mtybilly/Instruct2DS huggingface dataset:
Rethinking Fast Fourier Convolution in Image Inpainting
Authors: Tianyi Chu, Jiafu Chen, Jiakai Sun, Shuobin Lian, Zhizhong Wang, Zhiwen Zuo, Lei Zhao, Wei Xing, Dongming Lu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recently proposed image inpainting method LaMa builds its network upon Fast Fourier Convolution (FFC), which was originally proposed for high-level vision tasks like image classification. FFC empowers the fully convolutional network to have a global receptive field in its early layers. Thanks to the unique character of the FFC module, LaMa has the ability to produce robust repeating texture, which can not be achieved by the previous inpainting methods. However, is the vanilla FFC module suitable for low-level vision tasks like image inpainting? In this paper, we analyze the fundamental flaws of using FFC in image inpainting, which are 1) spectrum shifting, 2) unexpected spatial activation, and 3) limited frequency receptive field. Such flaws make FFC-based inpainting framework difficult in generating complicated texture and performing faithful reconstruction. Based on the above analysis, we propose a novel Unbiased Fast Fourier Convolution (UFFC) module, which modifies the vanilla FFC module with 1) range transform and inverse transform, 2) absolute position embedding, 3) dynamic skip connection, and 4) adaptive clip, to overcome such flaws, achieving better inpainting results. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our method, outperforming the state-of-the-art methods in both texture-capturing ability and expressiveness.
Learning Robust Representations with Information Bottleneck and Memory Network for RGB-D-based Gesture Recognition
Authors: Yunan Li, Huizhou Chen, Guanwen Feng, Qiguang Miao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Although previous RGB-D-based gesture recognition methods have shown promising performance, researchers often overlook the interference of task-irrelevant cues like illumination and background. These unnecessary factors are learned together with the predictive ones by the network and hinder accurate recognition. In this paper, we propose a convenient and analytical framework to learn a robust feature representation that is impervious to gesture-irrelevant factors. Based on the Information Bottleneck theory, two rules of Sufficiency and Compactness are derived to develop a new information-theoretic loss function, which cultivates a more sufficient and compact representation from the feature encoding and mitigates the impact of gesture-irrelevant information. To highlight the predictive information, we further integrate a memory network. Using our proposed content-based and contextual memory addressing scheme, we weaken the nuisances while preserving the task-relevant information, providing guidance for refining the feature representation. Experiments conducted on three public datasets demonstrate that our approach leads to a better feature representation and achieves better performance than state-of-the-art methods.
P1AC: Revisiting Absolute Pose From a Single Affine Correspondence
Authors: Jonathan Ventura, Zuzana Kukelova, Torsten Sattler, Dániel Baráth
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://github.com/jonathanventura/P1AC/.
LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction
Authors: Haesoo Chung, Nam Ik Cho
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: As demands for high-quality videos continue to rise, high-resolution and high-dynamic range (HDR) imaging techniques are drawing attention. To generate an HDR video from low dynamic range (LDR) images, one of the critical steps is the motion compensation between LDR frames, for which most existing works employed the optical flow algorithm. However, these methods suffer from flow estimation errors when saturation or complicated motions exist. In this paper, we propose an end-to-end HDR video composition framework, which aligns LDR frames in the feature space and then merges aligned features into an HDR frame, without relying on pixel-domain optical flow. Specifically, we propose a luminance-based alignment network for HDR (LAN-HDR) consisting of an alignment module and a hallucination module. The alignment module aligns a frame to the adjacent reference by evaluating luminance-based attention, excluding color information. The hallucination module generates sharp details, especially for washed-out areas due to saturation. The aligned and hallucinated features are then blended adaptively to complement each other. Finally, we merge the features to generate a final HDR frame. In training, we adopt a temporal loss, in addition to frame reconstruction losses, to enhance temporal consistency and thus reduce flickering. Extensive experiments demonstrate that our method performs better or comparable to state-of-the-art methods on several benchmarks. Codes are available at https://github.com/haesoochung/LAN-HDR.
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
Authors: Huiyuan Fu, Wenkai Zheng, Xicong Wang, Jiaxuan Wang, Heng Zhang, Huadong Ma
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Low-light video enhancement is a challenging task with broad applications. However, current research in this area is limited by the lack of high-quality benchmark datasets. To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras. Our dataset provides dynamic video pairs with pronounced camera motion and strict spatial alignment. To achieve general low-light video enhancement, we also propose a novel Retinex-based method named Light Adjustable Network (LAN). LAN iteratively refines the illumination and adaptively adjusts it under varying lighting conditions, leading to visually appealing results even in diverse real-world scenarios. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset and code will be publicly available.
RED-PSM: Regularization by Denoising of Partially Separable Models for Dynamic Imaging
Authors: Berk Iskender, Marc L. Klasky, Yoram Bresler
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Dynamic imaging involves the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. In this work, we propose an approach, RED-PSM, which combines for the first time two powerful techniques to address this challenging imaging problem. The first, are partially separable models, which have been used to introduce a low-rank prior for the spatio-temporal object. The second is the recent Regularization by Denoising (RED), which provides a flexible framework to exploit the impressive performance of state-of-the-art image denoising algorithms, for various inverse problems. We propose a partially separable objective with RED and an optimization scheme with variable splitting and ADMM. Our objective is proved to converge to a value corresponding to a stationary point satisfying the first-order optimality conditions. Convergence is accelerated by a particular projection-domain-based initialization. We demonstrate the performance and computational improvements of our proposed RED-PSM with a learned image denoiser by comparing it to a recent deep-prior-based method TD-DIP. Although the emphasis is on dynamic tomography, we also demonstrate the performance advantages of RED-PSM in a dynamic cardiac MRI setting.
Unsupervised Manifold Linearizing and Clustering
Authors: Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, Benjamin D. Haeffele
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data.
Lossy and Lossless (L2) Post-training Model Size Compression
Authors: Yumeng Shi, Shihao Bai, Xiuying Wei, Ruihao Gong, Jianlei Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge sizes cause significant inconvenience for transmission and storage. Many previous studies have explored model size compression. However, these studies often approach various lossy and lossless compression methods in isolation, leading to challenges in achieving high compression ratios efficiently. This work proposes a post-training model size compression method that combines lossy and lossless compression in a unified way. We first propose a unified parametric weight transformation, which ensures different lossy compression methods can be performed jointly in a post-training manner. Then, a dedicated differentiable counter is introduced to guide the optimization of lossy compression to arrive at a more suitable point for later lossless compression. Additionally, our method can easily control a desired global compression ratio and allocate adaptive ratios for different layers. Finally, our method can achieve a stable 10 times compression ratio without sacrificing accuracy and a 20 times compression ratio with minor accuracy loss in a short time. Our code is available at https://github.com/ModelTC/L2_Compression.
C2ST: Cross-Modal Contextualized Sequence Transduction for Continuous Sign Language Recognition
Authors: Huaiwen Zhang, Zihang Guo, Yang Yang, Xin Liu, De Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Continuous Sign Language Recognition (CSLR) aims to transcribe the signs of an untrimmed video into written words or glosses. The mainstream framework for CSLR consists of a spatial module for visual representation learning, a temporal module aggregating the local and global temporal information of frame sequence, and the connectionist temporal classification (CTC) loss, which aligns video features with gloss sequence. Unfortunately, the language prior implicit in the gloss sequence is ignored throughout the modeling process. Furthermore, the contextualization of glosses is further ignored in alignment learning, as CTC makes an independence assumption between glosses. In this paper, we propose a Cross-modal Contextualized Sequence Transduction (C2ST) for CSLR, which effectively incorporates the knowledge of gloss sequence into the process of video representation learning and sequence transduction. Specifically, we introduce a cross-modal context learning framework for CSLR, in which the linguistic features of gloss sequences is extracted by a language model, and recurrently integrate with visual features for video modelling. Moreover, we introduce the contextualized sequence transduction loss that incorporates the contextual information of gloss sequences in label prediction, without making any independence assumptions between the glosses. Our method sets the new state of the art on three widely used large-scale sign language recognition datasets: Phoenix-2014, Phoenix-2014-T, and CSL-Daily. On CSL-Daily, our approach achieves an absolute gain of 4.9% WER compared to the best published results.
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion
Authors: Qi Cai, Yingwei Pan, Ting Yao, Chong-Wah Ngo, Tao Mei
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for 3D object detection. ObjectFusion first learns three kinds of modality-specific feature maps (i.e., voxel, BEV, and image features) from LiDAR point clouds and its BEV projections, camera images. Then a set of 3D object proposals are produced from the BEV features via a heatmap-based proposal generator. Next, the 3D object proposals are reprojected back to voxel, BEV, and image spaces. We leverage voxel and RoI pooling to generate spatially aligned object-centric features for each modality. All the object-centric features of three modalities are further fused at object level, which is finally fed into the detection heads. Extensive experiments on nuScenes dataset demonstrate the superiority of our ObjectFusion, by achieving 69.8% mAP on nuScenes validation set and improving BEVFusion by 1.3%.
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field
Authors: Xueting Yang, Yihao Luo, Yuliang Xiu, Wei Wang, Hao Xu, Zhaoxin Fan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple "value to distribution" transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at https://github.com/psyai-net/D-IF_release.
MMVP: Motion-Matrix-Based Video Prediction
Authors: Yiqi Zhong, Luming Liang, Ilya Zharkov, Ulrich Neumann
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: A central challenge of video prediction lies where the system has to reason the object's future motion from image frames while simultaneously maintaining the consistency of its appearance across frames. This work introduces an end-to-end trainable two-stream video prediction framework, Motion-Matrix-based Video Prediction (MMVP), to tackle this challenge. Unlike previous methods that usually handle motion prediction and appearance maintenance within the same set of modules, MMVP decouples motion and appearance information by constructing appearance-agnostic motion matrices. The motion matrices represent the temporal similarity of each and every pair of feature patches in the input frames, and are the sole input of the motion prediction module in MMVP. This design improves video prediction in both accuracy and efficiency, and reduces the model size. Results of extensive experiments demonstrate that MMVP outperforms state-of-the-art systems on public data sets by non-negligible large margins (approx. 1 db in PSNR, UCF Sports) in significantly smaller model sizes (84% the size or smaller). Please refer to https://github.com/Kay1794/MMVP-motion-matrix-based-video-prediction for the official code and the datasets used in this paper.
Human Preference Score: Better Aligning Text-to-Image Models with Human Preference
Authors: Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, Hongsheng Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent years have witnessed a rapid growth of deep generative models, with text-to-image models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, such as awkward combinations of limbs and facial expressions. To address this issue, we collect a dataset of human choices on generated images from the Stable Foundation Discord channel. Our experiments demonstrate that current evaluation metrics for generative models do not correlate well with human choices. Thus, we train a human preference classifier with the collected dataset and derive a Human Preference Score (HPS) based on the classifier. Using HPS, we propose a simple yet effective method to adapt Stable Diffusion to better align with human preferences. Our experiments show that HPS outperforms CLIP in predicting human choices and has good generalization capability toward images generated from other models. By tuning Stable Diffusion with the guidance of HPS, the adapted model is able to generate images that are more preferred by human users. The project page is available here: https://tgxs002.github.io/align_sd_web/.
Guided Motion Diffusion for Controllable Human Motion Synthesis
Authors: Korrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, Siyu Tang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Denoising diffusion models have shown great promise in human motion synthesis conditioned on natural language descriptions. However, integrating spatial constraints, such as pre-defined motion trajectories and obstacles, remains a challenge despite being essential for bridging the gap between isolated human motion and its surrounding environment. To address this issue, we propose Guided Motion Diffusion (GMD), a method that incorporates spatial constraints into the motion generation process. Specifically, we propose an effective feature projection scheme that manipulates motion representation to enhance the coherency between spatial information and local poses. Together with a new imputation formulation, the generated motion can reliably conform to spatial constraints such as global motion trajectories. Furthermore, given sparse spatial constraints (e.g. sparse keyframes), we introduce a new dense guidance approach to turn a sparse signal, which is susceptible to being ignored during the reverse steps, into denser signals to guide the generated motion to the given constraints. Our extensive experiments justify the development of \methodname, which achieves a significant improvement over state-of-the-art methods in text-based motion generation while allowing control of the synthesized motions with spatial constraints.
AffordPose: A Large-Scale Dataset of Hand-Object Interactions with Affordance-Driven Hand Pose
Authors: Juntao Jian, Xiuping Liu, Manyi Li, Ruizhen Hu, Jian Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of plausible and appropriate hand-object interactions. In this work, we present AffordPose, a large-scale dataset of hand-object interactions with affordance-driven hand pose. We first annotate the specific part-level affordance labels for each object, e.g. twist, pull, handle-grasp, etc, instead of the general intents such as use or handover, to indicate the purpose and guide the localization of the hand-object interactions. The fine-grained hand-object interactions reveal the influence of hand-centered affordances on the detailed arrangement of the hand poses, yet also exhibit a certain degree of diversity. We collect a total of 26.7K hand-object interactions, each including the 3D object shape, the part-level affordance label, and the manually adjusted hand poses. The comprehensive data analysis shows the common characteristics and diversity of hand-object interactions per affordance via the parameter statistics and contacting computation. We also conduct experiments on the tasks of hand-object affordance understanding and affordance-oriented hand-object interaction generation, to validate the effectiveness of our dataset in learning the fine-grained hand-object interactions. Project page: https://github.com/GentlesJan/AffordPose .
Locomotion-Action-Manipulation: Synthesizing Human-Scene Interactions in Complex 3D Environments
Authors: Jiye Lee, Hanbyul Joo
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Synthesizing interaction-involved human motions has been challenging due to the high complexity of 3D environments and the diversity of possible human behaviors within. We present LAMA, Locomotion-Action-MAnipulation, to synthesize natural and plausible long term human movements in complex indoor environments. The key motivation of LAMA is to build a unified framework to encompass a series of everyday motions including locomotion, scene interaction, and object manipulation. Unlike existing methods that require motion data "paired" with scanned 3D scenes for supervision, we formulate the problem as a test-time optimization by using human motion capture data only for synthesis. LAMA leverages a reinforcement learning framework coupled with motion matching algorithm for optimization, and further exploits a motion editing framework via manifold learning to cover possible variations in interaction and manipulation. Throughout extensive experiments, we demonstrate that LAMA outperforms previous approaches in synthesizing realistic motions in various challenging scenarios.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation
Authors: Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, Zhengguo Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep learning framework for monocular depth estimation by assuming that 3D scenes are constituted by piece-wise planes. Particularly, we introduce a new normal-distance head that outputs pixel-level surface normal and plane-to-origin distance for deriving depth at each position. Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint. We further integrate an additional depth head to improve the robustness of the proposed framework. To fully exploit the strengths of these two heads, we develop an effective contrastive iterative refinement module that refines depth in a complementary manner according to the depth uncertainty. Extensive experiments indicate that the proposed method exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI depth prediction online benchmark at the submission time. The source code is available at https://github.com/ShuweiShao/NDDepth.
Sequential Texts Driven Cohesive Motions Synthesis with Natural Transitions
Authors: Shuai Li, Sisi Zhuang, Wenfeng Song, Xinyu Zhang, Hejia Chen, Aimin Hao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The intelligent synthesis/generation of daily-life motion sequences is fundamental and urgently needed for many VR/metaverse-related applications. However, existing approaches commonly focus on monotonic motion generation (e.g., walking, jumping, etc.) based on single instruction-like text, which is still not intelligent enough and can't meet practical demands. To this end, we propose a cohesive human motion sequence synthesis framework based on free-form sequential texts while ensuring semantic connection and natural transitions between adjacent motions. At the technical level, we explore the local-to-global semantic features of previous and current texts to extract relevant information. This information is used to guide the framework in understanding the semantics of the current moment. Moreover, we propose learnable tokens to adaptively learn the influence range of the previous motions towards natural transitions. These tokens can be trained to encode the relevant information into well-designed transition loss. To demonstrate the efficacy of our method, we conduct extensive experiments and comprehensive evaluations on the public dataset as well as a new dataset produced by us. All the experiments confirm that our method outperforms the state-of-the-art methods in terms of semantic matching, realism, and transition fluency. Our project is public available. https://druthrie.github.io/sequential-texts-to-motion/
Efficient Converted Spiking Neural Network for 3D and 2D Classification
Authors: Yuxiang Lan, Yachao Zhang, Xu Ma, Yanyun Qu, Yun Fu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Spiking Neural Networks (SNNs) have attracted enormous research interest due to their low-power and biologically plausible nature. Existing ANN-SNN conversion methods can achieve lossless conversion by converting a well-trained Artificial Neural Network (ANN) into an SNN. However, converted SNN requires a large amount of time steps to achieve competitive performance with the well-trained ANN, which means a large latency. In this paper, we propose an efficient unified ANN-SNN conversion method for point cloud classification and image classification to significantly reduce the time step to meet the fast and lossless ANN-SNN transformation. Specifically, we first adaptively adjust the threshold according to the activation state of spiking neurons, ensuring a certain proportion of spiking neurons are activated at each time step to reduce the time for accumulation of membrane potential. Next, we use an adaptive firing mechanism to enlarge the range of spiking output, getting more discrimination features in short time steps. Extensive experimental results on challenging point cloud and image datasets demonstrate that the suggested approach significantly outmatches state-of-the-art ANN-SNN conversion based methods.
Eulerian Single-Photon Vision
Authors: Shantanu Gupta, Mohit Gupta
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Single-photon sensors measure light signals at the finest possible resolution -- individual photons. These sensors introduce two major challenges in the form of strong Poisson noise and extremely large data acquisition rates, which are also inherited by downstream computer vision tasks. Previous work has largely focused on solving the image reconstruction problem first and then using off-the-shelf methods for downstream tasks, but the most general solutions that account for motion are costly and not scalable to large data volumes produced by single-photon sensors. This work forgoes the image reconstruction problem. Instead, we demonstrate computationally light-weight phase-based algorithms for the tasks of edge detection and motion estimation. These methods directly process the raw single-photon data as a 3D volume with a bank of velocity-tuned filters, achieving speed-ups of more than two orders of magnitude compared to explicit reconstruction-based methods. Project webpage: https://wisionlab.com/project/eulerian-single-photon-vision/
Adaptive Calibrator Ensemble: Navigating Test Set Difficulty in Out-of-Distribution Scenarios
Authors: Yuli Zou, Weijian Deng, Liang Zheng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Model calibration usually requires optimizing some parameters (e.g., temperature) w.r.t an objective function like negative log-likelihood. This work uncovers a significant aspect often overlooked that the objective function is influenced by calibration set difficulty: the ratio of misclassified to correctly classified samples. If a test set has a drastically different difficulty level from the calibration set, a phenomenon out-of-distribution (OOD) data often exhibit: the optimal calibration parameters of the two datasets would be different, rendering an optimal calibrator on the calibration set suboptimal on the OOD test set and thus degraded calibration performance. With this knowledge, we propose a simple and effective method named adaptive calibrator ensemble (ACE) to calibrate OOD datasets whose difficulty is usually higher than the calibration set. Specifically, two calibration functions are trained, one for in-distribution data (low difficulty), and the other for severely OOD data (high difficulty). To achieve desirable calibration on a new OOD dataset, ACE uses an adaptive weighting method that strikes a balance between the two extreme functions. When plugged in, ACE generally improves the performance of a few state-of-the-art calibration schemes on a series of OOD benchmarks. Importantly, such improvement does not come at the cost of the in-distribution calibration performance. Project Website: https://github.com/insysgroup/Adaptive-Calibrators-Ensemble.git.
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study
Authors: Yuanyi Zhong, Haoran Tang, Jun-Kun Chen, Yu-Xiong Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Though self-supervised contrastive learning (CL) has shown its potential to achieve state-of-the-art accuracy without any supervision, its behavior still remains under investigated by academia. Different from most previous work that understands CL from learning objectives, we focus on an unexplored yet natural aspect: the spatial inductive bias which seems to be implicitly exploited via data augmentations in CL. We design an experiment to study the reliance of CL on such spatial inductive bias, by destroying the global or local spatial structures of image with global or local patch shuffling, and comparing the performance drop between experiments on original and corrupted dataset to quantify the reliance of certain inductive bias. We also use the uniformity of feature space to further research on how CL-pre-trained model behave with the corrupted dataset. Our results and analysis show that CL has a much higher reliance on spatial inductive bias than SL, regardless of specific CL algorithm or backbones, opening a new direction for studying the behavior of CL.
DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models
Authors: Weijia Wu, Yuzhong Zhao, Mike Zheng Shou, Hong Zhou, Chunhua Shen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In contrast, synthetic data can be freely available using a generative model (e.g., DALL-E, Stable Diffusion). In this paper, we show that it is possible to automatically obtain accurate semantic masks of synthetic images generated by the pre-trained Stable Diffusion, which uses only text-image pairs during training. Our approach, called DiffuMask, exploits the potential of the cross-attention map between text and image, which is natural and seamless to extend the text-driven
 image synthesis to semantic mask generation. DiffuMask uses text-guided cross-attention information to localize class/word-specific regions, which are combined with practical techniques to create a novel high-resolution and class-discriminative pixel-wise mask. The methods help to reduce data collection and annotation costs obviously. Experiments demonstrate that the existing segmentation methods trained on synthetic data of DiffuMask can achieve a
 competitive performance over the counterpart of real data (VOC 2012, Cityscapes). For some classes (e.g., bird), DiffuMask presents promising performance, close to the state-of-the-art result of real data (within 3% mIoU gap). Moreover, in the open-vocabulary segmentation (zero-shot) setting,
 DiffuMask achieves a new SOTA result on Unseen class of VOC 2012.
NSF: Neural Surface Fields for Human Modeling from Monocular Depth
Authors: Yuxuan Xue, Bharat Lal Bhatnagar, Riccardo Marin, Nikolaos Sarafianos, Yuanlu Xu, Gerard Pons-Moll, Tony Tung
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Obtaining personalized 3D animatable avatars from a monocular camera has several real world applications in gaming, virtual try-on, animation, and VR/XR, etc. However, it is very challenging to model dynamic and fine-grained clothing deformations from such sparse data. Existing methods for modeling 3D humans from depth data have limitations in terms of computational efficiency, mesh coherency, and flexibility in resolution and topology. For instance, reconstructing shapes using implicit functions and extracting explicit meshes per frame is computationally expensive and cannot ensure coherent meshes across frames. Moreover, predicting per-vertex deformations on a pre-designed human template with a discrete surface lacks flexibility in resolution and topology. To overcome these limitations, we propose a novel method 'NSF: Neural Surface Fields' for modeling 3D clothed humans from monocular depth. NSF defines a neural field solely on the base surface which models a continuous and flexible displacement field. NSF can be adapted to the base surface with different resolution and topology without retraining at inference time. Compared to existing approaches, our method eliminates the expensive per-frame surface extraction while maintaining mesh coherency, and is capable of reconstructing meshes with arbitrary resolution without retraining. To foster research in this direction, we release our code in project page at: https://yuxuan-xue.com/nsf.
Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
Authors: Abril Corona-Figueroa, Sam Bond-Taylor, Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon, Hubert P. H. Shum, Chris G. Willcocks
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks cannot achieve this unless they explicitly define both a domain-invariant and geometric-invariant joint latent distribution, whereas Neural Radiance Fields are generally unable to handle both issues as they optimize at the pixel level. By contrast, we propose a simple and novel 2D to 3D synthesis approach based on conditional diffusion with vector-quantized codes. Operating in an information-rich code space enables high-resolution 3D synthesis via full-coverage attention across the views. Specifically, we generate the 3D codes (e.g. for CT images) conditional on previously generated 3D codes and the entire codebook of two 2D views (e.g. 2D X-rays). Qualitative and quantitative results demonstrate state-of-the-art performance over specialized methods across varied evaluation criteria, including fidelity metrics such as density, coverage, and distortion metrics for two complex volumetric imagery datasets from in real-world scenarios.
DMNet: Delaunay Meshing Network for 3D Shape Representation
Authors: Chen Zhang, Ganzhangqin Yuan, Wenbing Tao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recently, there has been a growing interest in learning-based explicit methods due to their ability to respect the original input and preserve details. However, the connectivity on complex structures is still difficult to infer due to the limited local shape perception, resulting in artifacts and non-watertight triangles. In this paper, we present a novel learning-based method with Delaunay triangulation to achieve high-precision reconstruction. We model the Delaunay triangulation as a dual graph, extract local geometric information from the points, and embed it into the structural representation of Delaunay triangulation in an organic way, benefiting fine-grained details reconstruction. To encourage neighborhood information interaction of edges and nodes in the graph, we introduce a local graph iteration algorithm, which is a variant of graph neural network. Moreover, a geometric constraint loss further improves the classification of tetrahedrons. Benefiting from our fully local network, a scaling strategy is designed to enable large-scale reconstruction. Experiments show that our method yields watertight and high-quality meshes. Especially for some thin structures and sharp edges, our method shows better performance than the current state-of-the-art methods. Furthermore, it has a strong adaptability to point clouds of different densities.
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
Authors: Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches, etc.). While there are many methods that tackle this problem in different ways, there are still many important questions that remain unanswered. In this paper, we provide a systematic and in-depth analysis of the domain adaptation problem of GANs, focusing on the StyleGAN model. We perform a detailed exploration of the most important parts of StyleGAN that are responsible for adapting the generator to a new domain depending on the similarity between the source and target domains. As a result of this study, we propose new efficient and lightweight parameterizations of StyleGAN for domain adaptation. Particularly, we show that there exist directions in StyleSpace (StyleDomain directions) that are sufficient for adapting to similar domains. For dissimilar domains, we propose Affine+ and AffineLight+ parameterizations that allows us to outperform existing baselines in few-shot adaptation while having significantly less training parameters. Finally, we examine StyleDomain directions and discover their many surprising properties that we apply for domain mixing and cross-domain image morphing. Source code can be found at https://github.com/AIRI-Institute/StyleDomain.
RankMixup: Ranking-Based Mixup Training for Network Calibration
Authors: Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Network calibration aims to accurately estimate the level of confidences, which is particularly important for employing deep neural networks in real-world systems. Recent approaches leverage mixup to calibrate the network's predictions during training. However, they do not consider the problem that mixtures of labels in mixup may not accurately represent the actual distribution of augmented samples. In this paper, we present RankMixup, a novel mixup-based framework alleviating the problem of the mixture of labels for network calibration. To this end, we propose to use an ordinal ranking relationship between raw and mixup-augmented samples as an alternative supervisory signal to the label mixtures for network calibration. We hypothesize that the network should estimate a higher level of confidence for the raw samples than the augmented ones (Fig.1). To implement this idea, we introduce a mixup-based ranking loss (MRL) that encourages lower confidences for augmented samples compared to raw ones, maintaining the ranking relationship. We also propose to leverage the ranking relationship among multiple mixup-augmented samples to further improve the calibration capability. Augmented samples with larger mixing coefficients are expected to have higher confidences and vice versa (Fig.1). That is, the order of confidences should be aligned with that of mixing coefficients. To this end, we introduce a novel loss, M-NDCG, in order to reduce the number of misaligned pairs of the coefficients and confidences. Extensive experimental results on standard benchmarks for network calibration demonstrate the effectiveness of RankMixup.
Body Knowledge and Uncertainty Modeling for Monocular 3D Human Body Reconstruction
Authors: Yufei Zhang, Hanjing Wang, Jeffrey O. Kephart, Qiang Ji
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: While 3D body reconstruction methods have made remarkable progress recently, it remains difficult to acquire the sufficiently accurate and numerous 3D supervisions required for training. In this paper, we propose KNOWN, a framework that effectively utilizes body KNOWledge and uNcertainty modeling to compensate for insufficient 3D supervisions. KNOWN exploits a comprehensive set of generic body constraints derived from well-established body knowledge. These generic constraints precisely and explicitly characterize the reconstruction plausibility and enable 3D reconstruction models to be trained without any 3D data. Moreover, existing methods typically use images from multiple datasets during training, which can result in data noise (e.g., inconsistent joint annotation) and data imbalance (e.g., minority images representing unusual poses or captured from challenging camera views). KNOWN solves these problems through a novel probabilistic framework that models both aleatoric and epistemic uncertainty. Aleatoric uncertainty is encoded in a robust Negative Log-Likelihood (NLL) training loss, while epistemic uncertainty is used to guide model refinement. Experiments demonstrate that KNOWN's body reconstruction outperforms prior weakly-supervised approaches, particularly on the challenging minority images.
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Authors: Huimin Wu, Chenyang Lei, Xiao Sun, Peng-Shuai Wang, Qifeng Chen, Kwang-Ting Cheng, Stephen Lin, Zhirong Wu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques, data augmentation lies at the core for creating the information gap. Towards this end, masking has emerged as a generic and powerful tool where content is withheld along the sequential dimension, e.g., spatial in images, temporal in audio, and syntactic in language. In this paper, we explore the orthogonal channel dimension for generic data augmentation by exploiting precision redundancy. The data for each channel is quantized through a non-uniform quantizer, with the quantized value sampled randomly within randomly sampled quantization bins. From another perspective, quantization is analogous to channel-wise masking, as it removes the information within each bin, but preserves the information across bins. Our approach significantly surpasses existing generic data augmentation methods, while showing on par performance against modality- specific augmentations. We comprehensively evaluate our approach on vision, audio, 3D point clouds, as well as the DABS benchmark which is comprised of various data modalities. The code is available at https: //github.com/microsoft/random_quantize.