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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... |
Ge_Improving_Zero-Shot_Generalization_and_Robustness_of_Multi-Modal_Models_CVPR_2023 | Abstract Multi-modal image-text models such as CLIP and LiT have demonstrated impressive performance on image clas-sification benchmarks and their zero-shot generalization ability is particularly exciting. While the top-5 zero-shot accuracies of these models are very high, the top-1 accu-racies are much lower (over 25%... | 1. Introduction Vision-language multi-modal models trained on large-scale data have achieved significant success in numerous domains and have demonstrated excellent zero-shot gener-alization ability [7, 12, 18, 19, 20, 28]. Given a test image and a set of candidate class labels, one can compute the similarity between t... |
Guo_Improving_Robustness_of_Vision_Transformers_by_Reducing_Sensitivity_To_Patch_CVPR_2023 | Abstract Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. In-deed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensi-tive to the corruptio... | 1. Introduction Despite the success of vision transformers [10] in recent years, they still lack robustness against common image cor-ruptions [24, 52], such as noise or blur, and adversarial per-turbations [13, 15, 42]. For example, even for the state-of-the-art robust architectures, e.g., RVT [34] and FAN [61], the ac... |
He_MSF_Motion-Guided_Sequential_Fusion_for_Efficient_3D_Object_Detection_From_CVPR_2023 | Abstract Point cloud sequences are commonly used to accurately detect 3D objects in applications such as autonomous driv-ing. Current top-performing multi-frame detectors mostly follow a Detect-and-Fuse framework, which extracts fea-tures from each frame of the sequence and fuses them to detect the objects in the curre... | 1. Introduction 3D object detection [1, 2, 6, 7, 9, 14, 21, 27–29, 36] is one of the key technologies in autonomous driving, which helps the vehicle to better understand the surrounding environ-ment and make critical decisions in the downstream tasks. As an indispensable sensing device in autonomous driving systems, Li... |
Bernasconi_Kernel_Aware_Resampler_CVPR_2023 | Abstract Deep learning based methods for super-resolution have become state-of-the-art and outperform traditional ap-proaches by a significant margin. From the initial mod-els designed for fixed integer scaling factors (e.g. ×2 or ×4), efforts were made to explore different directions such as modeling blur kernels or add... | 1. Introduction Thanks to recent advances in deep learning based super-resolution which allow to infer impressive high frequency details from low resolution inputs, it has become possible to bridge the gap between content and display resolution with-out noticeable degradation in quality. This is beneficial in different ... |
Huang_Not_All_Image_Regions_Matter_Masked_Vector_Quantization_for_Autoregressive_CVPR_2023 | Abstract Existing autoregressive models follow the two-stage gen-eration paradigm that first learns a codebook in the la-tent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region informat... | 1. Introduction Deep generative models of images have received signif-icant improvements over the past few years and broadly fall into two categories: likelihood-based models, which include V AEs [24], flow-based [36], diffusion models [17] and autoregressive models [40], and generative adversarial *Zhendong Mao is the... |
Cho_Transformer-Based_Unified_Recognition_of_Two_Hands_Manipulating_Objects_CVPR_2023 | Abstract Understanding the hand-object interactions from an egocentric video has received a great attention recently. So far , most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to prov... | 1. Introduction Estimating poses and actions of an egocentric video involving two hands and an object is an important factor of various appli-cations such as augmented reality (AR), virtual reality (VR) and human computer interaction (HCI). Previously, there has been much progress in the hand pose estimation [3 –5,11,1... |
Ando_RangeViT_Towards_Vision_Transformers_for_3D_Semantic_Segmentation_in_Autonomous_CVPR_2023 | Abstract Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-t... | 1. Introduction Semantic segmentation of LiDAR point clouds permits vehicles to perceive their surrounding 3D environment in-*This project was done during an internship at Valeo.ai. RGB Images Point Clouds Stem Decoder ViT Encoder LiDAR Segmentation Copying RangeViT Pre-training Fine-tuning ViT Encoder Image classifica... |
Azinovic_High-Res_Facial_Appearance_Capture_From_Polarized_Smartphone_Images_CVPR_2023 | Abstract We propose a novel method for high-quality facial tex-ture reconstruction from RGB images using a novel captur-ing routine based on a single smartphone which we equip with an inexpensive polarization foil. Specifically, we turn the flashlight into a polarized light source and add a polar-ization filter on top ... | 1. Introduction In recent years, we have seen tremendous advances in the development of virtual and mixed reality devices. At the same time, the commercial availability of such hardware has led to a massive interest in the creation of ’digital hu-man’ assets and photo-realistic renderings of human faces. In particular,... |
Guo_Class_Attention_Transfer_Based_Knowledge_Distillation_CVPR_2023 | Abstract Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they trans-ferred helps to improve the performance of the student net-work. In this work, we focus on proposing a knowledge distillation method that has b... | 1. Introduction Knowledge distillation (KD) transfers knowledge dis-tilled from the bigger teacher network to the smaller student network, aiming to improve the performance of the student network. Depending on the type of the transferred knowl-edge, previous KD methods can be divided into three cat-egories: based on tr... |
Chowdhury_What_Can_Human_Sketches_Do_for_Object_Detection_CVPR_2023 | Abstract Sketches are highly expressive, inherently capturing sub-jective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been lim-ited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundament... | 1. Introduction Sketches have been used from prehistoric times for hu-mans to express and record ideas [35, 76]. The level of ex-pressiveness [28, 41] they carry remains unparalleled today even in the face of language [14, 82] – recall that moment that you want to resort to pen and paper (or Zoom White-Object Detector ... |
Ci_UniHCP_A_Unified_Model_for_Human-Centric_Perceptions_CVPR_2023 | Abstract Human-centric perceptions (e.g., pose estimation, hu-man parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual mod-els. While specific human-centric tasks have their own rel-evant semantic aspect to focus on, they also share the same underlying sema... | 1. Introduction Research on human-centric perceptions has come a long way with tremendous advancements in recent years. Many methods have been developed to enhance the performance of pose estimation [9, 25, 60, 91], pedestrian detection [4, 62, 63, 76], person re-identification [42, 86, 101] (ReID), and many other huma... |
Chen_VoxelNeXt_Fully_Sparse_VoxelNet_for_3D_Object_Detection_and_Tracking_CVPR_2023 | Abstract 3D object detectors usually rely on hand-crafted prox-ies,e.g., anchors or centers, and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be densified and processed by dense prediction heads, which inevitably costs extra computation. In this paper, we in-stead propose VoxelNext fo... | 1. Introduction 3D perception is a fundamental component in au-tonomous driving systems. 3D detection networks take sparse point clouds or voxels as input, and localize and cat-egorize 3D objects. Recent 3D object detectors [40, 49, 57] usually apply sparse convolutional networks (Sparse CNNs) [53] for feature extracti... |
Guo_Zero-Shot_Generative_Model_Adaptation_via_Image-Specific_Prompt_Learning_CVPR_2023 | Abstract Recently, CLIP-guided image synthesis has shown ap-pealing performance on adapting a pre-trained source-domain generator to an unseen target domain. It does not require any target-domain samples but only the textual do-main labels. The training is highly efficient, e.g., a few minutes. However, existing method... | 1. Introduction In recent years, image synthesis using generative adver-sarial networks (GANs) [11] has been rapidly developed. This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the p... |
Iofinova_Bias_in_Pruned_Vision_Models_In-Depth_Analysis_and_Countermeasures_CVPR_2023 | Abstract Pruning—that is, setting a significant subset of the pa-rameters of a neural network to zero—is one of the most popular methods of model compression. Yet, several recent works have raised the issue that pruning may induce or ex-acerbate biasin the output of the compressed model. De-spite existing evidence for t... | 1. Introduction The concept of “bias” in machine learning models spans a range of considerations in terms of statistical, perfor-mance, and social metrics. Different definitions can lead to different relationships between bias and accuracy. For instance, if bias is defined in terms of accuracy disparity between identity ... |
He_Compositor_Bottom-Up_Clustering_and_Compositing_for_Robust_Part_and_Object_CVPR_2023 | Abstract In this work, we present a robust approach for joint part and object segmentation. Specifically, we reformulate ob-ject and part segmentation as an optimization problem and build a hierarchical feature representation including pixel, part, and object-level embeddings to solve it in a bottom-up clustering manne... | 1. Introduction Detecting objects and parsing them into semantic parts is a fundamental ability of human visual system. When view-ing images, humans not only detect, segment, and classify objects but also segment their semantic parts and identify them. This gives a hierarchical representation that enables a detailed an... |
Bao_All_Are_Worth_Words_A_ViT_Backbone_for_Diffusion_Models_CVPR_2023 | Abstract Vision transformers (ViT) have shown promise in vari-ous vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion mod-els. We design a simple and general ViT-based architecture (named U-ViT) for image generation with diffusion mod-els. U-ViT is characterized by t... | 1. Introduction Diffusion models [24, 56, 61] are powerful deep gener-ative models that emerge recently for high quality image generation [12, 25, 49]. They grow rapidly and find ap-plications in text-to-image generation [47, 49, 51], image-to-image generation [10, 42, 74], video generation [23, 27], *Corresponding to ... |
An_ZBS_Zero-Shot_Background_Subtraction_via_Instance-Level_Background_Modeling_and_Foreground_CVPR_2023 | Abstract Background subtraction (BGS) aims to extract all mov-ing objects in the video frames to obtain binary foreground segmentation masks. Deep learning has been widely used in this field. Compared with supervised-based BGS methods, unsupervised methods have better generalization. However, previous unsupervised deep... | 1. Introduction Background subtraction (BGS) is a fundamental task in computer vision applications [7], such as autonomous navigation, visual surveillance, human activity recognition, etc[15]. BGS aims to extract all moving objects as fore-ground in each video frame and outputs binary segmenta-*Corresponding Author Tra... |
Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023 | Abstract Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sketches – that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More specifically, we... | 1. Introduction As any reasonable drawing lesson would have taught you – sketching is an attentive process [24]. This paper sets out to prove just that but in the context of computer vi-sion. In particular, we show that attention information in-herently embedded in sketches can be cultivated to learn image saliency [30... |
Gao_ULIP_Learning_a_Unified_Representation_of_Language_Images_and_Point_CVPR_2023 | Abstract The recognition capabilities of current state-of-the-art 3D models are limited by datasets with a small number of annotated data and a pre-defined set of categories. In its 2D counterpart, recent advances have shown that simi-lar problems can be significantly alleviated by employing knowledge from other modali... | he augmented point cloud Pias input and outputs its 3D representation hP i via hP i=fP(Pi), (1) where fP(·)represents the 3D backbone encoder. Multi-view Image Rendering . ShapeNet55 CAD models do not come with images. To obtain images that semanti-cally align well with each CAD model, we synthesize multi-view images o... |
Bai_Sliced_Optimal_Partial_Transport_CVPR_2023 | Abstract Optimal transport (OT) has become exceedingly popu-lar in machine learning, data science, and computer vision. The core assumption in the OT problem is the equal to-tal amount of mass in source and target measures, which limits its application. Optimal Partial Transport (OPT) is a recently proposed solution to... | 1. Introduction The Optimal Transport (OT) problem studies how to find the most cost-efficient way to transport one probabil-ity measure to another, and it gives rise to popular prob-ability metrics like the Wasserstein distance. OT has at-tracted abundant attention in data science, statistics, ma-chine learning, signal ... |
Huang_Siamese_DETR_CVPR_2023 | Abstract Recent self-supervised methods are mainly designed for representation learning with the base model, e.g., ResNets or ViTs. They cannot be easily transferred to DETR, with task-specific Transformer modules. In this work, we present Siamese DETR , aSiamese self-supervised pretraining approach for the Transformer... | 1. Introduction Object detection with Transformers (DETR) [3] combines convolutional neural networks (CNNs) and Transformer-based encoder-decoders, viewing object detection as an end-to-end set prediction problem. Despite its impressive performance, DETR and its variants still rely on large-scale, high-quality training... |
Bao_SINE_Semantic-Driven_Image-Based_NeRF_Editing_With_Prior-Guided_Editing_Field_CVPR_2023 | Abstract Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still lim-ited, either relying on 3D modeling skills or allowing edit-ing within only a few categories. In this paper, we present a novel semantic-d... | 1. Introduction Semantic-driven editing approaches, such as stroke-based scene editing [34, 39, 66], text-driven image synthe-sis and editing [1, 50, 53], and attribute-based face edit-ing [27, 60], have greatly improved the ease of artistic cre-ation. However, despite the great success of 2D image edit-*Authors contri... |
Feng_NVTC_Nonlinear_Vector_Transform_Coding_CVPR_2023 | Abstract In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of ... | 1. Introduction Recent works based on nonlinear transform coding (NTC) [5] have achieved remarkable success in neural im-age compression [12, 34]. Unlike these traditional image codecs that employ linear transform such as discrete co-sine transform (DCT), NTC is constructed with the nonlin-ear transform layers and opti... |
Akula_MetaCLUE_Towards_Comprehensive_Visual_Metaphors_Research_CVPR_2023 | Abstract Creativity is an indispensable part of human cognition and also an inherent part of how we make sense of the world. Metaphorical abstraction is fundamental in commu-nicating creative ideas through nuanced relationships be-tween abstract concepts such as feelings. While computer vision benchmarks and approaches... | 1. Introduction “Metaphor is pervasive in everyday life ... Our ordinary conceptual system, in terms of which we both think and act, is fundamentally metaphorical in nature. ” — Lakoff & Johnson [25] Creativity is a process of generating a new perspective on a problem or a situation. Metaphorical thinking has been reco... |
Fang_You_Can_Ground_Earlier_Than_See_An_Effective_and_Efficient_CVPR_2023 | AbstractGiven an untrimmed video, temporal sentence ground-ing (TSG) aims to locate a target moment semantically ac-cording to a sentence query. Although previous respectableworks have made decent success, they only focus on high-level visual features extracted from the consecutive de-coded frames and fail to handle th... | SG task, called compressed-domain TSL, with merely compressed video rather than adecompressed frame sequence. 2449 Video compression.As a fundamental computer visiontask, video compression [26,27,32,48,57,72,75] dividesa video into a group of pictures (GOP), where each frameis coded as an I-, P-, and B-frame. An I-fram... |
Jeon_Genie_Show_Me_the_Data_for_Quantization_CVPR_2023 | Abstract Zero-shot quantization is a promising approach for de-veloping lightweight deep neural networks when data is in-accessible owing to various reasons, including cost and is-sues related to privacy. By exploiting the learned parame-ters (µandσ) of batch normalization layers in an FP32-pre-trained model, zero-shot... | 1. Introduction Quantization is an indispensable procedure for deploy-ing models in resource-constrained devices such as mobile phones. By representing tensors using a lower bit width and maintaining a dense format of tensors, quantization reduces a computing unit to a significantly smaller size compared to that achiev... |
Fang_EVA_Exploring_the_Limits_of_Masked_Visual_Representation_Learning_at_CVPR_2023 | Abstract We launch EVA , a vision-centric foundation model to Explore the limits of Visual representation at sc Ale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can e... | 1. Introduction Scaling up pre-trained language models (PLMs) [9,64,76] has revolutionized natural language processing (NLP) in the past few years. The key to this success lies in the simple and scalable self-supervised learning task of masked signal †Interns at Beijing Academy of Artificial Intelligence (BAAI). ‡Corre... |
Ai_HRDFuse_Monocular_360deg_Depth_Estimation_by_Collaboratively_Learning_Holistic-With-Regional_Depth_CVPR_2023 | Abstract Depth estimation from a monocular 360◦image is a bur-geoning problem owing to its holistic sensing of a scene. Recently, some methods, e.g., OmniFusion, have applied the tangent projection (TP) to represent a 360◦image and predicted depth values via patch-wise regressions, which are merged to get a depth map w... | 1. Introduction The 360◦camera is becoming increasingly popular as a 360◦image provides holistic sensing of a scene with a wide *Corresponding author (e-mail: linwang@ust.hk) Figure 1. (a) Our HRDFuse employs the SFA module to align the regional information in discrete TP patches and holistic informa-tion in a complete... |
Christen_Learning_Human-to-Robot_Handovers_From_Point_Clouds_CVPR_2023 | Abstract We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has made significant progress in training robot agents in simulated environments, interacting with humans remains challenging due to the... | 1. Introduction Handing over objects between humans and robots is an important tasks for human-robot interaction (HRI) [35]. It *This work was done during an internship at NVIDIA.allows robots to assist humans in daily collaborative activi-ties, such as helping to prepare a meal, or to exchange tools and parts with hum... |
Choudhuri_Context-Aware_Relative_Object_Queries_To_Unify_Video_Instance_and_Panoptic_CVPR_2023 | Abstract Object queries have emerged as a powerful abstraction to generically represent object proposals. However, their use for temporal tasks like video segmentation poses two questions: 1) How to process frames sequentially and prop-agate object queries seamlessly across frames. Using inde-pendent object queries per... | 1. Introduction Video instance segmentation (VIS) [56] and Multi-Object Tracking and Segmentation (MOTS) combines segmentation and tracking of object instances across frames of a given video, whereas video panoptic segmentation (VPS) requires to also pixel-wise categorize the entire video semantically. These are challe... |
Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023 | Abstract Federated learning enables the privacy-preserving train-ing of neural network models using real-world data across distributed clients. FedAvg has become the preferred opti-mizer for federated learning because of its simplicity and effectiveness. FedAvg uses naïve aggregation to update the server model, interpo... | 1. Introduction Unlike traditional centralized learning in which models are trained using large datasets stored in a central server [15], federated learning -first proposed in [40] -leverages data spread across many clients to learn classification tasks dis-*Equal contribution. †Corresponding author. This work is suppo... |
Eisenberger_G-MSM_Unsupervised_Multi-Shape_Matching_With_Graph-Based_Affinity_Priors_CVPR_2023 | Abstract We present G-MSM ( Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as an unordered set of samples, we explicitly model the underlying shape data manifold. To this end, we propose an adaptive multi-sha... | 1. Introduction Shape matching of non-rigid object categories is a central problem in 3D computer vision and graphics that has been studied extensively over the last few years. Especially in recent times, there is a growing demand for such algorithms as 3D reconstruction techniques and affordable scanning devices becom... |
Gumeli_ObjectMatch_Robust_Registration_Using_Canonical_Object_Correspondences_CVPR_2023 | Abstract We present ObjectMatch1, a semantic and object-centric camera pose estimator for RGB-D SLAM pipelines. Mod-ern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they can-not align camera frames with little or no overlap. In this work, we propose to leverage i... | bject poses, we use NOC correspondences directly in a multi-frame, global camera, and object pose optimization. 3. Method 3.1. Problem Setup Given KRGB-D frames {(Ic 1, Id 1), ...,(Ic K, Id K)}, we aim to optimize their 6-DoF camera poses Tc= {T2, ..., T K}, assuming the first frame is the reference, i.e., T1=I. A 6-Do... |
Bai_High-Fidelity_Facial_Avatar_Reconstruction_From_Monocular_Video_With_Generative_Priors_CVPR_2023 | Abstract High-fidelity facial avatar reconstruction from a monoc-ular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF) has shown impressive novel view rendering results and has been considered for facial avatar recon-struction. However, the complex... | 1. Introduction Reconstructing high-fidelity controllable 3D faces from a monocular video is significant in computer graphics and computer vision and has great potential in digital human, video conferencing, and AR/VR applications. Yet it is very challenging due to the complex facial dynamics and missing 3D information... |
Chen_Mixed_Autoencoder_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023 | Abstract Masked Autoencoder (MAE) has demonstrated superior performance on various vision tasks via randomly masking image patches and reconstruction. However, effective data augmentation strategies for MAE still remain open ques-tions, different from those in contrastive learning that serve as the most important part.... | 1. Introduction Self-supervised learning (SSL) has become one of the most popular pre-training paradigm due to its independence of human annotation. Previous literature mainly focuses on the handcrafted pretext task design [ 13,19,36] and instance discrimination [ 6,10], while with the development of Vision Transformer... |
Choi_Restoration_of_Hand-Drawn_Architectural_Drawings_Using_Latent_Space_Mapping_With_CVPR_2023 | Abstract This work presents the restoration of drawings of wooden built heritage. Hand-drawn drawings contain the most important original information but are often severely degraded over time. A novel restoration method based on the vector quantized variational autoencoders is presented. Latent space representations of... | 1. Introduction Cultural heritage is a valuable asset of humanity that requires our efforts to preserve archaeological, historical, cultural, and technological values. In particular, traditional wooden buildings are vulnerable to deformation, earth-quakes, and fires. We continuously collect and manage ar-chitectural dr... |
Cazenavette_Generalizing_Dataset_Distillation_via_Deep_Generative_Prior_CVPR_2023 | Abstract Dataset Distillation aims to distill an entire dataset’s knowledge into a few synthetic images. The idea is to synthe-size a small number of synthetic data points that, when given to a learning algorithm as training data, result in a model approximating one trained on the original data. Despite a recent upsurg... | 1. Introduction Many recent advancements in machine learning come from combining large networks and big data. Such trained models have shown strong capabilities to perform a wide range of diverse tasks [ 12,22,48] and are considered bysome as an ongoing paradigm shift [ 9]. While such ap-proaches show great potential t... |
Cong_Learning_To_Dub_Movies_via_Hierarchical_Prosody_Models_CVPR_2023 | Abstract Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker’s emotion presented in the video using the de-sired speaker voice as reference. V2C is more challeng-ing than conventional text-to-speech ta... | 1. Introduction Movie dubbing, also known as visual voice clone (V2C) [9], aims to convert a paragraph of text to a speech with both desired voice specified by reference audio and de-sired emotion and speed presented in the reference video as shown in the top panel of Figure 1. V2C is more challeng-ing than other speec... |
Ding_DiffusionRig_Learning_Personalized_Priors_for_Facial_Appearance_Editing_CVPR_2023 | Abstract We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person’s fa-cial appearance, such as expression and lighting, while pre-serving their identity and high-frequency facial details. Key to o... | 1. Introduction It is a longstanding problem in computer vision and graph-ics to photorealistically change the lighting, expression, head pose, etc. of a portrait photo while preserving the person’s identity and high-frequency facial characteristics. The dif-ficulty of this problem stems from its fundamentally under-co... |
Hassani_Neighborhood_Attention_Transformer_CVPR_2023 | Abstract We present Neighborhood Attention (NA) , the first ef-ficient and scalable sliding window attention mechanism for vision. NA is a pixel-wise operation, localizing self at-tention (SA) to the nearest neighboring pixels, and there-fore enjoys a linear time and space complexity compared to the quadratic complexity ... | 1.Proposing Neighborhood Attention (NA) : A simple and flexible explicit sliding window attention mech-anism that localizes each pixel’s attention span to its nearest neighborhood, approaches self attention as its span grows, and maintains translational equivariance. We compare NA in terms of complexity and memory usage... |
Cen_Enlarging_Instance-Specific_and_Class-Specific_Information_for_Open-Set_Action_Recognition_CVPR_2023 | Abstract Open-set action recognition is to reject unknown human action cases which are out of the distribution of the training set. Existing methods mainly focus on learning better un-certainty scores but dismiss the importance of feature rep-resentations. We find that features with richer semantic di-versity can signi... | 1. Introduction Deep learning methods for video action recognition have developed very fast and achieved remarkable performance in recent years [1–4]. However, these methods operate un-der the closed-set condition, i.e., to classify all videos into *Work done as an intern at Alibaba DAMO Academy. CSISInformationOoD𝑠!�... |
Huang_Progressive_Spatio-Temporal_Alignment_for_Efficient_Event-Based_Motion_Estimation_CVPR_2023 | Abstract In this paper, we propose an efficient event-based motion estimation framework for various motion models. Differ-ent from previous works, we design a progressive event-to-map alignment scheme and utilize the spatio-temporal cor-relations to align events. In detail, we progressively align sampled events in an e... | 1. Introduction Event cameras [25, 30, 33], also known as bio-inspired silicon retinas, are novel vision sensors that asynchronously respond to pixel-wise brightness changes. Event cameras have the properties of high temporal resolution and high dy-namic range, which make event cameras appealing to tackle many computer... |
Gillert_Iterative_Next_Boundary_Detection_for_Instance_Segmentation_of_Tree_Rings_CVPR_2023 | Abstract We address the problem of detecting tree rings in mi-croscopy images of shrub cross sections. This can be re-garded as a special case of the instance segmentation task with several unique challenges such as the concentric cir-cular ring shape of the objects and high precision require-ments that result in inade... | 1. Introduction Dendrochronology is the science that provides method-ologies to date tree rings [ 4], i.e. measuring and assigning calendar years to the growth rings present in a wood stem. By analyzing anatomical properties like ring widths or the cell sizes within the rings, dendrochronology can be ap-plied to dating... |
Buchner_Learning_and_Aggregating_Lane_Graphs_for_Urban_Automated_Driving_CVPR_2023 | Abstract Lane graph estimation is an essential and highly challeng-ing task in automated driving and HD map learning. Exist-ing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenar-ios, or significant occlusions in the image space. Moreover, merging overlappin... | 1. Introduction Most automated driving vehicles rely on the knowledge of their immediate surroundings to safely navigate urban environments. Onboard sensors including LiDARs and cam-eras provide perception inputs that are utilized in multiple tasks such as localization [7, 21, 27], tracking [4], or scene understanding ... |
Jin_Video-Text_As_Game_Players_Hierarchical_Banzhaf_Interaction_for_Cross-Modal_Representation_CVPR_2023 | Abstract Contrastive learning-based video-language representa-tion learning approaches, e.g., CLIP , have achieved out-standing performance, which pursue semantic interaction upon pre-defined video-text pairs. To clarify this coarse-grained global interaction and move a step further, we have to encounter challenging sh... | 1. Introduction Representation learning based on both vision and lan-guage has many potential benefits and direct applicability to *Corresponding author: Li Yuan, Jie Chen. 1/0 A woman in dress and a man in suit sit together. (a) Cross -modal contrastive modeling (b) Multivariate cooperative game modeling (Ours)Banzhaf... |
Huang_Rethinking_Few-Shot_Medical_Segmentation_A_Vector_Quantization_View_CVPR_2023 | Abstract The existing few-shot medical segmentation networks share the same practice that the more prototypes, the bet-ter performance. This phenomenon can be theoretically in-terpreted in Vector Quantization (VQ) view: the more pro-totypes, the more clusters are separated from pixel-wise feature points distributed ove... | 1. Introduction Semantic segmentation is one of the fundamental tasks in medical imaging applications, e.g., disease diagnosis [1, 2], monitoring [3,4], and screening [5]. With sufficient labeled data being fed into the deep network, segmentation models can achieve promising results. However, in most practical scenario... |
Bober-Irizar_Architectural_Backdoors_in_Neural_Networks_CVPR_2023 | Abstract Machine learning is vulnerable to adversarial manipula-tion. Previous literature demonstrated that at the training stage attackers can manipulate data [ 14] and data sampling procedures [ 29] to control model behaviour. A common at-tack goal is to plant backdoors i.e. force the victim model to learn to recogni... | 1. Introduction The Machine Learning (ML) community now faces a threat posed by backdoored neural networks; models which are intentionally modified by an attacker in the supply chain to insert hidden behaviour [ 3,14]. A backdoor causes a network’s behaviour to change arbitrarily when a specific se-cret ‘trigger’ is pr... |
Huang_Parametric_Implicit_Face_Representation_for_Audio-Driven_Facial_Reenactment_CVPR_2023 | Abstract Audio-driven facial reenactment is a crucial technique that has a range of applications in film-making, virtual avatars and video conferences. Existing works either em-ploy explicit intermediate face representations ( e.g., 2D fa-cial landmarks or 3D face models) or implicit ones ( e.g., Neural Radiance Fields... | 1. Introduction Audio-driven facial reenactment, also known as audio-driven talking head generation or synthesis, plays an im-portant role in various applications, such as digital human, film-making and virtual video conference. It is a challeng-ing cross-modal task from audio to visual face, which re-quires the genera... |
Chang_Making_Vision_Transformers_Efficient_From_a_Token_Sparsification_View_CVPR_2023 | Abstract The quadratic computational complexity to the number of tokens limits the practical applications of Vision Trans-formers (ViTs). Several works propose to prune redundant tokens to achieve efficient ViTs. However, these methods generally suffer from (i) dramatic accuracy drops, (ii) ap-plication difficulty in t... | 1. Introduction In contrast to standard Convolutional Neural Networks (CNNs) approaches which process images pixel-by-pixel, *Work done during an internship at Alibaba Group. †Equal corresponding authors. ‡Work done at Alibaba Group, and now affiliated with Amazon.Vision Transformers (ViTs) [15, 26, 35, 36, 43] treat a... |
Jin_RefCLIP_A_Universal_Teacher_for_Weakly_Supervised_Referring_Expression_Comprehension_CVPR_2023 | Abstract Referring Expression Comprehension (REC) is a task of grounding the referent based on an expression, and its de-velopment is greatly limited by expensive instance-level an-notations. Most existing weakly supervised methods are built based on two-stage detection networks, which are computationally expensive. In... | 1. Introduction Referring Expression Comprehension (REC), also known as visual grounding [5, 16], aims to locate the target instance in an image based on a referring expres-*Equal Contribution. †Corresponding Author. 123123 456 789Prediction (Pseudo Label) Anchor Points “Person on middle” 2Text Encoder Visual Encoder R... |
Guirguis_NIFF_Alleviating_Forgetting_in_Generalized_Few-Shot_Object_Detection_via_Neural_CVPR_2023 | Abstract Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These con-cerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for c... | 1. Introduction Object detection (OD) is an integral element in mod-ern computer vision perception systems (e.g., robotics and self-driving cars). However, object detectors [1–8] require abundant annotated data to train, which is labor and time intensive. In some applications requiring rare class de-tection, collecting... |
Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023 | Abstract Table structure recognition aims to extract the logical and physical structure of unstructured table images into a machine-readable format. The latest end-to-end image-to-text approaches simultaneously predict the two struc-tures by two decoders, where the prediction of the physi-cal structure (the bounding bo... | 1. Introduction Tables are an essential medium for expressing structural or semi-structural information. Table structure recognition, including recognizing a table’s logical and physical struc-ture, is crucial for understanding and further editing a vi-*Equal contribution. (a) TableFormer (Baseline) (b) V AST (Ours) Fi... |
Gu_MSINet_Twins_Contrastive_Search_of_Multi-Scale_Interaction_for_Object_ReID_CVPR_2023 | Abstract Neural Architecture Search (NAS) has been increasingly appealing to the society of object Re-Identification (ReID), for that task-specific architectures significantly improve the retrieval performance. Previous works explore new opti-mizing targets and search spaces for NAS ReID, yet theyneglect the difference of... | 1. Introduction Object re-identification (Re-ID) aims at retrieving spe-cific object instances across different views [ 39,40,57, 65,70], which attracts much attention in computer vi-sion community due to its wide-range applications. Pre-vious works have achieved great progresses on both super-vised [ 42,49,58] and unsup... |
Jiang_HumanGen_Generating_Human_Radiance_Fields_With_Explicit_Priors_CVPR_2023 | Abstract Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of hu-man radiance fields remains challenging, partially due to the limited human-related priors adopted in existing meth-ods. We present HumanGen, a no... | 1. Introduction We are entering an era where the boundaries of real and virtually generated worlds are dismissing. An epitome of this revolution is the recent rise of 3D-aware and photo-realistic image synthesis in the past several years [5, 6, 11, 16,53,63,91], which combine 2D Generative Adversar-ial Networks (GANs) ... |
Gao_Adaptive_Zone-Aware_Hierarchical_Planner_for_Vision-Language_Navigation_CVPR_2023 | Abstract The task of Vision-Language Navigation (VLN) is for an embodied agent to reach the global goal according to the instruction. Essentially, during navigation, a series of sub-goals need to be adaptively set and achieved, which is nat-urally a hierarchical navigation process. However, previ-ous methods leverage a... | 1. Introduction In recent years, Embodied-AI (E-AI) research has at-tracted a surge of interest within the computer vision, nat-ural language processing and robotics communities since its interdisciplinary nature. The long-term goal of E-AI re-search is to build intelligent agents that can interact with humans to compl... |
Go_Towards_Practical_Plug-and-Play_Diffusion_Models_CVPR_2023 | Abstract Diffusion-based generative models have achieved re-markable success in image generation. Their guidance for-mulation allows an external model to plug-and-play con-trol the generation process for various tasks without fine-tuning the diffusion model. However, the direct use of pub-licly available off-the-shelf ... | 1. Introduction Recently, diffusion-based generative models [49] have shown great success in various domains, including image generation [14, 44, 45], text-to-speech [21, 40], and text *Co-first autor. †Corresponding author. Diffusion…palacehammer[coralreef]street signArmadillo…Segmentation MapImage ClassDepth Map Dept... |
Cho_PartDistillation_Learning_Parts_From_Instance_Segmentation_CVPR_2023 | Abstract We present a scalable framework to learn part segmen-tation from object instance labels. State-of-the-art instance segmentation models contain a surprising amount of part information. However, much of this information is hidden from plain view. For each object instance, the part in-formation is noisy, inconsis... | part to the highest overlapping object ai: ai= arg max jO(Mo j, Mp i). (1) Here we use open-vocabulary Detic model to cover large number of object classes. This association provides us with not just an object query, but also its object instance feature fo ai. We rerank each part proposal using a scoring function r(fp ... |
Geng_GAPartNet_Cross-Category_Domain-Generalizable_Object_Perception_and_Manipulation_via_Generalizable_and_CVPR_2023 | Abstract For years, researchers have been devoted to general-izable object perception and manipulation, where cross-category generalizability is highly desired yet underex-plored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts ). By identifying and defining ... | 1. Introduction Generalizable object perception and manipulation are at the core of building intelligent and multi-functional robots. Recent efforts on generalizing the vision have been devoted to category-level object perception that deals with perceiv-ing novel object instances from known object categories, including... |
Deng_NeRDi_Single-View_NeRF_Synthesis_With_Language-Guided_Diffusion_As_General_Image_CVPR_2023 | Abstract 2D-to-3D reconstruction is an ill-posed problem, yet hu-mans are good at solving this problem due to their prior knowledge of the 3D world developed over years. Driven by this observation, we propose NeRDi , a single-view NeRF synthesis framework with general image priors from 2D diffusion models. Formulating ... | 1. Introduction Novel view synthesis is a long-existing problem in com-puter vision and computer graphics. Recent progresses in neural rendering such as NeRFs [22] have made huge strides in novel view synthesis. Given a set of multi-view images with known camera poses, NeRFs represent a static 3D scene as a radiance fie... |
Dessalene_Therbligs_in_Action_Video_Understanding_Through_Motion_Primitives_CVPR_2023 | Abstract In this paper we introduce a rule-based, compositional, and hierarchical modeling of action using Therbligs as our atoms. Introducing these atoms provides us with a con-sistent, expressive, contact-centered representation of ac-tion. Over the atoms we introduce a differentiable method of rule-based reasoning t... | 1. Introduction We propose the use of Therbligs -a low-level mutu-ally exclusive contact demarcated set of sub-actions. These Therbligs are consistent in that a given action segment has only a single Therblig representation, and Therbligs are ex-pressive in that they capture the meaningful physical as-pects of action r... |
Jiang_InstantAvatar_Learning_Avatars_From_Monocular_Video_in_60_Seconds_CVPR_2023 | Abstract In this paper, we take one step further towards real-world applicability of monocular neural avatar reconstruction by contributing InstantAvatar, a system that can reconstruct human avatars from a monocular video within seconds, and these avatars can be animated and rendered at an inter-active rate. To achieve... | ner (Sec. 3.2). To avoid inefficient sampling of empty space, we leverage the observation that the 3D bounding box around the hu-man body is dominated by empty space. We then propose an empty space skipping scheme specifically designed for humans (Sec. 3.3). Finally, we discuss training objectives and regularization st... |
Hu_You_Only_Segment_Once_Towards_Real-Time_Panoptic_Segmentation_CVPR_2023 | Abstract In this paper, we propose YOSO, a real-time panoptic segmentation framework. YOSO predicts masks via dy-namic convolutions between panoptic kernels and image feature maps, in which you only need to segment once for both instance and semantic segmentation tasks. To reduce the computational overhead, we design a... | 1. Introduction Panoptic segmentation is a task that involves assigning a semantic label and an instance identity to each pixel of an input image. The semantic labels are typically classified into two types, i.e.,stuff including amorphous and uncount-able concepts (such as sky and road), and things consisting of counta... |
Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023 | Abstract Although different approaches have been proposed for 3D point cloud-related tasks, few-shot learning (FSL) of 3D point clouds still remains under-explored. In FSL, un-like traditional supervised learning, the classes of training and test data do not overlap, and a model needs to rec-ognize unseen classes from ... | nt cloud segmentation, which uses an attention-aware, multi-prototype transductive method. A recent point cloud FSL work [26] uses DGCNN [22] as the backbone, and presents a Cross-Instance Adaption module, which achieves good FSL performance on CAD-based point cloud datasets. 3. Motivation Current point cloud FSL model... |
Huang_Learning_Sample_Relationship_for_Exposure_Correction_CVPR_2023 | Abstract Exposure correction task aims to correct the underex-posure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the opti-mization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both un... | 1. Introduction The images captured under non-ideal illumination con-ditions, i.e.,underexposure or overexposure scenes, usually suffer from unpleasant visual effects and thus count against the down-streaming vision tasks. To this end, exposure correction techniques have been developed, which aim to correct both undere... |
Chen_TrojDiff_Trojan_Attacks_on_Diffusion_Models_With_Diverse_Targets_CVPR_2023 | Abstract Diffusion models have achieved great success in a range of tasks, such as image synthesis and molecule design. As such successes hinge on large-scale training data collected from diverse sources, the trustworthiness of these collected data is hard to control or audit. In this work, we aim to explore the vulner... | 1. Introduction Recently, diffusion models [1–4] have emerged as the new competitive deep generative models, demonstrating their impressive capacities in generating diverse, high-quality samples in various data modalities [5–7]. Inspired by non-equilibrium thermodynamics [8], diffusion mod-els are latent variable model... |
Chen_End-to-End_3D_Dense_Captioning_With_Vote2Cap-DETR_CVPR_2023 | Abstract 3D dense captioning aims to generate multiple cap-tions localized with their associated object regions. Exist-ing methods follow a sophisticated “detect-then-describe” pipeline equipped with numerous hand-crafted components.However , these hand-crafted components would yield sub-optimal performance given clutt... | 1. Introduction In recent years, works on 3D learning has grown dramat-ically for various applications [10, 11,21, 41, 42]. Among them, 3D dense captioning [7, 13] requires a system to lo-calize all the objects in a 3D scene and generate descrip-tive sentences for each object. This problem is challenging,given 1) the s... |
Ding_Mitigating_Task_Interference_in_Multi-Task_Learning_via_Explicit_Task_Routing_CVPR_2023 | Abstract Multi-task learning (MTL) seeks to learn a single model to accomplish multiple tasks by leveraging shared infor-mation among the tasks. Existing MTL models, however, have been known to suffer from negative interference among tasks. Efforts to mitigate task interference have focused on either loss/gradient bala... | 1. Introduction Multi-task learning (MTL) is commonly employed to improve learning efficiency and performance of multiple tasks by using supervised signals from other related tasks [6, 25, 37]. These models have led to impressive results across numerous tasks. However, there is well-documented evidence [14,21,32,39] th... |
Ghosh_Learned_Two-Plane_Perspective_Prior_Based_Image_Resampling_for_Efficient_Object_CVPR_2023 | Abstract Real-time efficient perception is critical for autonomous navigation and city scale sensing. Orthogonal to archi-tectural improvements, streaming perception approaches have exploited adaptive sampling improving real-time de-tection performance. In this work, we propose a learnable geometry-guided prior that in... | 1. Introduction Visual perception is important for autonomous driving and decision-making for smarter and sustainable cities. Real-time efficient perception is critical to accelerate these ad-vances. For instance, a single traffic camera captures half a million frames every day or a commuter bus acting as a city sensor... |
Fu_Tell_Me_What_Happened_Unifying_Text-Guided_Video_Completion_via_Multimodal_CVPR_2023 | Abstract Generating a video given the first several static frames is challenging as it anticipates reasonable future frames with temporal coherence. Besides video prediction, the ability to rewind from the last frame or infilling between the head and tail is also crucial, but they have rarely been explored for video co... | 1. Introduction Generative video modeling [15, 70, 84] has made great progress, which first succeeds in unconditional video gen-eration [40,64]. More recently, video prediction [28,36,47] has been trying the controllable setting, which anticipates the future by completing a video from the past frames or a static starti... |
Huang_Neural_Kernel_Surface_Reconstruction_CVPR_2023 | Abstract We present a novel method for reconstructing a 3D im-plicit surface from a large-scale, sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural Kernel Fields (NKF) [ 58] representation. It enjoys similar generalization capabilities to NKF , while simulta-neously addressing its ma... | 1. Introduction The goal of 3D reconstruction is to recover geometry from partial measurements of a shape. In this work, we aimto map a sparse set of oriented points sampled from the sur-face of a shape to a 3D implicit field. This is a challenging inverse problem since point clouds acquired from real-world sensors are ... |
Guillaro_TruFor_Leveraging_All-Round_Clues_for_Trustworthy_Image_Forgery_Detection_and_CVPR_2023 | Abstract In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipula-tion methods, from classic cheapfakes to more recent ma-nipulations based on deep learning. We rely on the ex-traction of both high-level and low-level traces through a transformer-based fusion archit... | 1. Introduction Manipulating images has never been easier, with new powerful editing tools appearing by the day. These new opportunities stimulate the creativity of benign and mali-cious users alike. Previously, crafting a multimedia disin-formation campaign required sophisticated skills, and at-tackers could do little... |
Chen_Train-Once-for-All_Personalization_CVPR_2023 | Abstract We study the problem of how to train a “personalization-friendly” model such that given only the task descriptions, the model can be adapted to different end-users’ needs, e.g., for accurately classifying different subsets of objects. One baseline approach is to train a “generic” model for classi-fying a wide ... | 1. Introduction Recent years have witnessed multiple breakthroughs in visual recognition [10, 17, 23, 25, 36], thanks to the advance in deep learning and the accessibility to large datasets. Specifically, existing works have shown the possibility to train a gigantic and versatile “generic” model capable of classifying ... |
Jayasundara_FlexNeRF_Photorealistic_Free-Viewpoint_Rendering_of_Moving_Humans_From_Sparse_Views_CVPR_2023 | Abstract We present FlexNeRF , a method for photorealistic free-viewpoint rendering of humans in motion from monocular videos. Our approach works well with sparse views, which is a challenging scenario when the subject is exhibiting fast/complex motions. We propose a novel approach which jointly optimizes a canonical t... | 1. Introduction Free-viewpoint rendering of a scene is an important problem often attempted under constrained settings: on subjects demonstrating simple motion carefully captured with multiple cameras [17, 19, 20]. However, photoreal-istic free-viewpoint rendering of moving humans captured from a monocular video still ... |
Abousamra_Topology-Guided_Multi-Class_Cell_Context_Generation_for_Digital_Pathology_CVPR_2023 | Abstract In digital pathology, the spatial context of cells is impor-tant for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challeng-ing. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we intro... | 1. Introduction Deep learning has advanced our learning ability in digital pathology. Deep-learning-based methods have achieved im-pressive performance in various tasks including but not lim-ited to: cell detection and classification [2,23,24,52], nuclei instance segmentation [8, 18, 19, 21, 26, 32–34, 42, 51], sur-viv... |
Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023 | Abstract Structural pruning enables model acceleration by re-moving structurally-grouped parameters from neural net-works. However, the parameter-grouping patterns vary widely across different models, making architecture-specific pruners, which rely on manually-designed grouping schemes, non-generalizable to new archit... | 1. Introduction The recent emergence of edge computing applications calls for the necessity for deep neural compression [16, 22, 25,33,34,61,65–67,69,75]. Among the many network com-pression paradigms, pruning has proven itself to be highly effective and practical [7, 11, 30, 31, 44, 58, 59, 74]. The goal of network pr... |
Guo_Vid2Avatar_3D_Avatar_Reconstruction_From_Videos_in_the_Wild_via_CVPR_2023 | Abstract We present Vid2Avatar, a method to learn human avatars from monocular in-the-wild videos. Reconstructing humans that move naturally from monocular in-the-wild videos is difficult. Solving it requires accurately separating humans from arbitrary backgrounds. Moreover, it requires recon-structing detailed 3D surf... | non-complicated dynamic objects but are not directly applicable to articulated humans with intricate motions. 3. Method We introduce Vid2Avatar, a method for detailed geome-try and appearance reconstruction of implicit neural avatars from monocular videos in the wild. Our method is schemat-ically illustrated in Fig. 2... |
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