GenAI4ELab/papercli-papers huggingface dataset:
Panoramas from Photons
Authors: Sacha Jungerman; Atul Ingle; Mohit Gupta
Venue: ICCV 2023 (main)

Abstract: Scene reconstruction in the presence of high-speed motion and low illumination is important in many applications such as augmented and virtual reality, drone navigation, and autonomous robotics. Traditional motion estimation techniques fail in such conditions, suffering from too much blur in the presence of high-speed motion and strong noise in low-light conditions. Single-photon cameras have recently emerged as a promising technology capable of capturing hundreds of thousands of photon frames per second thanks to their high speed and extreme sensitivity. Unfortunately, traditional computer vision techniques are not well suited for dealing with the binary-valued photon data captured by these cameras because these are corrupted by extreme Poisson noise. Here we present a method capable of estimating extreme scene motion under challenging conditions, such as low light or high dynamic range, from a sequence of high-speed image frames such as those captured by a single-photon camera. Our method relies on iteratively improving a motion estimate by grouping and aggregating frames after-the-fact, in a stratified manner. We demonstrate the creation of high-quality panoramas under fast motion and extremely low light, and super-resolution results using a custom single-photon camera prototype.
Global Adaptation Meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation
Authors: Wenhao Chai; Zhongyu Jiang; Jenq-Neng Hwang; Gaoang Wang
Venue: ICCV 2023 (main)

Abstract: When applying a pre-trained 2D-to-3D Human Pose lifting model to a target unseen dataset, a large performance degradation is commonly encountered due to domain shift issues. We observe that the degradation is caused by two factors: 1) the large distribution gap over global positions of poses between the source and target datasets due to variant camera parameters and settings, and 2) the deficient diversity of local structures of poses in the training. To this end, we combine global adaptation and local generalization in PoseDA, a simple yet effective framework of unsupervised domain adaptation for 3D human pose estimation. Specifically, global adaptation aims to align global positions of poses from source domain to target domain with a proposed global position alignment (GPA) module. This module brings significant performance improvement without introducing additional learnable parameters. In addition, we propose local pose augmentation (LPA) to enhance the diversity of 3D poses following an adversarial training scheme consisting of 1) a augmentation generator that generates the parameters of pre-defined pose transformations and 2) an anchor discriminator to ensure the reality and quality of the augmented data. Our approach can be applicable to almost all 2D-3D lifting models. PoseDA achieves 61.3 mm of MPJPE on MPI-INF-3DHP under a cross-dataset evaluation setup, improving upon the previous state-of-the-art method by 10.2%.
Learning Neural Implicit Surfaces with Object-Aware Radiance Fields
Authors: Yiheng Zhang; Zhaofan Qiu; Yingwei Pan; Ting Yao; Tao Mei
Venue: ICCV 2023 (main)

Abstract: Recent progress on multi-view 3D object reconstruction has featured neural implicit surfaces via learning high-fidelity radiance fields. However, most approaches hinge on the visual hull derived from cost-expensive silhouette masks to obtain object surfaces. In this paper, we propose a novel Object-aware Radiance Fields (ORF) to automatically learn an object-aware geometry reconstruction. The geometric correspondences between multi-view 2D object regions and 3D implicit/explicit object surfaces are additionally exploited to boost the learning of object surfaces. Technically, a critical transparency discriminator is designed to distinguish the object-intersected and object-bypassed rays based on the estimated 2D object regions, leading to 3D implicit object surfaces. Such implicit surfaces can be directly converted into explicit object surfaces (e.g., meshes) via marching cubes. Then, we build the geometric correspondence between 2D planes and 3D meshes by rasterization, and project the estimated object regions into 3D explicit object surfaces by aggregating the object information across multiple views. The aggregated object information in 3D explicit object surfaces is further reprojected back to 2D planes, aiming to update 2D object regions and enforce them to be multi-view consistent. Extensive experiments on DTU and BlendedMVS verify the capability of ORF to produce comparable surfaces against the state-of-the-art models that demand silhouette masks.
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation
Authors: Zhengfeng Lai; Noranart Vesdapunt; Ning Zhou; Jun Wu; Cong Phuoc Huynh; Xuelu Li; Kah Kuen Fu; Chen-Nee Chuah
Venue: ICCV 2023 (main)

Abstract: Traditional Unsupervised Domain Adaptation (UDA) leverages the labeled source domain to tackle the learning tasks on the unlabeled target domain. It can be more challenging when a large domain gap exists between the source and the target domain. A more practical setting is to utilize a large-scale pre-trained model to fill the domain gap. For example, CLIP shows promising zero-shot generalizability to bridge the gap. However, after applying traditional fine-tuning to specifically adjust CLIP on a target domain, CLIP suffers from catastrophic forgetting issues where the new domain knowledge can quickly override CLIP's pre-trained knowledge and decreases the accuracy by half. We propose Catastrophic Forgetting Measurement (CFM) to adjust the learning rate to avoid excessive training (thus mitigating the catastrophic forgetting issue). We then utilize CLIP's zero-shot prediction to formulate a Pseudo-labeling setting with Adaptive Debiasing in CLIP (PADCLIP) by adjusting causal inference with our momentum and CFM. Our PADCLIP allows end-to-end training on source and target domains without extra overhead, and we achieved the best results on four public datasets, with a significant improvement (+18.5% accuracy) on DomainNet.
Causal-DFQ: Causality Guided Data-Free Network Quantization
Authors: Yuzhang Shang; Bingxin Xu; Gaowen Liu; Ramana Rao Kompella; Yan Yan
Venue: ICCV 2023 (main)

Abstract: Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devices. There is a common assumption in quantization methods from prior works that training data is available. In practice, however, this assumption cannot always be fulfilled due to reasons of privacy and security, rendering these methods inapplicable in real-life situations. 
 Thus, data-free network quantization has recently received significant attention in neural network compression. Causal reasoning provides an intuitive way to model casual relationships to eliminate data-driven correlations, making causality an essential component of analyzing data-free problems. However, causal formulations of data-free quantization are inadequate in the literature. To bridge this gap, we construct a causal graph to model the data generation and discrepancy reduction between the pre-trained and quantized models. Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions. Specifically, we design a content-style-decoupled generator, synthesizing images conditioned on the relevant and irrelevant factors; then we propose a discrepancy reduction loss to align the intervened distributions of the pre-trained and quantized models. It is worth noting that our work is the first attempt towards introducing causality to data-free quantization problem. Extensive experiments demonstrate the efficacy of Causal-DFQ.
Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
Authors: Xuannan Liu; Yaoyao Zhong; Yuhang Zhang; Lixiong Qin; Weihong Deng
Venue: ICCV 2023 (main)

Abstract: Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to instance-specific adversarial examples, UAP is more challenging as it needs to generalize across various samples and models. In this paper, we examine the serious dilemma of UAP generation methods from a generalization perspective -- the gradient vanishing problem using small-batch stochastic gradient optimization and the local optima problem using large-batch optimization. To address these problems, we propose a simple and effective method called Stochastic Gradient Aggregation (SGA), which alleviates the gradient vanishing and escapes from poor local optima at the same time. Specifically, SGA employs the small-batch training to perform multiple iterations of inner pre-search. Then, all the inner gradients are aggregated as a one-step gradient estimation to enhance the gradient stability and reduce quantization errors. Extensive experiments on the standard ImageNet dataset demonstrate that our method significantly enhances the generalization ability of UAP and outperforms other state-of-the-art methods. The code is available at https://github.com/liuxuannan/Stochastic-Gradient-Aggregation.
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Authors: Jieneng Chen; Yingda Xia; Jiawen Yao; Ke Yan; Jianpeng Zhang; Le Lu; Fakai Wang; Bo Zhou; Mingyan Qiu; Qihang Yu; Mingze Yuan; Wei Fang; Yuxing Tang; Minfeng Xu; Jian Zhou; Yuqian Zhao; Qifeng Wang; Xianghua Ye; Xiaoli Yin; Yu Shi; Xin Chen; Jingren Zhou; Alan Yuille; Zaiyi Liu; Ling Zhang
Venue: ICCV 2023 (main)

Abstract: Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.
Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan
Authors: Yongheng Sun; Fan Wang; Jun Shu; Haifeng Wang; Li Wang; Deyu Meng; Chunfeng Lian
Venue: ICCV 2023 (main)

Abstract: Brain tissue segmentation is essential for neuroscience and clinical studies. However, segmentation on longitudinal data is challenging due to dynamic brain changes across the lifespan. Previous researches mainly focus on self-supervision with regularizations and will lose longitudinal generalization when fine-tuning on a specific age group. In this paper, we propose a dual meta-learning paradigm to learn longitudinally consistent representations and persist when fine-tuning. Specifically, we learn a plug-and-play feature extractor to extract longitudinal-consistent anatomical representations by meta-feature learning and a well-initialized task head for fine-tuning by meta-initialization learning. Besides, two class-aware regularizations are proposed to encourage longitudinal consistency. Experimental results on the iSeg2019 and ADNI datasets demonstrate the effectiveness of our method.
DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Authors: Di Liu; Xiang Yu; Meng Ye; Qilong Zhangli; Zhuowei Li; Zhixing Zhang; Dimitris N. Metaxas
Venue: ICCV 2023 (main)

Abstract: Explicit 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics. By leveraging a set of primitives to represent the target shape, recent methods have achieved promising results. However, these methods either use a relatively larger number of primitives or lack geometric flexibility due to the low-dimensional expressibility of the primitives. In this paper, we propose a novel bi-channel Transformer architecture, integrated with parameterized deformable models, termed DeFormer, to simultaneously estimate global and local deformations. In this way, DeFormer can abstract complex object shapes while using a small number of primitives which offer a broader geometry coverage and finer details. Then, we introduce a force-driven dynamic fitting and a cycle-consistent re-projection loss to optimize the primitive parameters. Extensive experiments on ShapeNet across various settings show that DeFormer achieves better reconstruction accuracy over the state-of-the-art, and visualizes with consistent semantic correspondences for improved interpretability.
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition
Authors: Xingyu Liu; Sanping Zhou; Le Wang; Gang Hua
Venue: ICCV 2023 (main)

Abstract: Learning discriminative features from very few labeled samples to identify novel classes has received increasing attention in skeleton-based action recognition. Existing works aim to learn action-specific embeddings by exploiting either intra-skeleton or inter-skeleton spatial associations, which may lead to less discriminative representations. To address these issues, we propose a novel Parallel Attention Interaction Network (PAINet) that incorporates two complementary branches to strengthen the match by inter-skeleton and intra-skeleton correlation. Specifically, a topology encoding module utilizing topology and physical information is proposed to enhance the modeling of interactive parts and joint pairs in both branches. In the Cross Spatial Alignment branch, we employ a spatial cross-attention module to establish joint associations across sequences, and a directional Average Symmetric Surface Metric is introduced to locate the closest temporal similarity. In parallel, the Cross Temporal Alignment branch incorporates a spatial self-attention module to aggregate spatial context within sequences as well as applies the temporal cross-attention network to correct misalignment temporally and calculate similarity. Extensive experiments on three skeleton benchmarks, namely NTU-T, NTU-S, and Kinetics, demonstrate the superiority of our framework and consistently outperform state-of-the-art methods.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Authors: Wenjie Yang; Yiyi Chen; Yan Li; Yanhua Cheng; Xudong Liu; Quan Chen; Han Li
Venue: ICCV 2023 (main)

Abstract: Live commerce is the act of selling products online through livestreaming. The customer's diverse demands for online products introduces more challenges to Livestreaming Product Recognition. Previous works are either focus on fashion clothing data or subject to single-modal input, thus inconsistent with the real-world scenario where multimodal data from various categories are present. In this paper, we contribute LPR4M, a large-scale multimodal dataset that covers 34 categories, comprises 3 modalities (image, video, and text), and is 50 times larger than the largest publicly available dataset. In addition, LPR4M contains diverse videos and noise modality pair while also having a long-tailed distribution, resembling real-world problems. Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the two views (image and video) of products via instance-level contrastive learning as well as cross-view patch-level feature propagation. A novel Patch Feature Reconstruction loss is proposed to penalize the semantic misalignment between the cross-view patches. Extensive ablation studies demonstrate the effectiveness of RICE and provide insights into the importance of dataset diversity and expressivity.
Continuously Masked Transformer for Image Inpainting
Authors: Keunsoo Ko; Chang-Su Kim
Venue: ICCV 2023 (main)

Abstract: A novel continuous-mask-aware transformer for image inpainting, called CMT, is proposed in this paper, which uses a continuous mask to represent the amounts of errors in tokens. First, we initialize a mask and use it during the self-attention. To facilitate the masked self-attention, we also introduce the notion of overlapping tokens. Second, we update the mask by modeling the error propagation during the masked self-attention. Through several masked self-attention and mask update (MSAU) layers, we predict initial inpainting results. Finally, we refine the initial results to reconstruct a more faithful image. Experimental results on multiple datasets show that the proposed CMT algorithm outperforms existing algorithms significantly. The source codes are available at https://github.com/keunsoo-ko/CMT.
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction
Authors: Rémi Pautrat; Shaohui Liu; Petr Hruby; Marc Pollefeys; Daniel Barath
Venue: ICCV 2023 (main)

Abstract: We tackle the problem of estimating a Manhattan frame, i.e. three orthogonal vanishing points, and the unknown focal length of the camera, leveraging a prior vertical direction. The direction can come from an Inertial Measurement Unit that is a standard component of recent consumer devices, e.g., smartphones. We provide an exhaustive analysis of minimal line configurations and derive two new 2-line solvers, one of which does not suffer from singularities affecting existing solvers. Additionally, we design a new non-minimal method, running on an arbitrary number of lines, to boost the performance in local optimization. Combining all solvers in a hybrid robust estimator, our method achieves increased accuracy even with a rough prior. Experiments on synthetic and real-world datasets demonstrate the superior accuracy of our method compared to the state of the art, while having comparable runtimes. We further demonstrate the applicability of our solvers for relative rotation estimation. The code is available at https://github.com/cvg/VP-Estimation-with-Prior-Gravity.
Learn TAROT with MENTOR: A Meta-Learned Self-Supervised Approach for Trajectory Prediction
Authors: Mozhgan Pourkeshavarz; Changhe Chen; Amir Rasouli
Venue: ICCV 2023 (main)

Abstract: Predicting diverse yet admissible trajectories that adhere to the map constraints is challenging. Graph-based scene encoders have been proven effective for preserving local structures of maps by defining lane-level connections. However, such encoders do not capture more complex patterns emerging from long-range heterogeneous connections between nonadjacent interacting lanes. To this end, we shed new light on learning common driving patterns by introducing meTA ROad paTh (TAROT) to formulate combinations of various relations between lanes on the road topology. Intuitively, this can be viewed as finding feasible routes. Furthermore, we propose MEta-road NeTwORk (MENTOR) that helps trajectory prediction by providing it with TAROT as navigation tips. More specifically, 1) we define TAROT prediction as a novel self-supervised proxy task to identify the complex heterogeneous structure of the map. 2) For typical driving actions, we establish several TAROTs that result in multiple Heterogeneous Structure Learning (HSL) tasks. These tasks are used in MENTOR, which performs meta-learning by simultaneously predicting trajectories along with proxy tasks, identifying an optimal combination of them, and automatically balancing them to improve the primary task. We show that our model achieves state-of-the-art performance on the Argoverse dataset, especially on diversity and admissibility metrics, achieving up to 20% improvements in challenging scenarios. We further investigate the contribution of proposed modules in ablation studies.
MatrixVT: Efficient Multi-Camera to BEV Transformation for 3D Perception
Authors: Hongyu Zhou; Zheng Ge; Zeming Li; Xiangyu Zhang
Venue: ICCV 2023 (main)

Abstract: This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation method for 3D perception, dubbed MatrixVT.
 Existing view transformers either suffer from poor transformation efficiency or rely on device-specific operators, hindering the broad application of BEV models. In contrast, our method generates BEV features efficiently with only convolutions and matrix multiplications (MatMul). Specifically, we propose describing the BEV feature as the MatMul of image feature and a sparse Feature Transporting Matrix (FTM). A Prime Extraction module is then introduced to compress the dimension of image features and reduce FTM's sparsity. Moreover, we propose the Ring & Ray Decomposition to replace the FTM with two matrices and reformulate our pipeline to reduce calculation further. Compared to existing methods, MatrixVT enjoys a faster speed and less memory footprint while remaining deploy-friendly. Extensive experiments on the nuScenes benchmark demonstrate that our method is highly efficient but obtains results on par with the SOTA method in object detection and map segmentation tasks. Code will be available.
Local and Global Logit Adjustments for Long-Tailed Learning
Authors: Yingfan Tao; Jingna Sun; Hao Yang; Li Chen; Xu Wang; Wenming Yang; Daniel Du; Min Zheng
Venue: ICCV 2023 (main)

Abstract: Multi-expert ensemble models for long-tailed learning typically either learn diverse generalists from the whole dataset or aggregate specialists on different subsets. However, the former is insufficient for tail classes due to the high imbalance factor of the entire dataset, while the latter may bring ambiguity in predicting unseen classes. To address these issues, we propose a novel Local and Global Logit Adjustments (LGLA) method that learns experts with full data covering all classes and enlarges the discrepancy among them by elaborated logit adjustments. LGLA consists of two core components: a Class-aware Logit Adjustment (CLA) strategy and an Adaptive Angular Weighted (AAW) loss. The CLA strategy trains multiple experts which excel at each subset using the Local Logit Adjustment (LLA). It also trains one expert specializing in an inversely long-tailed distribution through Global Logit Adjustment (GLA). Moreover, the AAW loss adopts adaptive hard sample mining with respect to different experts to further improve accuracy. Extensive experiments on popular long-tailed benchmarks manifest the superiority of LGLA over the SOTA methods.