mtybilly/Instruct2DS huggingface dataset:
OmniMAE: Single Model Masked Pretraining on Images and Videos
Authors: Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra Rohit Girdhar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Transformer-based architectures have become competitive across a variety of visual domains, most notably images and videos. While prior work studies these modalities in isolation, having a common architecture suggests that one can train a single unified model for multiple visual modalities. Prior attempts at unified modeling typically use architectures tailored for vision tasks, or obtain worse performance compared to single modality models. In this work, we show that masked autoencoding can be used to train a simple Vision Transformer on images and videos, without requiring any labeled data. This single model learns visual representations that are comparable to or better than single-modality representations on both image and video benchmarks, while using a much simpler architecture. Furthermore, this model can be learned by dropping 90% of the image and 95% of the video patches, enabling extremely fast training of huge model architectures. In particular, we show that our single ViT-Huge model can be finetuned to achieve 86.6% on ImageNet and 75.5% on the challenging Something Something-v2 video benchmark, setting a new state-of-the-art.
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Authors: Erika Lu, Zhengqi Li, Noah Snavely, Leonid Sigal, Forrester Cole Mohammed Suhail
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We propose a method to decompose a video into a background and a set of foreground layers, where the background captures stationary elements while the foreground layers capture moving objects along with their associated effects (e.g. shadows and reflections). Our approach is designed for unconstrained monocular videos, with arbitrary camera and object motion. Prior work that tackles this problem assumes that the video can be mapped onto a fixed 2D canvas, severely limiting the possible space of camera motion. Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object. To solve the underconstrained decomposition problem, we propose a new loss formulation based on multi-view consistency. We test our method on challenging videos with complex camera motion and show significant qualitative improvement over current approaches.
Real-Time Neural Light Field on Mobile Devices
Authors: Huan Wang, Pavlo Chemerys, Vladislav Shakhrai, Ju Hu, Yun Fu, Denys Makoviichuk, Sergey Tulyakov, Jian Ren Junli Cao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving 15x   24x storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., 18.04ms (iPhone 13) for rendering one 1008x756 image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR 26.15 vs. 25.91 on the real-world forward-facing dataset).
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class
Authors: Hongliang Li, Fanman Meng, Qingbo Wu, Heqian Qiu, Lanxiao Wang Chao Shang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes. Based on the above observations, we propose a new transformer framework for class-incremental semantic segmentation, dubbed Incrementer, which only needs to add new class tokens to the transformer decoder for new-class learning. Based on the Incrementer, we propose a new knowledge distillation scheme that focuses on the distillation in the old-class regions, which reduces the constraints of the old model on the new-class learning, thus improving the plasticity. Moreover, we propose a class deconfusion strategy to alleviate the overfitting to new classes and the confusion of similar classes. Our method is simple and effective, and extensive experiments show that our method outperforms the SOTAs by a large margin (5 15 absolute points boosts on both Pascal VOC and ADE20k). We hope that our Incrementer can serve as a new strong pipeline for class-incremental semantic segmentation.
End-to-End Video Matting With Trimap Propagation
Authors: Ming-Sui Lee Wei-Lun Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: The research of video matting mainly focuses on temporal coherence and has gained significant improvement via neural networks. However, matting usually relies on user-annotated trimaps to estimate alpha values, which is a labor-intensive issue. Although recent studies exploit video object segmentation methods to propagate the given trimaps, they suffer inconsistent results. Here we present a more robust and faster end-to-end video matting model equipped with trimap propagation called FTP-VM (Fast Trimap Propagation - Video Matting). The FTP-VM combines trimap propagation and video matting in one model, where the additional backbone in memory matching is replaced with the proposed lightweight trimap fusion module. The segmentation consistency loss is adopted from automotive segmentation to fit trimap segmentation with the collaboration of RNN (Recurrent Neural Network) to improve the temporal coherence. The experimental results demonstrate that the FTP-VM performs competitively both in composited and real videos only with few given trimaps. The efficiency is eight times higher than the state-of-the-art methods, which confirms its robustness and applicability in real-time scenarios. The code is available at https://github.com/csvt32745/FTP-VM
DropMAE: Masked Autoencoders With Spatial-Attention Dropout for Tracking Tasks
Authors: Tianyu Yang, Ziquan Liu, Baoyuan Wu, Ying Shan, Antoni B. Chan Qiangqiang Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this paper, we study masked autoencoder (MAE) pretraining on videos for matching-based downstream tasks, including visual object tracking (VOT) and video object segmentation (VOS). A simple extension of MAE is to randomly mask out frame patches in videos and reconstruct the frame pixels. However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS. To alleviate this problem, we propose DropMAE, which adaptively performs spatial-attention dropout in the frame reconstruction to facilitate temporal correspondence learning in videos. We show that our DropMAE is a strong and efficient temporal matching learner, which achieves better finetuning results on matching-based tasks than the ImageNetbased MAE with 2x faster pre-training speed. Moreover, we also find that motion diversity in pre-training videos is more important than scene diversity for improving the performance on VOT and VOS. Our pre-trained DropMAE model can be directly loaded in existing ViT-based trackers for fine-tuning without further modifications. Notably, DropMAE sets new state-of-the-art performance on 8 out of 9 highly competitive video tracking and segmentation datasets. Our code and pre-trained models are available at https://github.com/jimmy-dq/DropMAE.git.
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning
Authors: Renjie Liang, Zhedong Zheng, Wenqiao Zhang, Shengyu Zhang, Juncheng Li, Mengze Li, Tat-seng Chua Wei Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent research on video moment retrieval has mostly focused on enhancing the performance of accuracy, efficiency, and robustness, all of which largely rely on the abundance of high-quality annotations. While the precise frame-level annotations are time-consuming and cost-expensive, few attentions have been paid to the labeling process. In this work, we explore a new interactive manner to stimulate the process of human-in-the-loop annotation in video moment retrieval task. The key challenge is to select "ambiguous" frames and videos for binary annotations to facilitate the network training. To be specific, we propose a new hierarchical uncertainty-based modeling that explicitly considers modeling the uncertainty of each frame within the entire video sequence corresponding to the query description, and selecting the frame with the highest uncertainty. Only selected frame will be annotated by the human experts, which can largely reduce the workload. After obtaining a small number of labels provided by the expert, we show that it is sufficient to learn a competitive video moment retrieval model in such a harsh environment. Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection. In general, our active learning strategy for video moment retrieval works not only at the frame level but also at the sequence level. Experiments on two public datasets validate the effectiveness of our proposed method.
High-Fidelity Clothed Avatar Reconstruction From a Single Image
Authors: Xiaomei Zhang, Yuliang Xiu, Hongwei Yi, Xudong Liu, Guo-Jun Qi, Yong Zhang, Xuan Wang, Xiangyu Zhu, Zhen Lei Tingting Liao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining the advantages of the high accuracy of optimization-based methods and the efficiency of learning-based methods, we propose a coarse-to-fine way to realize a high-fidelity clothed avatar reconstruction (CAR) from a single image. At the first stage, we use an implicit model to learn the general shape in the canonical space of a person in a learning-based way, and at the second stage, we refine the surface detail by estimating the non-rigid deformation in the posed space in an optimization way. A hyper-network is utilized to generate a good initialization so that the convergence of the optimization process is greatly accelerated. Extensive experiments on various datasets show that the proposed CAR successfully produces high-fidelity avatars for arbitrarily clothed humans in real scenes. The codes will be released.
Zero-Shot Object Counting
Authors: Hieu Le, Vu Nguyen, Viresh Ranjan, Dimitris Samaras Jingyi Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Class-agnostic object counting aims to count object instances of an arbitrary class at test time. It is challenging but also enables many potential applications. Current methods require human-annotated exemplars as inputs which are often unavailable for novel categories, especially for autonomous systems. Thus, we propose zero-shot object counting (ZSC), a new setting where only the class name is available during test time. Such a counting system does not require human annotators in the loop and can operate automatically. Starting from a class name, we propose a method that can accurately identify the optimal patches which can then be used as counting exemplars. Specifically, we first construct a class prototype to select the patches that are likely to contain the objects of interest, namely class-relevant patches. Furthermore, we introduce a model that can quantitatively measure how suitable an arbitrary patch is as a counting exemplar. By applying this model to all the candidate patches, we can select the most suitable patches as exemplars for counting. Experimental results on a recent class-agnostic counting dataset, FSC-147, validate the effectiveness of our method.
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective
Authors: Haotian Bai, Lin Wang Jinjing Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Endeavors have been recently made to leverage the vision transformer (ViT) for the challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross-attention in ViT for direct domain alignment. However, as the performance of cross-attention highly relies on the quality of pseudo labels for targeted samples, it becomes less effective when the domain gap becomes large. We solve this problem from a game theory's perspective with the proposed model dubbed as PMTrans, which bridges source and target domains with an intermediate domain. Specifically, we propose a novel ViT-based module called PatchMix that effectively builds up the intermediate domain, i.e., probability distribution, by learning to sample patches from both domains based on the game-theoretical models. This way, it learns to mix the patches from the source and target domains to maximize the cross entropy (CE), while exploiting two semi-supervised mixup losses in the feature and label spaces to minimize it. As such, we interpret the process of UDA as a min-max CE game with three players, including the feature extractor, classifier, and PatchMix, to find the Nash Equilibria. Moreover, we leverage attention maps from ViT to re-weight the label of each patch by its importance, making it possible to obtain more domain-discriminative feature representations. We conduct extensive experiments on four benchmark datasets, and the results show that PMTrans significantly surpasses the ViT-based and CNN-based SoTA methods by +3.6% on Office-Home, +1.4% on Office-31, and +17.7% on DomainNet, respectively. https://vlis2022.github.io/cvpr23/PMTrans
Implicit Diffusion Models for Continuous Super-Resolution
Authors: Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, Xiantong Zhen, Baochang Zhang Sicheng Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Image super-resolution (SR) has attracted increasing attention due to its wide applications. However, current SR methods generally suffer from over-smoothing and artifacts, and most work only with fixed magnifications. This paper introduces an Implicit Diffusion Model (IDM) for high-fidelity continuous image super-resolution. IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation. Furthermore, we design a scale-controllable conditioning mechanism that consists of a low-resolution (LR) conditioning network and a scaling factor. The scaling factor regulates the resolution and accordingly modulates the proportion of the LR information and generated features in the final output, which enables the model to accommodate the continuous-resolution requirement. Extensive experiments validate the effectiveness of our IDM and demonstrate its superior performance over prior arts.
VGFlow: Visibility Guided Flow Network for Human Reposing
Authors: Krishna Kumar Singh, Mayur Hemani, Jingwan Lu, Mausoom Sarkar, Duygu Ceylan, Balaji Krishnamurthy Rishabh Jain
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: The task of human reposing involves generating a realistic image of a model standing in an arbitrary conceivable pose. There are multiple difficulties in generating perceptually accurate images and existing methods suffers from limitations in preserving texture, maintaining pattern coherence, respecting cloth boundaries, handling occlusions, manipulating skin generation etc. These difficulties are further exacerbated by the fact that the possible space of pose orientation for humans is large and variable, the nature of clothing items are highly non-rigid and the diversity in body shape differ largely among the population. To alleviate these difficulties and synthesize perceptually accurate images, we propose VGFlow, a model which uses a visibility guided flow module to disentangle the flow into visible and invisible parts of the target for simultaneous texture preservation and style manipulation. Furthermore, to tackle distinct body shapes and avoid network artifacts, we also incorporate an a self-supervised patch-wise "realness" loss to further improve the output. VGFlow achieves state-of-the-art results as observed qualitatively and quantitatively on different image quality metrics(SSIM, LPIPS, FID).
Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection
Authors: Feipeng Da Yi Yu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://github.com/open-mmlab/mmrotate.
Improving Selective Visual Question Answering by Learning From Your Peers
Authors: Spencer Whitehead, Rishabh Maheshwary, Ramakrishna Vedantam, Stefan Scherer, Xinlei Chen, Matthieu Cord, Marcus Rohrbach Corentin Dancette
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-box, can have difficulties abstaining from answering when they are wrong. The option to abstain, also called Selective Prediction, is highly relevant when deploying systems to users who must trust the system's output (e.g., VQA assistants for users with visual impairments). For such scenarios, abstention can be especially important as users may provide out-of-distribution (OOD) or adversarial inputs that make incorrect answers more likely. In this work, we explore Selective VQA in both in-distribution (ID) and OOD scenarios, where models are presented with mixtures of ID and OOD data. The goal is to maximize the number of questions answered while minimizing the risk of error on those questions. We propose a simple yet effective Learning from Your Peers (LYP) approach for training multimodal selection functions for making abstention decisions. Our approach uses predictions from models trained on distinct subsets of the training data as targets for optimizing a Selective VQA model. It does not require additional manual labels or held-out data and provides a signal for identifying examples that are easy/difficult to generalize to. In our extensive evaluations, we show this benefits a number of models across different architectures and scales. Overall, for ID, we reach 32.92% in the selective prediction metric coverage at 1% risk of error (C@1%) which doubles the previous best coverage of 15.79% on this task. For mixed ID/OOD, using models' softmax confidences for abstention decisions performs very poorly, answering <5% of questions at 1% risk of error even when faced with only 10% OOD examples, but a learned selection function with LYP can increase that to 25.38% C@1%.
CAMS: CAnonicalized Manipulation Spaces for Category-Level Functional Hand-Object Manipulation Synthesis
Authors: Qingyuan Zheng, Lixing Fang, Yun Liu, Li Yi Juntian Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: In this work, we focus on a novel task of category-level functional hand-object manipulation synthesis covering both rigid and articulated object categories. Given an object geometry, an initial human hand pose as well as a sparse control sequence of object poses, our goal is to generate a physically reasonable hand-object manipulation sequence that performs like human beings. To address such a challenge, we first design CAnonicalized Manipulation Spaces (CAMS), a two-level space hierarchy that canonicalizes the hand poses in an object-centric and contact-centric view. Benefiting from the representation capability of CAMS, we then present a two-stage framework for synthesizing human-like manipulation animations. Our framework achieves state-of-the-art performance for both rigid and articulated categories with impressive visual effects. Codes and video results can be found at our project homepage: https://cams-hoi.github.io/.
Neural Lens Modeling
Authors: Aljaž Božič, Noah Snavely, Christoph Lassner Wenqi Xian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be achieved for camera calibration and the fidelity of the results of 3D reconstruction. In this paper, we propose NeuroLens, a neural lens model for distortion and vignetting that can be used for point projection and ray casting and can be optimized through both operations. This means that it can (optionally) be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction, e.g., while optimizing a radiance field. To evaluate the performance of our proposed model, we create a comprehensive dataset assembled from the Lensfun database with a multitude of lenses. Using this and other real-world datasets, we show that the quality of our proposed lens model outperforms standard packages as well as recent approaches while being much easier to use and extend. The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems. Visit our project website at: https://neural-lens.github.io.
CoralStyleCLIP: Co-Optimized Region and Layer Selection for Image Editing
Authors: Debraj Basu, Shradha Agrawal, Dhwanit Agarwal, Deepak Pai Ambareesh Revanur
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Edit fidelity is a significant issue in open-world controllable generative image editing. Recently, CLIP-based approaches have traded off simplicity to alleviate these problems by introducing spatial attention in a handpicked layer of a StyleGAN. In this paper, we propose CoralStyleCLIP, which incorporates a multi-layer attention-guided blending strategy in the feature space of StyleGAN2 for obtaining high-fidelity edits. We propose multiple forms of our co-optimized region and layer selection strategy to demonstrate the variation of time complexity with the quality of edits over different architectural intricacies while preserving simplicity. We conduct extensive experimental analysis and benchmark our method against state-of-the-art CLIP-based methods. Our findings suggest that CoralStyleCLIP results in high-quality edits while preserving the ease of use.
GLeaD: Improving GANs With a Generator-Leading Task
Authors: Ceyuan Yang, Yinghao Xu, Xihui Liu, Yujiu Yang, Yujun Shen Qingyan Bai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Generative adversarial network (GAN) is formulated as a two-player game between a generator (G) and a discriminator (D), where D is asked to differentiate whether an image comes from real data or is produced by G. Under such a formulation, D plays as the rule maker and hence tends to dominate the competition. Towards a fairer game in GANs, we propose a new paradigm for adversarial training, which makes G assign a task to D as well. Specifically, given an image, we expect D to extract representative features that can be adequately decoded by G to reconstruct the input. That way, instead of learning freely, D is urged to align with the view of G for domain classification. Experimental results on various datasets demonstrate the substantial superiority of our approach over the baselines. For instance, we improve the FID of StyleGAN2 from 4.30 to 2.55 on LSUN Bedroom and from 4.04 to 2.82 on LSUN Church. We believe that the pioneering attempt present in this work could inspire the community with better designed generator-leading tasks for GAN improvement. Project page is at https://ezioby.github.io/glead/.
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis
Authors: Bing-Kun Bao, Hao Tang, Changsheng Xu Ming Tao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are challenging to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about 3% training data and 6% learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves 120 times faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of our GALIP. Code is available at https://github.com/tobran/GALIP.
Look, Radiate, and Learn: Self-Supervised Localisation via Radio-Visual Correspondence
Authors: Maximilian Arnold Mohammed Alloulah
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Next generation cellular networks will implement radio sensing functions alongside customary communications, thereby enabling unprecedented worldwide sensing coverage outdoors. Deep learning has revolutionised computer vision but has had limited application to radio perception tasks, in part due to lack of systematic datasets and benchmarks dedicated to the study of the performance and promise of radio sensing. To address this gap, we present MaxRay: a synthetic radio-visual dataset and benchmark that facilitate precise target localisation in radio. We further propose to learn to localise targets in radio without supervision by extracting self-coordinates from radio-visual correspondence. We use such self-supervised coordinates to train a radio localiser network. We characterise our performance against a number of state-of-the-art baselines. Our results indicate that accurate radio target localisation can be automatically learned from paired radio-visual data without labels, which is important for empirical data. This opens the door for vast data scalability and may prove key to realising the promise of robust radio sensing atop a unified communication-perception cellular infrastructure. Dataset will be hosted on IEEE DataPort.
Multiplicative Fourier Level of Detail
Authors: Zhong Zheng, Qiaoqiao Jin, Bingbing Ni Yishun Dou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme.
Indiscernible Object Counting in Underwater Scenes
Authors: Zhaochong An, Yun Liu, Ce Liu, Christos Sakaridis, Deng-Ping Fan, Luc Van Gool Guolei Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 high-resolution images and 659,024 annotated center points. Our dataset consists of a large number of indiscernible objects (mainly fish) in underwater scenes, making the annotation process all the more challenging. IOCfish5K is superior to existing datasets with indiscernible scenes because of its larger scale, higher image resolutions, more annotations, and denser scenes. All these aspects make it the most challenging dataset for IOC so far, supporting progress in this area. For benchmarking purposes, we select 14 mainstream methods for object counting and carefully evaluate them on IOCfish5K. Furthermore, we propose IOCFormer, a new strong baseline that combines density and regression branches in a unified framework and can effectively tackle object counting under concealed scenes. Experiments show that IOCFormer achieves state-of-the-art scores on IOCfish5K.
Shape-Erased Feature Learning for Visible-Infrared Person Re-Identification
Authors: Ancong Wu, Wei-Shi Zheng Jiawei Feng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Due to the modality gap between visible and infrared images with high visual ambiguity, learning diverse modality-shared semantic concepts for visible-infrared person re-identification (VI-ReID) remains a challenging problem. Body shape is one of the significant modality-shared cues for VI-ReID. To dig more diverse modality-shared cues, we expect that erasing body-shape-related semantic concepts in the learned features can force the ReID model to extract more and other modality-shared features for identification. To this end, we propose shape-erased feature learning paradigm that decorrelates modality-shared features in two orthogonal subspaces. Jointly learning shape-related feature in one subspace and shape-erased features in the orthogonal complement achieves a conditional mutual information maximization between shape-erased feature and identity discarding body shape information, thus enhancing the diversity of the learned representation explicitly. Extensive experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
Relational Context Learning for Human-Object Interaction Detection
Authors: Deunsol Jung, Minsu Cho Sanghyun Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Recent state-of-the-art methods for HOI detection typically build on transformer architectures with two decoder branches, one for human-object pair detection and the other for interaction classification. Such disentangled transformers, however, may suffer from insufficient context exchange between the branches and lead to a lack of context information for relational reasoning, which is critical in discovering HOI instances. In this work, we propose the multiplex relation network (MUREN) that performs rich context exchange between three decoder branches using unary, pairwise, and ternary relations of human, object, and interaction tokens. The proposed method learns comprehensive relational contexts for discovering HOI instances, achieving state-of-the-art performance on two standard benchmarks for HOI detection, HICO-DET and V-COCO.
Low-Light Image Enhancement via Structure Modeling and Guidance
Authors: Ruixing Wang, Jiangbo Lu Xiaogang Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: This paper proposes a new framework for low-light image enhancement by simultaneously conducting the appearance as well as structure modeling. It employs the structural feature to guide the appearance enhancement, leading to sharp and realistic results. The structure modeling in our framework is implemented as the edge detection in low-light images. It is achieved with a modified generative model via designing a structure-aware feature extractor and generator. The detected edge maps can accurately emphasize the essential structural information, and the edge prediction is robust towards the noises in dark areas. Moreover, to improve the appearance modeling, which is implemented with a simple U-Net, a novel structure-guided enhancement module is proposed with structure-guided feature synthesis layers. The appearance modeling, edge detector, and enhancement module can be trained end-to-end. The experiments are conducted on representative datasets (sRGB and RAW domains), showing that our model consistently achieves SOTA performance on all datasets with the same architecture.
On Calibrating Semantic Segmentation Models: Analyses and an Algorithm
Authors: Boqing Gong, Liqiang Wang Dongdong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: We study the problem of semantic segmentation calibration. Lots of solutions have been proposed to approach model miscalibration of confidence in image classification. However, to date, confidence calibration research on semantic segmentation is still limited. We provide a systematic study on the calibration of semantic segmentation models and propose a simple yet effective approach. First, we find that model capacity, crop size, multi-scale testing, and prediction correctness have impact on calibration. Among them, prediction correctness, especially misprediction, is more important to miscalibration due to over-confidence. Next, we propose a simple, unifying, and effective approach, namely selective scaling, by separating correct/incorrect prediction for scaling and more focusing on misprediction logit smoothing. Then, we study popular existing calibration methods and compare them with selective scaling on semantic segmentation calibration. We conduct extensive experiments with a variety of benchmarks on both in-domain and domain-shift calibration and show that selective scaling consistently outperforms other methods.
Visual Atoms: Pre-Training Vision Transformers With Sinusoidal Waves
Authors: Ryo Hayamizu, Nakamasa Inoue, Hirokatsu Kataoka, Rio Yokota Sora Takashima
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Formula-driven supervised learning (FDSL) has been shown to be an effective method for pre-training vision transformers, where ExFractalDB-21k was shown to exceed the pre-training effect of ImageNet-21k. These studies also indicate that contours mattered more than textures when pre-training vision transformers. However, the lack of a systematic investigation as to why these contour-oriented synthetic datasets can achieve the same accuracy as real datasets leaves much room for skepticism. In the present work, we develop a novel methodology based on circular harmonics for systematically investigating the design space of contour-oriented synthetic datasets. This allows us to efficiently search the optimal range of FDSL parameters and maximize the variety of synthetic images in the dataset, which we found to be a critical factor. When the resulting new dataset VisualAtom-21k is used for pre-training ViT-Base, the top-1 accuracy reached 83.7% when fine-tuning on ImageNet-1k. This is only 0.5% difference from the top-1 accuracy (84.2%) achieved by the JFT-300M pre-training, even though the scale of images is 1/14. Unlike JFT-300M which is a static dataset, the quality of synthetic datasets will continue to improve, and the current work is a testament to this possibility. FDSL is also free of the common issues associated with real images, e.g. privacy/copyright issues, labeling costs/errors, and ethical biases.
Multi-Label Compound Expression Recognition: C-EXPR Database & Network
Authors: Dimitrios Kollias
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Research in automatic analysis of facial expressions mainly focuses on recognising the seven basic ones. However, compound expressions are more diverse and represent the complexity and subtlety of our daily affective displays more accurately. Limited research has been conducted for compound expression recognition (CER), because only a few databases exist, which are small, lab controlled, imbalanced and static. In this paper we present an in-the-wild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes. We also propose C-EXPR-NET, a multi-task learning (MTL) method for CER and AU detection (AU-D); the latter task is introduced to enhance CER performance. For AU-D we incorporate AU semantic description along with visual information. For CER we use a multi-label formulation and the KL-divergence loss. We also propose a distribution matching loss for coupling CER and AU-D tasks to boost their performance and alleviate negative transfer (i.e., when MT model's performance is worse than that of at least one single-task model). An extensive experimental study has been conducted illustrating the excellent performance of C-EXPR-NET, validating the theoretical claims. Finally, C-EXPR-NET is shown to effectively generalize its knowledge in new emotion recognition contexts, in a zero-shot manner.
Masked Autoencoding Does Not Help Natural Language Supervision at Scale
Authors: Vaishaal Shankar, Angelos Katharopoulos, Yinfei Yang, Tom Gunter Floris Weers
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks. Recent works such as M3AE (Geng et al 2022) and SLIP (Mu et al 2022) have suggested that these approaches can be effectively combined, but most notably their results use small (<20M examples) pre-training datasets and don't effectively reflect the large-scale regime (>100M samples) that is commonly used for these approaches. Here we investigate whether a similar approach can be effective when trained with a much larger amount of data. We find that a combination of two state of the art approaches: masked auto-encoders, MAE (He et al 2021) and contrastive language image pre-training, CLIP (Radford et al 2021) provides a benefit over CLIP when trained on a corpus of 11.3M image-text pairs, but little to no benefit (as evaluated on a suite of common vision tasks) over CLIP when trained on a large corpus of 1.4B images. Our work provides some much needed clarity into the effectiveness (or lack thereof) of self supervision for large-scale image-text training.
CORA: Adapting CLIP for Open-Vocabulary Detection With Region Prompting and Anchor Pre-Matching
Authors: Feng Zhu, Rui Zhao, Hongsheng Li Xiaoshi Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks; (2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA+ on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA+ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark. The code is available at https://github.com/tgxs002/CORA.
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars
Authors: Hsin-Ying Lee, Peihao Zhu, Menglei Chai, Aliaksandr Siarohin, Peter Wonka, Sergey Tulyakov Rameen Abdal
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 (Main Conference Track), 2023

Abstract: Modern 3D-GANs synthesize geometry and texture by training on large-scale datasets with a consistent structure. Training such models on stylized, artistic data, with often unknown, highly variable geometry, and camera information has not yet been shown possible. Can we train a 3D GAN on such artistic data, while maintaining multi-view consistency and texture quality? To this end, we propose an adaptation framework, where the source domain is a pre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic datasets. We, then, distill the knowledge from a 2D generator to the source 3D generator. To do that, we first propose an optimization-based method to align the distributions of camera parameters across domains. Second, we propose regularizations necessary to learn high-quality texture, while avoiding degenerate geometric solutions, such as flat shapes. Third, we show a deformation-based technique for modeling exaggerated geometry of artistic domains, enabling---as a byproduct---personalized geometric editing. Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains. Our contributions---for the first time---allow for the generation, editing, and animation of personalized artistic 3D avatars on artistic datasets.