mtybilly/Instruct2DS huggingface dataset:
Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models
Authors: Jason J. Yu, Fereshteh Forghani, Konstantinos G. Derpanis, Marcus A. Brubaker
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Novel view synthesis from a single input image is a challenging task, where the goal is to generate a new view of a scene from a desired camera pose that may be separated by a large motion. The highly uncertain nature of this synthesis task due to unobserved elements within the scene (i.e. occlusion) and outside the field-of-view makes the use of generative models appealing to capture the variety of possible outputs. In this paper, we propose a novel generative model capable of producing a sequence of photorealistic images consistent with a specified camera trajectory, and a single starting image. Our approach is centred on an autoregressive conditional diffusion-based model capable of interpolating visible scene elements, and extrapolating unobserved regions in a view, in a geometrically consistent manner. Conditioning is limited to an image capturing a single camera view and the (relative) pose of the new camera view. To measure the consistency over a sequence of generated views, we introduce a new metric, the thresholded symmetric epipolar distance (TSED), to measure the number of consistent frame pairs in a sequence. While previous methods have been shown to produce high quality images and consistent semantics across pairs of views, we show empirically with our metric that they are often inconsistent with the desired camera poses. In contrast, we demonstrate that our method produces both photorealistic and view-consistent imagery. Additional material is available on our project page: https://yorkucvil.github.io/Photoconsistent-NVS/.
What Can a Cook in Italy Teach a Mechanic in India? Action Recognition Generalisation Over Scenarios and Locations
Authors: Chiara Plizzari, Toby Perrett, Barbara Caputo, Dima Damen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose and address a new generalisation problem: can a model trained for action recognition successfully classify actions when they are performed within a previously unseen scenario and in a previously unseen location? To answer this question, we introduce the Action Recognition Generalisation Over scenarios and locations dataset ARGO1M, which contains 1.1M video clips from the large-scale Ego4D dataset, across 10 scenarios and 13 locations. We demonstrate recognition models struggle to generalise over 10 proposed test splits, each of an unseen scenario in an unseen location. We thus propose CIR, a method to represent each video as a Cross-Instance Reconstruction of videos from other domains. Reconstructions are paired with text narrations to guide the learning of a domain generalisable representation. We provide extensive analysis and ablations on ARGO1M that show CIR outperforms prior domain generalisation works on all test splits.
EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild
Authors: Manuel Kaufmann, Jie Song, Chen Guo, Kaiyue Shen, Tianjian Jiang, Chengcheng Tang, Juan José Zárate, Otmar Hilliges
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present EMDB, the Electromagnetic Database of Global 3D Human Pose and Shape in the Wild. EMDB is a novel dataset that contains high-quality 3D SMPL pose and shape parameters with global body and camera trajectories for in-the-wild videos. We use body-worn, wireless electromagnetic (EM) sensors and a hand-held iPhone to record a total of 58 minutes of motion data, distributed over 81 indoor and outdoor sequences and 10 participants. Together with accurate body poses and shapes, we also provide global camera poses and body root trajectories. To construct EMDB, we propose a multi-stage optimization procedure, which first fits SMPL to the 6-DoF EM measurements and then refines the poses via image observations. To achieve high-quality results, we leverage a neural implicit avatar model to reconstruct detailed human surface geometry and appearance, which allows for improved alignment and smoothness via a dense pixel-level objective. Our evaluations, conducted with a multi-view volumetric capture system, indicate that EMDB has an expected accuracy of 2.3 cm positional and 10.6 degrees angular error, surpassing the accuracy of previous in-the-wild datasets. We evaluate existing state-of-the-art monocular RGB methods for camera-relative and global pose estimation on EMDB. EMDB is publicly available under https://ait.ethz.ch/emdb.
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning
Authors: Tao Han, Lei Bai, Lingbo Liu, Wanli Ouyang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms. An important factor is that they typically involve cooperative learning across multi-resolutions, which could be suboptimal for learning the most discriminative features from each scale. In this paper, we propose a novel method termed STEERER (SelecTivE inhERitance lEaRning) that addresses the issue of scale variations in object counting. STEERER selects the most suitable scale for patch objects to boost feature extraction and only inherits discriminative features from lower to higher resolution progressively. The main insights of STEERER are a dedicated Feature Selection and Inheritance Adaptor (FSIA), which selectively forwards scale-customized features at each scale, and a Masked Selection and Inheritance Loss (MSIL) that helps to achieve high-quality density maps across all scales. Our experimental results on nine datasets with counting and localization tasks demonstrate the unprecedented scale generalization ability of STEERER. Code is available at https://github.com/taohan10200/STEERER.
Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation
Authors: Hao Liang, Pietro Perona, Guha Balakrishnan
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose an experimental method for measuring bias in face recognition systems. Existing methods to measure bias depend on benchmark datasets that are collected in the wild and annotated for protected (e.g., race, gender) and non-protected (e.g., pose, lighting) attributes. Such observational datasets only permit correlational conclusions, e.g., "Algorithm A's accuracy is different on female and male faces in dataset X.". By contrast, experimental methods manipulate attributes individually and thus permit causal conclusions, e.g., "Algorithm A's accuracy is affected by gender and skin color." Our method is based on generating synthetic faces using a neural face generator, where each attribute of interest is modified independently while leaving all other attributes constant. Human observers crucially provide the ground truth on perceptual identity similarity between synthetic image pairs. We validate our method quantitatively by evaluating race and gender biases of three research-grade face recognition models. Our synthetic pipeline reveals that for these algorithms, accuracy is lower for Black and East Asian population subgroups. Our method can also quantify how perceptual changes in attributes affect face identity distances reported by these models. Our large synthetic dataset, consisting of 48,000 synthetic face image pairs (10,200 unique synthetic faces) and 555,000 human annotations (individual attributes and pairwise identity comparisons) is available to researchers in this important area.
Spatial-Aware Token for Weakly Supervised Object Localization
Authors: Pingyu Wu, Wei Zhai, Yang Cao, Jiebo Luo, Zheng-Jun Zha
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Weakly supervised object localization (WSOL) is a challenging task aiming to localize objects with only image-level supervision. Recent works apply visual transformer to WSOL and achieve significant success by exploiting the long-range feature dependency in self-attention mechanism. However, existing transformer-based methods synthesize the classification feature maps as the localization map, which leads to optimization conflicts between classification and localization tasks. To address this problem, we propose to learn a task-specific spatial-aware token (SAT) to condition localization in a weakly supervised manner. Specifically, a spatial token is first introduced in the input space to aggregate representations for localization task. Then a spatial aware attention module is constructed, which allows spatial token to generate foreground probabilities of different patches by querying and to extract localization knowledge from the classification task. Besides, for the problem of sparse and unbalanced pixel-level supervision obtained from the image-level label, two spatial constraints, including batch area loss and normalization loss, are designed to compensate and enhance this supervision. Experiments show that the proposed SAT achieves state-of-the-art performance on both CUB-200 and ImageNet, with 98.45% and 73.13% GT-known Loc, respectively. Even under the extreme setting of using only 1 image per class from ImageNet for training, SAT already exceeds the SOTA method by 2.1% GT-known Loc. Code and models are available at https://github.com/wpy1999/SAT.
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis
Authors: Qiucheng Wu, Yujian Liu, Handong Zhao, Trung Bui, Zhe Lin, Yang Zhang, Shiyu Chang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generated images with respect to the text description, such as missing objects, mismatched attributes, and mislocated objects. One key reason for such inconsistencies is the inaccurate cross-attention to text in both the spatial dimension, which controls at what pixel region an object should appear, and the temporal dimension, which controls how different levels of details are added through the denoising steps. In this paper, we propose a new text-to-image algorithm that adds explicit control over spatial-temporal cross-attention in diffusion models. We first utilize a layout predictor to predict the pixel regions for objects mentioned in the text. We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object. The temporal attention control is further added by allowing the combination weights to change at each denoising step, and the combination weights are optimized to ensure high fidelity between the image and the text. Experiments show that our method generates images with higher fidelity compared to diffusion-model-based baselines without fine-tuning the diffusion model. Our code is publicly available.
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection
Authors: Ziying Song, Haiyue Wei, Lin Bai, Lei Yang, Caiyan Jia
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: LiDAR and cameras are complementary sensors for 3D object detection in autonomous driving. However, it is challenging to explore the unnatural interaction between point clouds and images, and the critical factor is how to conduct feature alignment of heterogeneous modalities. Currently, many methods achieve feature alignment by projection calibration only, without considering the problem of coordinate conversion accuracy errors between sensors, leading to sub-optimal performance. In this paper, we present GraphAlign, a more accurate feature alignment strategy for 3D object detection by graph matching. Specifically, we fuse image features from a semantic segmentation encoder in the image branch and point cloud features from a 3D Sparse CNN in the LiDAR branch. To save computation, we construct the nearest neighbor relationship by calculating Euclidean distance within the subspaces that are divided into the point cloud features. Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features. Then by matching the nearest neighbors with a single point cloud to multiple images, we search for a more appropriate feature alignment. In addition, we provide a self-attention module to enhance the weights of significant relations to fine-tune the feature alignment between heterogeneous modalities. Extensive experiments on nuScenes benchmark demonstrate the effectiveness and efficiency of our GraphAlign.
Weakly-Supervised Action Segmentation and Unseen Error Detection in Anomalous Instructional Videos
Authors: Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Behzad Dariush
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos. In the absence of an appropriate dataset for this task, we introduce the Anomalous Toy Assembly (ATA) dataset, which comprises 1152 untrimmed videos of 32 participants assembling three different toys, recorded from four different viewpoints. The training set comprises 27 participants who assemble toys in an expected and consistent manner, while the test and validation sets comprise 5 participants who display sequential anomalies in their task. We introduce a weakly labeled segmentation algorithm that is a generalization of the constrained Viterbi algorithm and identifies potential anomalous moments based on the difference between future anticipation and current recognition results. The proposed method is not restricted by the training transcripts during testing, allowing for the inference of anomalous action sequences while maintaining real-time performance. Based on these segmentation results, we also introduce a baseline to detect pre-defined human errors, and benchmark results on the ATA dataset. Experiments were conducted on the ATA and CSV datasets, demonstrating that the proposed method outperforms the state-of-the-art in segmenting anomalous videos under both online and offline conditions.
NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects
Authors: Dongqing Wang, Tong Zhang, Sabine Süsstrunk
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refractions and the strong dependency of surface appearance on illumination. With 2D images of the transparent object as input, our method is capable of high-quality novel view and relighting synthesis. We leverage implicit Signed Distance Functions (SDF) to model the object geometry and propose a refraction-aware ray bending network to model the effects of light refraction within the object. Our ray bending network is more tolerant to geometric inaccuracies than traditional physically-based methods for rendering transparent objects. We provide extensive evaluations on both synthetic and real-world datasets to demonstrate our high-quality synthesis and the applicability of our method.
Geometric Viewpoint Learning with Hyper-Rays and Harmonics Encoding
Authors: Zhixiang Min, Juan Carlos Dibene, Enrique Dunn
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Viewpoint is a fundamental modality that carries the interaction between observers and their environment. This paper proposes the first deep-learning framework for the viewpoint modality. The challenge in formulating learning frameworks for viewpoints resides in a suitable multimodal representation that links across the camera viewing space and 3D environment. Traditional approaches reduce the problem to image analysis instances, making them computationally expensive and not adequately modelling the intrinsic geometry and environmental context of 6DoF viewpoints. We improve these issues in two ways. 1) We propose a generalized viewpoint representation forgoing the analysis of photometric pixels in favor of encoded viewing ray embeddings attained from point cloud learning frameworks. 2) We propose a novel SE(3)-bijective 6D viewing ray, hyper-ray, that addresses the DoF deficiency problem of using 5DoF viewing rays representing 6DoF viewpoints. We demonstrate our approach has both efficiency and accuracy superiority over existing methods in novel real-world environments.
C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction
Authors: Luoyuan Xu, Tao Guan, Yuesong Wang, Wenkai Liu, Zhaojie Zeng, Junle Wang, Wei Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: There is an emerging effort to combine the two popular 3D frameworks using Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific focus on the few-shot / sparse view setting. In this paper, we introduce a novel integration scheme that combines the multi-view stereo with neural signed distance function representations, which potentially overcomes the limitations of both methods. MVS uses per-view depth estimation and cross-view fusion to generate accurate surfaces, while NIS relies on a common coordinate volume. Based on this strategy, we propose to construct per-view cost frustum for finer geometry estimation, and then fuse cross-view frustums and estimate the implicit signed distance functions to tackle artifacts that are due to noise and holes in the produced surface reconstruction. We further apply a cascade frustum fusion strategy to effectively captures global-local information and structural consistency. Finally, we apply cascade sampling and a pseudo-geometric loss to foster stronger integration between the two architectures. Extensive experiments demonstrate that our method reconstructs robust surfaces and outperforms existing state-of-the-art methods.
Mesh2Tex: Generating Mesh Textures from Image Queries
Authors: Alexey Bokhovkin, Shubham Tulsiani, Angela Dai
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Remarkable advances have been achieved recently in learning neural representations that characterize object geometry, while generating textured objects suitable for downstream applications and 3D rendering remains at an early stage. In particular, reconstructing textured geometry from images of real objects is a significant challenge - reconstructed geometry is often inexact, making realistic texturing a significant challenge. We present Mesh2Tex, which learns a realistic object texture manifold from uncorrelated collections of 3D object geometry and photorealistic RGB images, by leveraging a hybrid mesh-neural-field texture representation. Our texture representation enables compact encoding of high-resolution textures as a neural field in the barycentric coordinate system of the mesh faces. The learned texture manifold enables effective navigation to generate an object texture for a given 3D object geometry that matches to an input RGB image, which maintains robustness even under challenging real-world scenarios where the mesh geometry approximates an inexact match to the underlying geometry in the RGB image. Mesh2Tex can effectively generate realistic object textures for an object mesh to match real images observations towards digitization of real environments, significantly improving over previous state of the art.
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Authors: Zelin Peng, Guanchun Wang, Lingxi Xie, Dongsheng Jiang, Wei Shen, Qi Tian
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification network is the de facto paradigm for seed area generation, but CAMs generated from Convolutional Neural Networks (CNNs) and Transformers are prone to be under- and over-activated, respectively, which makes the strategies to refine CAMs for CNNs usually inappropriate for Transformers, and vice versa. In this paper, we propose a Unified optimization paradigm for Seed Area GEneration (USAGE) for both types of networks, in which the objective function to be optimized consists of two terms: One is a generation loss, which controls the shape of seed areas by a temperature parameter following a deterministic principle for different types of networks; The other is a regularization loss, which ensures the consistency between the seed areas that are generated by self-adaptive network adjustment from different views, to overturn false activation in seed areas. Experimental results show that USAGE consistently improves seed area generation for both CNNs and Transformers by large margins, e.g., outperforming state-of-the-art methods by an mIoU of 4.1% on PASCAL VOC. Moreover, based on the USAGE generated seed areas on Transformers, we achieve state-of-the-art WSSS results on both PASCAL VOC and MS COCO.
NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction
Authors: Yiming Wang, Qin Han, Marc Habermann, Kostas Daniilidis, Christian Theobalt, Lingjie Liu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Recent methods for neural surface representation and rendering, for example NeuS, have demonstrated the remarkably high-quality reconstruction of static scenes. However, the training of NeuS takes an extremely long time (8 hours), which makes it almost impossible to apply them to dynamic scenes with thousands of frames. We propose a fast neural surface reconstruction approach, called NeuS2, which achieves two orders of magnitude improvement in terms of acceleration without compromising reconstruction quality. To accelerate the training process, we parameterize a neural surface representation by multi-resolution hash encodings and present a novel lightweight calculation of second-order derivatives tailored to our networks to leverage CUDA parallelism, achieving a factor two speed up. To further stabilize and expedite training, a progressive learning strategy is proposed to optimize multi-resolution hash encodings from coarse to fine. We extend our method for fast training of dynamic scenes, with a proposed incremental training strategy and a novel global transformation prediction component, which allow our method to handle challenging long sequences with large movements and deformations. Our experiments on various datasets demonstrate that NeuS2 significantly outperforms the state-of-the-arts in both surface reconstruction accuracy and training speed for both static and dynamic scenes. The code is available at our website: https://vcai.mpi-inf.mpg.de/projects/NeuS2/.
Deep Feature Deblurring Diffusion for Detecting Out-of-Distribution Objects
Authors: Aming Wu, Da Chen, Cheng Deng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: To promote the safe application of detectors, a task of unsupervised out-of-distribution object detection (OOD-OD) is recently proposed, whose goal is to detect unseen OOD objects without accessing any auxiliary OOD data. For this task, the challenge mainly lies in how to only leverage the known in-distribution (ID) data to detect OOD objects accurately without affecting the detection of ID objects, which can be framed as the diffusion problem for deep feature synthesis. Accordingly, such challenge could be addressed by the forward and reverse processes in the diffusion model. In this paper, we propose a new approach of Deep Feature Deblurring Diffusion (DFDD), consisting of forward blurring and reverse deblurring processes. Specifically, the forward process gradually performs Gaussian Blur on the extracted features, which is instrumental in retaining sufficient input-relevant information. By this way, the forward process could synthesize virtual OOD features that are close to the classification boundary between ID and OOD objects, which improves the performance of detecting OOD objects. During the reverse process, based on the blurred features, a dedicated deblurring model is designed to continually recover the lost details in the forward process. Both the deblurred features and original features are taken as the input for training, strengthening the discrimination ability. In the experiments, our method is evaluated on OOD-OD, open-set object detection, and incremental object detection. The significant performance gains over baselines demonstrate the superiorities of our method. The source code will be made available at: https://github.com/AmingWu/DFDD-OOD.
Fast Full-frame Video Stabilization with Iterative Optimization
Authors: Weiyue Zhao, Xin Li, Zhan Peng, Xianrui Luo, Xinyi Ye, Hao Lu, Zhiguo Cao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.
Gender Artifacts in Visual Datasets
Authors: Nicole Meister, Dora Zhao, Angelina Wang, Vikram V. Ramaswamy, Ruth Fong, Olga Russakovsky
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigating gender biases, often by attempting to remove gender expression information from images. To understand the feasibility and practicality of these approaches, we investigate what "gender artifacts" exist in large-scale visual datasets. We define a "gender artifact" as a visual cue correlated with gender , focusing specifically on cues that are learnable by a modern image classifier and have an interpretable human corollary. Through our analyses, we find that gender artifacts are ubiquitous in the COCO and OpenImages datasets, occurring everywhere from low-level information (e.g., the mean value of the color channels) to higher-level image composition (e.g., pose and location of people). Further, bias mitigation methods that attempt to remove gender actually remove more information from the scene than the person. Given the prevalence of gender artifacts, we claim that attempts to remove these artifacts from such datasets are largely infeasible as certain removed artifacts may be necessary for the downstream task of object recognition. Instead, the responsibility lies with researchers and practitioners to be aware that the distribution of images within datasets is highly gendered and hence develop fairness-aware methods which are robust to these distributional shifts across groups.
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
Authors: Bingchen Zhao, Xin Wen, Kai Han
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, we address the problem of generalized category discovery (GCD), i.e., given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain images from the labelled classes and also new ones. GCD is similar to semi-supervised learning (SSL) but is more realistic and challenging, as SSL assumes all the unlabelled images are from the same classes as the labelled ones. 
 We also do not assume the class number in the unlabelled data is known a-priori, making the GCD problem even harder. 
 To tackle the problem of GCD without knowing the class number, we propose an EM-like framework that alternates between representation learning and class number estimation. We propose a semi-supervised variant of the Gaussian Mixture Model (GMM) with a stochastic splitting and merging mechanism to dynamically determine the prototypes by examining the cluster compactness and separability. With these prototypes, we leverage prototypical contrastive learning for representation learning on the partially labelled data subject to the constraints imposed by the labelled data. Our framework alternates between these two steps until convergence. The cluster assignment for an unlabelled instance can then be retrieved by identifying its nearest prototype. We comprehensively evaluate our framework on both generic image classification datasets and challenging fine-grained object recognition datasets, achieving state-of-the-art performance.
SuS-X: Training-Free Name-Only Transfer of Vision-Language Models
Authors: Vishaal Udandarao, Ankush Gupta, Samuel Albanie
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet effective way to train large-scale vision-language models. CLIP demonstrates impressive zero-shot classification and retrieval performance on diverse downstream tasks. However, to leverage its full potential, fine-tuning still appears to be necessary. Fine-tuning the entire CLIP model can be resource-intensive and unstable. Moreover, recent methods that aim to circumvent this need for fine-tuning still require access to images from the target distribution. In this paper, we pursue a different approach and explore the regime of training-free "name-only transfer" in which the only knowledge we possess about downstream tasks comprises the names of downstream target categories. We propose a novel method, SuS-X, consisting of two key building blocks--"SuS" and "TIP-X", that requires neither intensive fine-tuning nor costly labelled data. SuS-X achieves state-of-the-art (SoTA) zero-shot classification results on 19 benchmark datasets. We further show the utility of TIP-X in the training-free few-shot setting, where we again achieve SoTA results over strong training-free baselines.
Rethinking Point Cloud Registration as Masking and Reconstruction
Authors: Guangyan Chen, Meiling Wang, Li Yuan, Yi Yang, Yufeng Yue
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Point cloud registration is essential in computer vision and robotics. In this paper, a critical observation is made that the invisible parts of each point cloud can be directly utilized as inherent masks, and the aligned point cloud pair can be regarded as the reconstruction target. Motivated by this observation, we rethink the point cloud registration problem as a masking and reconstruction task. To this end, a generic and concise auxiliary training network, the Masked Reconstruction Auxiliary Network (MRA), is proposed. The MRA reconstructs the complete point cloud by separately using the encoded features of each point cloud obtained from the backbone, guiding the contextual features in the backbone to capture fine-grained geometric details and the overall structures of point cloud pairs. Unlike recently developed high-performing methods that incorporate specific encoding methods into transformer models, which sacrifice versatility and introduce significant computational complexity during the inference process, our MRA can be easily inserted into other methods to further improve registration accuracy. Additionally, the MRA is detached after training, thereby avoiding extra computational complexity during the inference process. Building upon the MRA, we present a novel transformer-based method, the Masked Reconstruction Transformer (MRT), which achieves both precise and efficient alignment using standard transformers. Extensive experiments conducted on the 3DMatch, ModelNet40, and KITTI datasets demonstrate the superior performance of our MRT over state-of-the-art methods, and the efficiency of the MRA in improving registration accuracy.
Beating Backdoor Attack at Its Own Game
Authors: Min Liu, Alberto Sangiovanni-Vincentelli, Xiangyu Yue
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks (DNNs) are vulnerable to backdoor attack, which does not affect the network's performance on clean data but would manipulate the network behavior once a trigger pattern is added. 
 Existing defense methods have greatly reduced attack success rate, but their prediction accuracy on clean data still lags behind a clean model by a large margin. 
 Inspired by the stealthiness and effectiveness of backdoor attack, we propose a simple but highly effective defense framework which injects non-adversarial backdoors targeting poisoned samples.
 Following the general steps in backdoor attack, we detect a small set of suspected samples and then apply a poisoning strategy to them. 
 The non-adversarial backdoor, once triggered, suppresses the attacker's backdoor on poisoned data, but has limited influence on clean data.
 The defense can be carried out during data preprocessing, without any modification to the standard end-to-end training pipeline.
 We conduct extensive experiments on multiple benchmarks with different architectures and representative attacks. 
 Results demonstrate that our method achieves state-of-the-art defense effectiveness with by far the lowest performance drop on clean data. 
 Considering the surprising defense ability displayed by our framework, we call for more attention to utilizing backdoor for backdoor defense. 
 Code is available at https://github.com/damianliumin/non-adversarial_backdoor.
Introducing Language Guidance in Prompt-based Continual Learning
Authors: Muhammad Gul Zain Ali Khan, Muhammad Ferjad Naeem, Luc Van Gool, Didier Stricker, Federico Tombari, Muhammad Zeshan Afzal
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Continual Learning aims to learn a single model on a sequence of tasks without having access to data from previous tasks. The biggest challenge in the domain still remains catastrophic forgetting: a loss in performance on seen classes of earlier tasks. Some existing methods rely on an expensive replay buffer to store a chunk of data from previous tasks. This, while promising, becomes expensive when the number of tasks becomes large or data can not be stored for privacy reasons. As an alternative, prompt-based methods have been proposed that store the task information in a learnable prompt pool. This prompt pool instructs a frozen image encoder on how to solve each task. While the model faces a disjoint set of classes in each task in this setting, we argue that these classes can be encoded to the same embedding space of a pre-trained language encoder. In this work, we propose Language Guidance for Prompt-based Continual Learning (LGCL) as a plug-in for prompt-based methods. LGCL is model agnostic and introduces language guidance at the task level in the prompt pool and at the class level on the output feature of the vision encoder. We show with extensive experimentation that LGCL consistently improves the performance of prompt-based continual learning methods to set a new state-of-the-art. LGCL achieves these performance improvements without needing any additional learnable parameters.
Invariant Training 2D-3D Joint Hard Samples for Few-Shot Point Cloud Recognition
Authors: Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out the crux is the less effective training for the "joint hard samples", which have high confidence prediction on different wrong labels, implying that the 2D and 3D models do not collaborate well. To this end, our proposed invariant training strategy, called INVJOINT, does not only emphasize the training more on the hard samples, but also seeks the invariance between the conflicting 2D and 3D ambiguous predictions. INVJOINT can learn more collaborative 2D and 3D representations for better ensemble. Extensive experiments on 3D shape classification with widely-adopted ModelNet10/40, ScanObjectNN and Toys4K, and shape retrieval with ShapeNet-Core validate the superiority of our INVJOINT.
EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
Authors: Gabriele Berton, Gabriele Trivigno, Barbara Caputo, Carlo Masone
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Visual Place Recognition is a task that aims to predict the place of an image (called query) based solely on its visual features.
 This is typically done through image retrieval, where the query is matched to the most similar images from a large database of geotagged photos, using learned global descriptors.
 A major challenge in this task is recognizing places seen from different viewpoints. To overcome this limitation, we propose a new method, called EigenPlaces, to train our neural network on images from different point of views, which embeds viewpoint robustness into the learned global descriptors. The underlying idea is to cluster the training data so as to explicitly present the model with different views of the same points of interest. The selection of this points of interest is done without the need for extra supervision.
 We then present experiments on the most comprehensive set of datasets in literature, finding that EigenPlaces is able to outperform previous state of the art on the majority of datasets, while requiring 60% less GPU memory for training and using 50% smaller descriptors.
 The code and trained models for EigenPlaces are available at https://github.com/gmberton/EigenPlaces, while results with any other baseline can be computed with the codebase at https://github.com/gmberton/auto_VPR.
Do DALL-E and Flamingo Understand Each Other?
Authors: Hang Li, Jindong Gu, Rajat Koner, Sahand Sharifzadeh, Volker Tresp
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence of sophisticated models dedicated to image captioning at scale, such as the notable Flamingo model and text-to-image generative models, with DALL-E serving as a prominent example. An interesting question worth exploring in this domain is whether Flamingo and DALL-E understand each other. To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image. We argue that these models understand each other if the generated image is similar to the given image. Specifically, we study the relationship between the quality of the image reconstruction and that of the text generation. We find that an optimal description of an image is one that gives rise to a generated image similar to the original one. The finding motivates us to propose a unified framework to finetune the text-to-image and image-to-text models. Concretely, the reconstruction part forms a regularization loss to guide the tuning of the models. Extensive experiments on multiple datasets with different image captioning and image generation models validate our findings and demonstrate the effectiveness of our proposed unified framework. As DALL-E and Flamingo are not publicly available, we use Stable Diffusion and BLIP in the remaining work. Project website: https://dalleflamingo.github.io.
CIRI: Curricular Inactivation for Residue-aware One-shot Video Inpainting
Authors: Weiying Zheng, Cheng Xu, Xuemiao Xu, Wenxi Liu, Shengfeng He
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Video inpainting aims at filling in missing regions of a video. However, when dealing with dynamic scenes with camera or object movements, annotating the inpainting target becomes laborious and impractical. In this paper, we resolve the one-shot video inpainting problem in which only one annotated first frame is provided. A naive solution is to propagate the initial target to the other frames with techniques like object tracking. In this context, the main obstacles are the unreliable propagation and the partially inpainted artifacts due to the inaccurate mask. For the former problem, we propose curricular inactivation to replace the hard masking mechanism for indicating the inpainting target, which is robust to erroneous predictions in long-term video inpainting. For the latter, we explore the properties of inpainting residue and present an online residue removal method in an iterative detect-and-refine manner. Extensive experiments on several real-world datasets demonstrate the quantitative and qualitative superiorities of our proposed method in one-shot video inpainting. More importantly, our method is extremely flexible that can be integrated with arbitrary traditional inpainting models, activating them to perform the reliable one-shot video inpainting task. Video demonstrations can be found in our supplement, and our code can be found at https://github.com/Arise-zwy/CIRI.
FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation
Authors: Tianyi Shi, Xiaohuan Ding, Liang Zhang, Xin Yang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Curvilinear object segmentation is critical for many applications. However, manually annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently available annotated datasets for existing supervised methods and domain adaptation methods. This paper proposes a self-supervised curvilinear object segmentation method (FreeCOS) that learns robust and distinctive features from fractals and unlabeled images. The key contributions include a novel Fractal-FDA synthesis (FFS) module and a geometric information alignment (GIA) approach. FFS generates curvilinear structures based on the parametric Fractal L-system and integrates the generated structures into unlabeled images to obtain synthetic training images via Fourier Domain Adaptation. GIA reduces the intensity differences between the synthetic and unlabeled images by comparing the intensity order of a given pixel to the values of its nearby neighbors. Such image alignment can explicitly remove the dependency on absolute intensity values and enhance the inherent geometric characteristics which are common in both synthetic and real images. In addition, GIA aligns features of synthetic and real images via the prediction space adaptation loss (PSAL) and the curvilinear mask contrastive loss (CMCL). Extensive experimental results on four public datasets, i.e., XCAD, DRIVE, STARE and CrackTree demonstrate that our method outperforms the state-of-the-art unsupervised methods, self-supervised methods and traditional methods by a large margin. The source code of this work is available at https://github.com/TY-Shi/FreeCOS.
Generating Dynamic Kernels via Transformers for Lane Detection
Authors: Ziye Chen, Yu Liu, Mingming Gong, Bo Du, Guoqi Qian, Kate Smith-Miles
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: State-of-the-art lane detection methods often rely on specific knowledge about lanes -- such as straight lines and parametric curves -- to detect lane lines. While the specific knowledge can ease the modeling process, it poses challenges in handling lane lines with complex topologies (e.g., dense, forked, curved, etc.). Recently, dynamic convolution-based methods have shown promising performance by utilizing the features from some key locations of a lane line, such as the starting point, as convolutional kernels, and convoluting them with the whole feature map to detect lane lines. While such methods reduce the reliance on specific knowledge, the kernels computed from the key locations fail to capture the lane line's global structure due to its long and thin structure, leading to inaccurate detection of lane lines with complex topologies. In addition, the kernels resulting from the key locations are sensitive to occlusion and lane intersections. To overcome these limitations, we propose a transformer-based dynamic kernel generation architecture for lane detection. It utilizes a transformer to generate dynamic convolutional kernels for each lane line in the input image, and then detect these lane lines with dynamic convolution. Compared to the kernels generated from the key locations of a lane line, the kernels generated with the transformer can capture the lane line's global structure from the whole feature map, enabling them to effectively handle occlusions and lane lines with complex topologies. We evaluate our method on three lane detection benchmarks, and the results demonstrate its state-of-the-art performance. Specifically, our method achieves an F1 score of 63.40 on OpenLane and 88.47 on CurveLanes, surpassing the state of the art by 4.30 and 2.37 points, respectively.