53, 86] and self-supervised representations [55, 58, 61, 90] to major da... |
Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023 | Abstract Existing studies have empirically observed that the reso-lution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little under-standing is given to explain it. In this paper, we try to give a feasible an... | 1. Introduction Image super resolution aims to restore a high-resolution (HR) image from a low-resolution (LR) one, which is an important technique in image processing [13,26,27,52] and computer vision [7,14,18,45,51]. In the past decades, plen-tiful SR methods have been proposed [19, 53], and applied to a wide range o... |
Hong_Watch_or_Listen_Robust_Audio-Visual_Speech_Recognition_With_Visual_Corruption_CVPR_2023 | Abstract This paper deals with Audio-Visual Speech Recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previ-ous studies have focused on how to complement the cor-rupted audio inputs with th... | 1. Introduction Imagine you are watching the news on Youtube. Whether the recording microphone is a problem or the video encoding is wrong, the anchor’s voice keeps breaking off, so you cannot hear well. You try to understand her by her lip motions, but making matters worse, the microphone keeps covering her mouth, so ... |
Gao_VisFusion_Visibility-Aware_Online_3D_Scene_Reconstruction_From_Videos_CVPR_2023 | Abstract We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction meth-ods which aggregate features for each voxel from input views without considering its visibil... | 1. Introduction 3D scene reconstruction from RGB videos is a critical task in 3D computer vision, which finds its broad appli-cations in augmented reality (AR), robot navigation and human-robot interaction. These applications require accu-rate, complete and real-time 3D reconstruction of scenes. While state-of-the-art ... |
Huang_Feature_Shrinkage_Pyramid_for_Camouflaged_Object_Detection_With_Transformers_CVPR_2023 | Abstract Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detec-tion. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggre-gation in decoders, which are not conducive to camou-flaged object detection tha... | 1. Introduction Camouflage is a common defense or tactic in organ-isms that “perfectly” blend in with their surroundings to deceive predators (prey) or sneak up on prey (hunters). Camouflaged object detection (COD) [11] aims to segment camouflaged objects in the scene and has been widely ap-†Equal contributions. *Corre... |
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