GenAI4ELab/papercli-papers huggingface dataset:
Query6DoF: Learning Sparse Queries as Implicit Shape Prior for Category-Level 6DoF Pose Estimation
Authors: Ruiqi Wang; Xinggang Wang; Te Li; Rong Yang; Minhong Wan; Wenyu Liu
Venue: ICCV 2023 (main)

Abstract: Category-level 6DoF object pose estimation intends to estimate the rotation, translation, and size of unseen objects. Many previous works use point clouds as a pre-learned shape prior to overcome intra-category variability. The shape prior is deformed to reconstruct instances' point clouds in canonical space and to build dense 3D-3D correspondences between the observed and reconstructed point clouds. However, the pre-learned shape prior is not jointly optimized with estimation networks, and they are trained with a surrogate objective. We propose a novel 6D pose estimation network, named Query6DoF, based on a series of category-specific sparse queries that represent the prior shape. Each query represents a shape component, and these queries are learnable embeddings that can be optimized together with the estimation network according to the point cloud reconstruction loss, the normalized object coordinate loss, and the 6d pose estimation loss. Query6DoF adopts a deformation-and-matching paradigm with attention, where the queries dynamically extract features from regions of interest using the attention mechanism and then directly regress results.
 Furthermore, Query6DoF reduces computation overhead through the sparseness of the queries and the incorporation of a lightweight global information injection block. With the aforementioned design, Query6DoF achieves state-of-the-art (SOTA) pose estimation performance on the NOCS datasets. The source code and models are available at https://github.com/hustvl/Query6DoF.
Towards High-Quality Specular Highlight Removal by Leveraging Large-Scale Synthetic Data
Authors: Gang Fu; Qing Zhang; Lei Zhu; Chunxia Xiao; Ping Li
Venue: ICCV 2023 (main)

Abstract: This paper aims to remove specular highlights from a single object-level image. Although previous methods have made some progresses, their performance remains somewhat limited, particularly for real images with complex specular highlights. To this end, we propose a three-stage network to address them. Specifically, given an input image, we first decompose it into the albedo, shading, and specular residue components to estimate a coarse specular-free image. Then, we further refine the coarse result to alleviate its visual artifacts such as color distortion. Finally, we adjust the tone of the refined result to match the tone of the input as closely as possible. In addition, to facilitate network training and quantitative evaluation, we present a large-scale synthetic dataset of object-level images, covering diverse objects and illumination conditions. Extensive experiments illustrate that our network is able to generalize well to unseen real object-level images, and even produce good results for scene-level images with multiple background objects and complex lighting.
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
Authors: Changjiang Li; Ren Pang; Zhaohan Xi; Tianyu Du; Shouling Ji; Yuan Yao; Ting Wang
Venue: ICCV 2023 (main)

Abstract: As a new paradigm in machine learning, self-supervised learning (SSL) is capable of learning high-quality representations of complex data without relying on labels. In addition to eliminating the need for labeled data, research has found that SSL improves the adversarial robustness over supervised learning since lacking labels makes it more challenging for adversaries to manipulate model predictions. However, the extent to which this robustness superiority generalizes to other types of attacks remains an open question. We explore this question in the context of backdoor attacks. Specifically, we design and evaluate CTRL, an embarrassingly simple yet highly effective self-supervised backdoor attack. By only polluting a tiny fraction of training data (<1%) with indistinguishable poisoning samples, CTRL causes any trigger-embedded input to be misclassified to the adversary's designated class with a high probability (>99%) at inference time. Our findings suggest that SSL and supervised learning are comparably vulnerable to backdoor attacks. More importantly, through the lens of CTRL, we study the inherent vulnerability of SSL to backdoor attacks. With both empirical and analytical evidence, we reveal that the representation invariance property of SSL, which benefits adversarial robustness, may also be the very reason making SSL highly susceptible to backdoor attacks. Our findings also imply that the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL.
Cross-Modal Translation and Alignment for Survival Analysis
Authors: Fengtao Zhou; Hao Chen
Venue: ICCV 2023 (main)

Abstract: With the rapid advances in high-throughput sequencing technologies, the focus of survival analysis has shifted from examining clinical indicators to incorporating genomic profiles with pathological images. However, existing methods either directly adopt a straightforward fusion of pathological features and genomic profiles for survival prediction, or take genomic profiles as guidance to integrate the features of pathological images. The former would overlook intrinsic cross-modal correlations. The latter would discard pathological information irrelevant to gene expression. To address these issues, we present a Cross-Modal Translation and Alignment (CMTA) framework to explore the intrinsic cross-modal correlations and transfer potential complementary information. Specifically, we construct two parallel encoder-decoder structures for multi-modal data to integrate intra-modal information and generate cross-modal representation. Taking the generated cross-modal representation to enhance and recalibrate intra-modal representation can significantly improve its discrimination for comprehensive survival analysis. To explore the intrinsic cross-modal correlations, we further design a cross-modal attention module as the information bridge between different modalities to perform cross-modal interactions and transfer complementary information. Our extensive experiments on five public TCGA datasets demonstrate that our proposed framework outperforms the state-of-the-art methods. The source code has been released.
Chaotic World: A Large and Challenging Benchmark for Human Behavior Understanding in Chaotic Events
Authors: Kian Eng Ong; Xun Long Ng; Yanchao Li; Wenjie Ai; Kuangyi Zhao; Si Yong Yeo; Jun Liu
Venue: ICCV 2023 (main)

Abstract: Understanding and analyzing human behaviors (actions and interactions of people), voices, and sounds in chaotic events is crucial in many applications, e.g., crowd management, emergency response services. Different from human behaviors in daily life, human behaviors in chaotic events are generally different in how they behave and influence others, and hence are often much more complex. However, currently there is lack of a large video dataset for analyzing human behaviors in chaotic situations. To this end, we create the first large and challenging multi-modal dataset, Chaotic World, that simultaneously provides different levels of fine-grained and dense spatio-temporal annotations of sounds, individual actions and group interaction graphs, and even text descriptions for each scene in each video, thereby enabling a thorough analysis of complicated behaviors in crowds and chaos. Our dataset consists of a total of 299,923 annotated instances for detecting human behaviors for Spatiotemporal Action Localization in chaotic events, 224,275 instances for identifying interactions between people for Behavior Graph Analysis in chaotic events, 336,390 instances for localizing relevant scenes of interest in long videos for Spatiotemporal Event Grounding, and 378,093 instances for triangulating the source of sound for Event Sound Source Localization. Given the practical complexity and challenges in chaotic events (e.g., large crowds, serious occlusions, complicated interaction patterns), our dataset shall be able to facilitate the community to develop, adapt, and evaluate various types of advanced models for analyzing human behaviors in chaotic events. We also design a simple yet effective IntelliCare model with a Dynamic Knowledge Pathfinder module that intelligently learns from multiple tasks and can analyze various aspects of a chaotic scene in a unified architecture. This method achieves promising results in experiments. Dataset and code can be found at https://github.com/sutdcv/Chaotic-World
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving
Authors: Mahyar Najibi; Jingwei Ji; Yin Zhou; Charles R. Qi; Xinchen Yan; Scott Ettinger; Dragomir Anguelov
Venue: ICCV 2023 (main)

Abstract: Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object types can be encountered after deployment. In this paper, we present a multi-modal auto labeling pipeline capable of generating amodal 3D bounding boxes and tracklets for training models on open-set categories without 3D human labels. Our pipeline exploits motion cues inherent in point cloud sequences in combination with the freely available 2D image-text pairs to identify and track all traffic participants. Compared to the recent studies in this domain, which can only provide class-agnostic auto labels limited to moving objects, our method can handle both static and moving objects in the unsupervised manner and is able to output open-vocabulary semantic labels thanks to the proposed vision-language knowledge distillation. Experiments on the Waymo Open Dataset show that our approach outperforms the prior work by significant margins on various unsupervised 3D perception tasks.
Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification
Authors: Bin Yang; Jun Chen; Mang Ye
Venue: ICCV 2023 (main)

Abstract: Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) is an extremely important and challenging task, which can alleviate the issue of expensive cross-modality annotations. Existing works focus on handling the cross-modality discrepancy under unsupervised conditions. However, they ignore the fact that USL-VI-ReID is a cross-modality retrieval task with the hierarchical discrepancy, i.e., camera variation and modality discrepancy, resulting in clustering inconsistencies and ambiguous cross-modality label association. To address these issues, we propose a hierarchical framework to learn grand unified representation (GUR) for USL-VI-ReID. The grand unified representation lies in two aspects: 1) GUR adopts a bottom-up domain learning strategy with a cross-memory association embedding module to explore the information of hierarchical domains, i.e., intra-camera, inter-camera, and inter-modality domains, learning a unified and robust representation against hierarchical discrepancy. 2) To unify the identities of the two modalities, we develop a cross-modality label unification module that constructs a cross-modality affinity matrix as a bridge for propagating labels between two modalities. Then, we utilize the homogeneous structure matrix to smooth the propagated labels, ensuring that the label structure within one modality remains unchanged. Extensive experiments demonstrate that our GUR framework significantly outperforms existing USL-VI-ReID methods, and even surpasses some supervised counterparts.
Active Stereo Without Pattern Projector
Authors: Luca Bartolomei; Matteo Poggi; Fabio Tosi; Andrea Conti; Stefano Mattoccia
Venue: ICCV 2023 (main)

Abstract: This paper proposes a novel framework integrating the principles of active stereo in standard passive camera systems without a physical pattern projector. We virtually project a pattern over the left and right images according to the sparse measurements obtained from a depth sensor. Any such devices can be seamlessly plugged into our framework, allowing for the deployment of a virtual active stereo setup in any possible environment, overcoming the limitation of pattern projectors, such as limited working range or environmental conditions. Experiments on indoor/outdoor datasets, featuring both long and close-range, support the seamless effectiveness of our approach, boosting the accuracy of both stereo algorithms and deep networks.
Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis
Authors: Ke Liu; Feng Liu; Haishuai Wang; Ning Ma; Jiajun Bu; Bo Han
Venue: ICCV 2023 (main)

Abstract: Implicit neural representations (INRs) aim to learn a continuous function (i.e., a neural network) to represent an image, where the input and output of the function are pixel coordinates and RGB/Gray values, respectively. However, images tend to consist of many objects whose colors are not perfectly consistent, resulting in the challenge that image is actually a discontinuous piecewise function and cannot be well estimated by a continuous function. In this paper, we empirically investigate that if a neural network is enforced to fit a discontinuous piecewise function to reach a fixed small error, the time costs will increase exponentially with respect to the boundaries in the spatial domain of the target signal. We name this phenomenon the exponential-increase hypothesis. Under the exponential-increase hypothesis, learning INRs for images with many objects will converge very slowly. To address this issue, we first prove that partitioning a complex signal into several sub-regions and utilizing piecewise INRs to fit that signal can significantly speed up the convergence. Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs. In both cases, we partition an image into different sub-regions and dedicate smaller networks for each part. In addition, we further propose two partition rules based on regular grids and semantic segmentation maps, respectively. Extensive experiments validate the effectiveness of the proposed partitioning methods in terms of learning INR for a single image (ordinary learning framework) and the learning-to-learn framework.
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
Authors: Junpeng Jing; Jiankun Li; Pengfei Xiong; Jiangyu Liu; Shuaicheng Liu; Yichen Guo; Xin Deng; Mai Xu; Lai Jiang; Leonid Sigal
Venue: ICCV 2023 (main)

Abstract: Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed trained model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.
ReFit: Recurrent Fitting Network for 3D Human Recovery
Authors: Yufu Wang; Kostas Daniilidis
Venue: ICCV 2023 (main)

Abstract: We present Recurrent Fitting (ReFit), a neural network architecture for single-image, parametric 3D human reconstruction. ReFit learns a feedback-update loop that mirrors the strategy of solving an inverse problem through optimization. At each iterative step, it reprojects keypoints from the human model to feature maps to query feedback, and uses a recurrent-based updater to adjust the model to fit the image better. Because ReFit encodes strong knowledge of the inverse problem, it is faster to train than previous regression models. At the same time, ReFit improves state-of-the-art performance on standard benchmarks. Moreover, ReFit applies to other optimization settings, such as multi-view fitting and single-view shape fitting. Project website: https://yufu-wang.github.io/refit_humans/
Towards Instance-adaptive Inference for Federated Learning
Authors: Chun-Mei Feng; Kai Yu; Nian Liu; Xinxing Xu; Salman Khan; Wangmeng Zuo
Venue: ICCV 2023 (main)

Abstract: Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. However, the performance of the global model is often hampered by non-i.i.d. distribution among the clients, requiring extensive efforts to mitigate inter-client data heterogeneity. Going beyond inter-client data heterogeneity, we note that intra-client heterogeneity can also be observed on complex real-world data and seriously deteriorate FL performance. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Instead of huge instance-adaptive models, we resort to a parameter-efficient fine-tuning method, i.e., scale and shift deep features (SSF), upon a pre-trained model. Specifically, we first train an SSF pool for each client, and aggregate these SSF pools on the server side, thus still maintaining a low communication cost. To enable instance-adaptive inference, for a given instance, we dynamically find the best-matched SSF subsets from the pool and aggregate them to generate an adaptive SSF specified for the instance, thereby reducing the intra-client as well as the inter-client heterogeneity. Extensive experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
CGBA: Curvature-aware Geometric Black-box Attack
Authors: Md Farhamdur Reza; Ali Rahmati; Tianfu Wu; Huaiyu Dai
Venue: ICCV 2023 (main)

Abstract: Decision-based black-box attacks often necessitate a large number of queries to craft an adversarial example. Moreover, decision-based attacks based on querying boundary points in the estimated normal vector direction often suffer from inefficiency and convergence issues. In this paper, we propose a novel query-efficient curvature-aware geometric decision-based black-box attack (CGBA) that conducts boundary search along a semicircular path on a restricted 2D plane to ensure finding a boundary point successfully irrespective of the boundary curvature. While the proposed CGBA attack can work effectively for an arbitrary decision boundary, it is particularly efficient in exploiting the low curvature to craft high-quality adversarial examples, which is widely seen and experimentally verified in commonly used classifiers under non-targeted attacks. In contrast, the decision boundaries often exhibit higher curvature under targeted attacks. Thus, we develop a new query-efficient variant, CGBA-H, that is adapted for the targeted attack. In addition, we further design an algorithm to obtain a better initial boundary point at the expense of some extra queries, which considerably enhances the performance of the targeted attack. Extensive experiments are conducted to evaluate the performance of our proposed methods against some well-known classifiers on the ImageNet and CIFAR10 datasets, demonstrating the superiority of CGBA and CGBA-H over state-of-the-art non-targeted and targeted attacks, respectively. The source code is available at https://github.com/Farhamdur/CGBA.
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations
Authors: Berkay Kicanaoglu; Pablo Garrido; Gaurav Bharaj
Venue: ICCV 2023 (main)

Abstract: High-fidelity virtual human avatar applications create a need for photorealistic video face synthesis with controllable semantic editing over facial features. While recent generative neural methods have shown significant progress in portrait video synthesis, intuitive facial control, e.g., of mouth interior and gaze at different levels of details, remains a challenge. In this work, we present a novel face editing framework that combines a 3D face model with StyleGAN vector-quantization to learn multi-level semantic facial control. We show that vector quantization of StyleGAN features unveils richer semantic facial representations, e.g., teeth and pupils, which are difficult to model with 3D tracking priors. Such representations along with 3D tracking can be used as self-supervision to train a generator with control over coarse expressions and finer facial attributes. Learned representations can be combined with user-defined masks to create semantic segmentations that act as custom detail handles for semantic-aware video editing. Our formulation allows video face manipulation with precise local control over facial attributes, such as eyes and teeth, opening up a number of face reenactment and visual expression articulation applications.
Online Clustered Codebook
Authors: Chuanxia Zheng; Andrea Vedaldi
Venue: ICCV 2023 (main)

Abstract: Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is increasingly used in representation learning. However, optimizing the codevectors in existing VQ-VAE is not entirely trivial. A problem is codebook collapse, where only a small subset of codevectors receive gradients useful for their optimization, whereas a majority of them simply "dies off" and is never updated or used. This limits the effectiveness of VQ for learning larger codebooks in complex computer vision tasks that require high-capacity representations. In this paper, we present a simple alternative method for online codebook learning, Clustering VQ-VAE (CVQ-VAE).
 Our approach selects encoded features as anchors to update the "dead" codevectors, while optimizing the codebooks which are alive via the original loss. This strategy brings unused codevectors closer in distribution to the encoded features, increasing the likelihood of being chosen and optimized. We extensively validate the generalization capability of our quantizer on various datasets, tasks (e.g., reconstruction and generation), and architectures (e.g., VQ-VAE, VQGAN, LDM). CVQ-VAE can be easily integrated into the existing models with just a few lines of code.
A Multidimensional Analysis of Social Biases in Vision Transformers
Authors: Jannik Brinkmann; Paul Swoboda; Christian Bartelt
Venue: ICCV 2023 (main)

Abstract: The embedding spaces of image models have been shown to encode a range of social biases such as racism and sexism. Here, we investigate specific factors that contribute to the emergence of these biases in Vision Transformers (ViT). Therefore, we measure the impact of training data, model architecture, and training objectives on social biases in the learned representations of ViTs. Our findings indicate that counterfactual augmentation training using diffusion-based image editing can mitigate biases, but does not eliminate them. Moreover, we find that larger models are less biased than smaller models, and that models trained using discriminative objectives are less biased than those trained using generative objectives. In addition, we observe inconsistencies in the learned social biases. To our surprise, ViTs can exhibit opposite biases when trained on the same data set using different self-supervised objectives. Our findings give insights into the factors that contribute to the emergence of social biases and suggests that we could achieve substantial fairness improvements based on model design choices.