mtybilly/Instruct2DS huggingface dataset:
Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting
Authors: Fangyin Wei, Thomas Funkhouser, Szymon Rusinkiewicz
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Removing clutter from scenes is essential in many applications, ranging from privacy-concerned content filtering to data augmentation. In this work, we present an automatic system that removes clutter from 3D scenes and inpaints with coherent geometry and texture. We propose techniques for its two key components: 3D segmentation based on shared properties and 3D inpainting, both of which are important problems. We define 3D scene clutter as frequently-moving objects (e.g. clothes or chairs that are typically moved within a few days). The definition of 3D scene clutter (frequently-moving objects) is not well captured by commonly-studied object categories in computer vision. To tackle the lack of well-defined clutter annotations, we group noisy fine-grained labels, leverage virtual rendering, and impose an instance-level area-sensitive loss. Once clutter is removed, we inpaint geometry and texture in the resulting holes by merging inpainted RGB-D images. This requires novel voting and pruning strategies that guarantee multi-view consistency across individually inpainted images for mesh reconstruction. Experiments on ScanNet and Matterport3D dataset show that our method outperforms baselines for clutter segmentation and 3D inpainting, both visually and quantitatively. Project page: https://weify627.github.io/clutter/.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
Authors: Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng Qin, Shanghang Zhang, Peng Gao
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still limited and only constrained to the classification task. In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection. To better align 3D data with the pre-trained language knowledge, PointCLIP V2 contains two key designs. For the visual end, we prompt CLIP via a shape projection module to generate more realistic depth maps, narrowing the domain gap between projected point clouds with natural images. For the textual end, we prompt the GPT model to generate 3D-specific text as the input of CLIP's textual encoder. Without any training in 3D domains, our approach significantly surpasses PointCLIP by +42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D classification. On top of that, V2 can be extended to few-shot 3D classification, zero-shot 3D part segmentation, and 3D object detection in a simple manner, demonstrating our generalization ability for unified 3D open-world learning.
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation
Authors: Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We introduce VideoFlow, a novel optical flow estimation framework for videos. In contrast to previous methods that learn to estimate optical flow from two frames, VideoFlow concurrently estimates bi-directional optical flows for multiple frames that are available in videos by sufficiently exploiting temporal cues. We first propose a TRi-frame Optical Flow (TROF) module that estimates bi-directional optical flows for the center frame in a three-frame manner. The information of the frame triplet is iteratively fused onto the center frame. To extend TROF for handling more frames, we further propose a MOtion Propagation (MOP) module that bridges multiple TROFs and propagates motion features between adjacent TROFs. With the iterative flow estimation refinement, the information fused in individual TROFs can be propagated into the whole sequence via MOP. By effectively exploiting video information, VideoFlow presents extraordinary performance, ranking 1st on all public benchmarks. On the Sintel benchmark, VideoFlow achieves 1.649 and 0.991 average end-point-error (AEPE) on the final and clean passes, a 15.1% and 7.6% error reduction from the best published results (1.943 and 1.073 from FlowFormer++). On the KITTI-2015 benchmark, VideoFlow achieves an F1-all error of 3.65%, a 19.2% error reduction from the best published result (4.52% from FlowFormer++). Code is released at https://github.com/XiaoyuShi97/VideoFlow.
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
Authors: Ta-Ying Cheng, Matheus Gadelha, Sören Pirk, Thibault Groueix, Radomír Měch, Andrew Markham, Niki Trigoni
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that, within a large-enough dataset, there must exist images of objects with similar shapes but varying backgrounds, textures, and viewpoints. Our approach leverages the recent advances in learning self-supervised image representations to cluster images with geometrically similar shapes and find common image correspondences between them. We then exploit these correspondences to obtain rough camera estimates as initialization for bundle-adjustment. Finally, for every image cluster, we apply a progressive bundle-adjusting reconstruction method to learn a neural occupancy field representing the underlying shape. We show that this procedure is robust to several types of errors introduced in previous steps (e.g., wrong camera poses, images containing dissimilar shapes, etc.), allowing us to obtain shape and pose annotations for images in-the-wild.
 When using images from Pix3D chairs, our method is capable of producing significantly better results than state-of-the-art unsupervised 3D
 reconstruction techniques, both quantitatively and qualitatively. Furthermore, we show how 3DMiner can be applied to in-the-wild data by reconstructing shapes present in images from the LAION-5B dataset.
Identification of Systematic Errors of Image Classifiers on Rare Subgroups
Authors: Jan Hendrik Metzen, Robin Hutmacher, N. Grace Hua, Valentyn Boreiko, Dan Zhang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under-represented in the training data. These systematic errors can impact both fairness for demographic minority groups as well as robustness and safety under domain shift. A major challenge is to identify such subgroups with subpar performance when the subgroups are not annotated and their occurrence is very rare. We leverage recent advances in text-to-image models and search in the space of textual descriptions of subgroups ("prompts") for subgroups where the target model has low performance on the prompt-conditioned synthesized data. To tackle the exponentially growing number of subgroups, we employ combinatorial testing. We denote this procedure as PromptAttack as it can be interpreted as an adversarial attack in a prompt space. We study subgroup coverage and identifiability with PromptAttack in a controlled setting and find that it identifies systematic errors with high accuracy. Thereupon, we apply PromptAttack to ImageNet classifiers and identify novel systematic errors on rare subgroups.
Hierarchical Spatio-Temporal Representation Learning for Gait Recognition
Authors: Lei Wang, Bo Liu, Fangfang Liang, Bincheng Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Gait recognition is a biometric technique that identifies individuals by their unique walking styles, which is suitable for unconstrained environments and has a wide range of applications. While current methods focus on exploiting body part-based representations, they often neglect the hierarchical dependencies between local motion patterns. In this paper, we propose a hierarchical spatio-temporal representation learning (HSTL) framework for extracting gait features from coarse to fine. Our framework starts with a hierarchical clustering analysis to recover multi-level body structures from the whole body to local details. Next, an adaptive region-based motion extractor (ARME) is designed to learn region-independent motion features. The proposed HSTL then stacks multiple ARMEs in a top-down manner, with each ARME corresponding to a specific partition level of the hierarchy. An adaptive spatio-temporal pooling (ASTP) module is used to capture gait features at different levels of detail to perform hierarchical feature mapping. Finally, a frame-level temporal aggregation (FTA) module is employed to reduce redundant information in gait sequences through multi-scale temporal downsampling. Extensive experiments on CASIA-B, OUMVLP, GREW, and Gait3D datasets demonstrate that our method outperforms the state-of-the-art while maintaining a reasonable balance between model accuracy and complexity. Code is available at: https://github.com/gudaochangsheng/HSTL.
Order-Prompted Tag Sequence Generation for Video Tagging
Authors: Zongyang Ma, Ziqi Zhang, Yuxin Chen, Zhongang Qi, Yingmin Luo, Zekun Li, Chunfeng Yuan, Bing Li, Xiaohu Qie, Ying Shan, Weiming Hu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Video Tagging intends to infer multiple tags spanning relevant content for a given video. Typically, video tags are freely defined and uploaded by a variety of users, so they have two characteristics: abundant in quantity and disordered intra-video. It is difficult for the existing multi-label classification and generation methods to adapt directly to this task. This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics. It regards video tagging as a tag sequence generation problem guided by sample-dependent order prompts. These prompts are semantically aligned with tags and enable to decouple tag generation order, making the model focus on modeling the tag dependencies. Moreover, the word-based generation strategy enables the model to generate novel tags. To verify the effectiveness and generalization of the proposed method, a Chinese video tagging benchmark CREATE-tagging, and an English image tagging benchmark Pexel-tagging are established. Extensive results show that OP-TSG is significantly superior to other methods, especially the results on rare tags improve by 3.3% and 3% over SOTA methods on CREATE-tagging and Pexel-tagging, and novel tags generated on CREATE-tagging exhibit a tag gain of 7.04%.
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
Authors: Lei Lai, Zhongkai Shangguan, Jimuyang Zhang, Eshed Ohn-Bar
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings. In contrast to standard monocular VO approaches which often study a known calibration within a single dataset, XVO efficiently learns to recover relative pose with real-world scale from visual scene semantics, i.e., without relying on any known camera parameters. We optimize the motion estimation model via self-training from large amounts of unconstrained and heterogeneous dash camera videos available on YouTube. Our key contribution is twofold. First, we empirically demonstrate the benefits of semi-supervised training for learning a general-purpose direct VO regression network. Second, we demonstrate multi-modal supervision, including segmentation, flow, depth, and audio auxiliary prediction tasks, to facilitate generalized representations for the VO task. Specifically, we find audio prediction task to significantly enhance the semi-supervised learning process while alleviating noisy pseudo-labels, particularly in highly dynamic and out-of-domain video data. Our proposed teacher network achieves state-of-the-art performance on the commonly used KITTI benchmark despite no multi-frame optimization or knowledge of camera parameters. Combined with the proposed semi-supervised step, XVO demonstrates off-the-shelf knowledge transfer across diverse conditions on KITTI, nuScenes, and Argoverse without fine-tuning.
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement
Authors: Yiwen Huang, Yixuan Sun, Chenghang Lai, Qing Xu, Xiaomei Wang, Xuli Shen, Weifeng Ge
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: In this paper, we develop a weakly supervised learning algorithm to learn robust semantic correspondences from large-scale datasets with only image-level labels. Following the spirit of multiple instance learning (MIL), we decompose the weakly supervised correspondence learning problem into three stages: image-level matching, region-level matching, and pixel-level matching. We propose a novel cascaded online correspondence refinement algorithm to integrate MIL and the correspondence filtering and refinement procedure into a single deep network and train this network end-to-end with only image-level supervision, i.e., without point-to-point matching information. During the correspondence learning process, pixel-to-pixel matching pairs inferred from weak supervision are propagated, filtered, and enhanced through masked correspondence voting and calibration. Besides, we design a correspondence consistency check algorithm to select images with discriminative key points to generate pseudo-labels for classical matching algorithms. Finally, we filter out about 110,000 images from the ImageNet ILSVRC training set to formulate a new dataset, called SC-ImageNet. Experiments on several popular benchmarks indicate that pre-training on SC-ImageNet can improve the performance of state-of-the-art algorithms efficiently. Our project is available on https://github.com/21210240056/SC-ImageNet.
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds
Authors: Yu Pei, Xian Zhao, Hao Li, Jingyuan Ma, Jingwei Zhang, Shiliang Pu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Attributed to the unstructured and sparse nature of point clouds, the transformer shows greater potential in point clouds data processing. However, the recent query-based 3D detectors usually project the features acquired from a sparse backbone into the structured and compact Bird's Eye View(BEV) plane before adopting the transformer, which destroys the sparsity of features, introducing empty tokens and additional resource consumption for the transformer. To this end, in this paper, we propose a novel query-based 3D detector called Clusterformer, our Clusterformer regards each object as a cluster of 3D space which mainly consists of the non-empty voxels belonging to the same object, and leverages the cluster to conduct the transformer decoder to generate the proposals from the sparse voxel features directly. Such cluster-based transformer structure can effectively improve the performance and convergence speed of query-based detectors by making use of the object prior information contained in the clusters. Additionally, we introduce a Query2Key strategy to enhance the key and value features with the object-level information iteratively in our cluster-based transformer structure. Experimental results show that the proposed Clusterformer outperforms the previous query-based detectors with a lower latency and memory usage, which achieves state-of-the-art performance on the Waymo Open Datasets and KITTI Datasets.
HMD-NeMo: Online 3D Avatar Motion Generation From Sparse Observations
Authors: Sadegh Aliakbarian, Fatemeh Saleh, David Collier, Pashmina Cameron, Darren Cosker
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Generating both plausible and accurate full body avatar motion is the key to the quality of immersive experiences in mixed reality scenarios. Head-Mounted Devices (HMDs) typically only provide a few input signals, such as head and hands 6-DoF. Recently, different approaches achieved impressive performance in generating full body motion given only head and hands signal. However, to the best of our knowledge, all existing approaches rely on full hand visibility. While this is the case when, e.g., using motion controllers, a considerable proportion of mixed reality experiences do not involve motion controllers and instead rely on egocentric hand tracking. This introduces the challenge of partial hand visibility owing to the restricted field of view of the HMD. In this paper, we propose the first unified approach, HMD-NeMo, that addresses plausible and accurate full body motion generation even when the hands may be only partially visible. HMD-NeMo is a lightweight neural network that predicts the full body motion in an online and real-time fashion. At the heart of HMD-NeMo is the spatio-temporal encoder with novel temporally adaptable mask tokens that encourage plausible motion in the absence of hand observations. We perform extensive analysis of the impact of different components in HMD-NeMo and introduce a new state-of-the-art on AMASS dataset through our evaluation.
NaviNeRF: NeRF-based 3D Representation Disentanglement by Latent Semantic Navigation
Authors: Baao Xie, Bohan Li, Zequn Zhang, Junting Dong, Xin Jin, Jingyu Yang, Wenjun Zeng
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: 3D representation disentanglement aims to identify, decompose, and manipulate the underlying explanatory factors of 3D data, which helps AI fundamentally understand our 3D world. This task is currently under-explored and poses great challenges: (i) the 3D representations are complex and in general contains much more information than 2D image; (ii) many 3D representations are not well suited for gradient-based optimization, let alone disentanglement. To address these challenges, we use NeRF as a differentiable 3D representation, and introduce a self-supervised Navigation to identify interpretable semantic directions in the latent space. To our best knowledge, this novel method, dubbed NaviNeRF, is the first work to achieve fine-grained 3D disentanglement without any priors or supervision. Specifically, NaviNeRF is built upon the generative NeRF pipeline, and equipped with an Outer Navigation Branch and an Inner Refinement Branch. They are complementary ---- the outer navigation is to identify global-view semantic directions, and the inner refinement dedicates to fine-grained attributes. A synergistic loss is further devised to coordinate two branches. Extensive experiments demonstrate that NaviNeRF has a superior fine-grained 3D disentanglement ability than the previous 3D-aware models. Its performance is also comparable to editing-oriented models relying on semantic or geometry priors.
Adaptive Illumination Mapping for Shadow Detection in Raw Images
Authors: Jiayu Sun, Ke Xu, Youwei Pang, Lihe Zhang, Huchuan Lu, Gerhard Hancke, Rynson Lau
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Shadow detection methods rely on multi-scale contrast, especially global contrast, information to locate shadows correctly. However, we observe that the camera image signal processor (ISP) tends to preserve more local contrast information by sacrificing global contrast information during the raw-to-sRGB conversion process. This often causes existing methods to fail in scenes with high global contrast but low local contrast in shadow regions. In this paper, we propose a novel method to detect shadows from raw images. Our key idea is that instead of performing a many-to-one mapping like the ISP process, we can learn a many-to-many mapping from the high dynamic range raw images to the sRGB images of different illumination, which is able to preserve multi-scale contrast for accurate shadow detection. To this end, we first construct a new shadow dataset with  7000 raw images and shadow masks. We then propose a novel network, which includes a novel adaptive illumination mapping (AIM) module to project the input raw images into sRGB images of different intensity ranges and a shadow detection module to leverage the preserved multi-scale contrast information to detect shadows. To learn the shadow-aware adaptive illumination mapping process, we propose a novel feedback mechanism to guide the AIM during training. Experiments show that our method outperforms state-of-the-art shadow detectors. Code and dataset are available at https://github.com/jiayusun/SARA.
CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
Authors: Rabab Abdelfattah, Qing Guo, Xiaoguang Li, Xiaofeng Wang, Song Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference. At the initialization stage, we take full advantage of the powerful CLIP model and propose a novel approach to extend CLIP for multi-label predictions based on global-local image-text similarity aggregation. To be more specific, we split each image into snippets and leverage CLIP to generate the similarity vector for the whole image (global) as well as each snippet (local). Then a similarity aggregator is introduced to leverage the global and local similarity vectors. Using the aggregated similarity scores as the initial pseudo labels at the training stage, we propose an optimization framework to train the parameters of the classification network and refine pseudo labels for unobserved labels. During inference, only the classification network is used to predict the labels of the input image. Extensive experiments show that our method outperforms state-of-the-art unsupervised methods on MS-COCO, PASCAL VOC 2007, PASCAL VOC 2012, and NUS datasets and even achieves comparable results to weakly supervised classification methods.
Your Diffusion Model is Secretly a Zero-Shot Classifier
Authors: Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive compositional generalization abilities. Almost all use cases thus far have solely focused on sampling; however, diffusion models can also provide conditional density estimates, which are useful for tasks beyond image generation. In this paper, we show that the density estimates from large-scale text-to-image diffusion models like Stable Diffusion can be leveraged to perform zero-shot classification without any additional training. Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models. Although a gap remains between generative and discriminative approaches on zero-shot recognition tasks, our diffusion-based approach has stronger multimodal compositional reasoning abilities than competing discriminative approaches. Finally, we use Diffusion Classifier to extract standard classifiers from class-conditional diffusion models trained on ImageNet. These models approach the performance of SOTA discriminative classifiers and exhibit strong "effective robustness" to distribution shift. Overall, our results are a step toward using generative over discriminative models for downstream tasks.
Backpropagation Path Search On Adversarial Transferability
Authors: Zhuoer Xu, Zhangxuan Gu, Jianping Zhang, Shiwen Cui, Changhua Meng, Weiqiang Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation. To enhance the adversarial transferability, structure-based attackers adjust the backpropagation path to avoid the attack from overfitting the surrogate model. However, existing structure-based attackers fail to explore the convolution module in CNNs and modify the backpropagation graph heuristically, leading to limited effectiveness. In this paper, we propose backPropagation pAth Search (PAS), solving the aforementioned two problems. We first propose SkipConv to adjust the backpropagation path of convolution by structural reparameterization. To overcome the drawback of heuristically designed backpropagation paths, we further construct a DAG-based search space, utilize one-step approximation for path evaluation and employ Bayesian Optimization to search for the optimal path. We conduct comprehensive experiments in a wide range of transfer settings, showing that PAS improves the attack success rate by a huge margin for both normally trained and defense models.
Boosting Adversarial Transferability via Gradient Relevance Attack
Authors: Hegui Zhu, Yuchen Ren, Xiaoyan Sui, Lianping Yang, Wuming Jiang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Plentiful adversarial attack researches have revealed the fragility of deep neural networks (DNNs), where the imperceptible perturbations can cause drastic changes in the output. Among the diverse types of attack methods, gradient-based attacks are powerful and easy to implement, arousing wide concern for the security problem of DNNs. However, under the black-box setting, the existing gradient-based attacks have much trouble in breaking through DNN models with defense technologies, especially those adversarially trained models. To make adversarial examples more transferable, in this paper, we explore the fluctuation phenomenon on the plus-minus sign of the adversarial perturbations' pixels during the generation of adversarial examples, and propose an ingenious Gradient Relevance Attack (GRA). Specifically, two gradient relevance frameworks are presented to better utilize the information in the neighborhood of the input, which can correct the update direction adaptively. Then we adjust the update step at each iteration with a decay indicator to counter the fluctuation. Experiment results on a subset of the ILSVRC 2012 validation set forcefully verify the effectiveness of GRA. Furthermore, the attack success rates of 68.7% and 64.8% on Tencent Cloud and Baidu AI Cloud further indicate that GRA can craft adversarial examples with the ability to transfer across both datasets and model architectures. Code is released at https://github.com/RYC-98/GRA.
Image-Free Classifier Injection for Zero-Shot Classification
Authors: Anders Christensen, Massimiliano Mancini, A. Sophia Koepke, Ole Winther, Zeynep Akata
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Zero-shot learning models achieve remarkable results on image classification for samples from classes that were not seen during training. However, such models must be trained from scratch with specialised methods: therefore, access to a training dataset is required when the need for zero-shot classification arises. In this paper, we aim to equip pre-trained models with zero-shot classification capabilities without the use of image data. We achieve this with our proposed Image-free Classifier Injection with Semantics (ICIS) that injects classifiers for new, unseen classes into pre-trained classification models in a post-hoc fashion without relying on image data. Instead, the existing classifier weights and simple class-wise descriptors, such as class names or attributes, are used. ICIS has two encoder-decoder networks that learn to reconstruct classifier weights from descriptors (and vice versa), exploiting (cross-)reconstruction and cosine losses to regularise the decoding process. Notably, ICIS can be cheaply trained and applied directly on top of pre-trained classification models. Experiments on benchmark ZSL datasets show that ICIS produces unseen classifier weights that achieve strong (generalised) zero-shot classification performance. Code is available at https://github.com/ExplainableML/ImageFreeZSL.
CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No
Authors: Hualiang Wang, Yi Li, Huifeng Yao, Xiaomeng Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Out-of-distribution (OOD) detection refers to training the model on in-distribution (ID) dataset to classify if the input images come from unknown classes. Considerable efforts have been invested in designing various OOD detection methods based on either convolutional neural networks or transformers. However, Zero-shot OOD detection methods driven by CLIP, which require only class names for ID, have received less attention. This paper presents a novel method, namely CLIP saying no (CLIPN), which empowers "no" logic within CLIP. Our key motivation is to equip CLIP with the capability of distinguishing OOD and ID samples via positive-semantic prompts and negation-semantic prompts. To be specific, we design a novel learnable "no" prompt and a "no" text encoder to capture the negation-semantic with images. Subsequently, we introduce two loss functions: the image-text binary-opposite loss and the text semantic-opposite loss, which we use to teach CLIPN to associate images with "no" prompts, thereby enabling it to identify unknown samples. Furthermore, we propose two threshold-free inference algorithms to perform OOD detection via using negation semantics from "no" prompts and text encoder. Experimental results on 9 benchmark datasets (3 ID datasets and 6 OOD datasets) for the OOD detection task demonstrate that CLIPN outperforms 7 well-used algorithms by at least 1.1% and 7.37% on AUROC and FPR95 on zero-shot OOD detection of ImageNet-1K. Our CLIPN can serve as a solid foundation for leveraging CLIP effectively in downstream OOD tasks.
CO-Net: Learning Multiple Point Cloud Tasks at Once with A Cohesive Network
Authors: Tao Xie, Ke Wang, Siyi Lu, Yukun Zhang, Kun Dai, Xiaoyu Li, Jie Xu, Li Wang, Lijun Zhao, Xinyu Zhang, Ruifeng Li
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present CO-Net, a cohesive framework that optimizes multiple point cloud tasks collectively across heterogeneous dataset domains. CO-Net maintains the characteristics of high storage efficiency since models with the preponderance of shared parameters can be assembled into a single model. Specifically, we leverage residual MLP (Res-MLP) block for effective feature extraction and scale it gracefully along the depth and width of the network to meet the demands of different tasks. Based on the block, we propose a novel nested layer-wise processing policy, which identifies the optimal architecture for each task while provides partial sharing parameters and partial non-sharing parameters inside each layer of the block. Such policy tackles the inherent challenges of multi-task learning on point cloud, e.g., diverse model topologies resulting from task skew and conflicting gradients induced by heterogeneous dataset domains. Finally, we propose a sign-based gradient surgery to promote the training of CO-Net, thereby emphasizing the usage of task-shared parameters and guaranteeing that each task can be thoroughly optimized. Experimental results reveal that models optimized by CO-Net jointly for all point cloud tasks maintain much fewer computation cost and overall storage cost yet outpace prior methods by a significant margin. We also demonstrate that CO-Net allows incremental learning and prevents catastrophic amnesia when adapting to a new point cloud task.
Quality Diversity for Visual Pre-Training
Authors: Ruchika Chavhan, Henry Gouk, Da Li, Timothy Hospedales
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Models pre-trained on large datasets such as ImageNet provide the de-facto standard for transfer learning, with both supervised and self-supervised approaches proving effective. However, emerging evidence suggests that any single pre-trained feature will not perform well on diverse downstream tasks. Each pre-training strategy encodes a certain inductive bias, which may suit some downstream tasks but not others. Notably, the augmentations used in both supervised and self-supervised training lead to features with high invariance to spatial and appearance transformations. This renders them sub-optimal for tasks that demand sensitivity to these factors. In this paper we develop a feature that better supports diverse downstream tasks by providing a diverse set of sensitivities and invariances. In particular, we are inspired by Quality-Diversity in evolution, to define a pre-training objective that requires high quality yet diverse features -- where diversity is defined in terms of transformation (in)variances. Our framework plugs in to both supervised and self-supervised pre-training, and produces a small ensemble of features. We further show how downstream tasks can easily and efficiently select their preferred (in)variances. Both empirical and theoretical analysis show the efficacy of our representation and transfer learning approach for diverse downstream tasks.
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-Aware Curriculum and Iterative Generalist-Specialist Learning
Authors: Weikang Wan, Haoran Geng, Yun Liu, Zikang Shan, Yaodong Yang, Li Yi, He Wang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++. To address the challenge of learning the vision-based policy across thousands of object instances, we propose Geometry-aware Curriculum Learning (GeoCurriculum) and Geometry-aware iterative Generalist-Specialist Learning (GiGSL) which leverage the geometry feature of the task and significantly improve the generalizability. With our proposed techniques, our final policy shows universal dexterous grasping on thousands of object instances with 85.4% and 78.2% success rate on the train set and test set which outperforms the state-of-the-art baseline UniDexGrasp by 11.7% and 11.3%, respectively.
Multi-Scale Residual Low-Pass Filter Network for Image Deblurring
Authors: Jiangxin Dong, Jinshan Pan, Zhongbao Yang, Jinhui Tang
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present a simple and effective Multi-scale Residual Low-Pass Filter Network (MRLPFNet) that jointly explores the image details and main structures for image deblurring. Our work is motivated by an observation that the difference between the blurry image and the clear one not only contains high-frequency contents(Note that the high-frequency contents in an image correspond to the image details, while the low-frequency ones denote the main structures of an image.) but also includes low-frequency information due to the influence of blur, while using the standard residual learning is less effective for modeling the main structure distorted by the blur. Considering that the low-frequency contents usually correspond to main global structures that are spatially variant, we first propose a learnable low-pass filter based on a self-attention mechanism to adaptively explore the global contexts for better modeling the low-frequency information. Then we embed it into a Residual Low-Pass Filter (RLPF) module, which involves an additional fully convolutional neural network with the standard residual learning to model the high-frequency information. We formulate the RLPF module into an end-to-end trainable network based on an encoder and decoder architecture and develop a wavelet-based feature fusion to fuse the multi-scale features. Experimental results show that our method performs favorably against state-of-the-art ones on commonly-used benchmarks.
Neural Fields for Structured Lighting
Authors: Aarrushi Shandilya, Benjamin Attal, Christian Richardt, James Tompkin, Matthew O'toole
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present an image formation model and optimization procedure that combines the advantages of neural radiance fields and structured light imaging. Existing depth-supervised neural models rely on depth sensors to accurately capture the scene's geometry. However, the depth maps recovered by these sensors can be prone to error, or even fail outright. Instead of depending on the fidelity of processed depth maps from a structured light system, a more principled approach is to explicitly model the raw structured light images themselves. Our proposed approach enables the estimation of high-fidelity depth maps, including for objects with complex material properties (e.g., partially-transparent surfaces). Besides computing depth, the raw structured light images also confer other useful radiometric cues, which enable predicting surface normals and decomposing scene appearance in terms of a direct, indirect, and ambient component. We evaluate our framework quantitatively and qualitatively on a range of real and synthetic scenes, and decompose scenes into their constituent components for novel views.
ClothPose: A Real-world Benchmark for Visual Analysis of Garment Pose via An Indirect Recording Solution
Authors: Wenqiang Xu, Wenxin Du, Han Xue, Yutong Li, Ruolin Ye, Yan-Feng Wang, Cewu Lu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Garments are important and pervasive in daily life. However, visual analysis on them for pose estimation is challenging because it requires recovering the complete configurations of garments, which is difficult, if not impossible, to annotate in the real world. In this work, we propose a recording system, GarmentTwin, which can track garment poses in dynamic settings such as manipulation. GarmentTwin first collects garment models and RGB-D manipulation videos from the real world and then replays the manipulation process using physics-based animation. This way, we can obtain deformed garments with poses coarsely aligned with real-world observations. Finally, we adopt an optimization-based approach to fit the pose with real-world observations. We verify the fitting results quantitatively and qualitatively. With GarmentTwin, we construct a large-scale dataset named ClothPose, which consists of 30K RGB-D frames from 2K video clips on 600 garments of 10 categories. We benchmark two tasks on the proposed ClothPose: non-rigid reconstruction and pose estimation. The experiments show that previous baseline methods struggle with highly large non-rigid deformation of manipulated garments. Therefore, we hope that the recording system and the dataset can facilitate research on pose estimation tasks on non-rigid objects. Datasets, models, and codes are made publicly available.
Semantically Structured Image Compression via Irregular Group-Based Decoupling
Authors: Ruoyu Feng, Yixin Gao, Xin Jin, Runsen Feng, Zhibo Chen
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Image compression techniques typically focus on compressing rectangular images for human consumption, however, resulting in transmitting redundant content for downstream applications. To overcome this limitation, some previous works propose to semantically structure the bitstream, which can meet specific application requirements by selective transmission and reconstruction. Nevertheless, they divide the input image into multiple rectangular regions according to semantics and ignore avoiding information interaction among them, causing waste of bitrate and distorted reconstruction of region boundaries. In this paper, we propose to decouple an image into multiple groups with irregular shapes based on a customized group mask and compress them independently. Our group mask describes the image at a finer granularity, enabling significant bitrate saving by reducing the transmission of redundant content. Moreover, to ensure the fidelity of selective reconstruction, this paper proposes the concept of group-independent transform that maintain the independence among distinct groups. And we instantiate it by the proposed Group-Independent Swin-Block (GI Swin-Block). Experimental results demonstrate that our framework structures the bitstream with negligible cost, and exhibits superior performance on both visual quality and intelligent task supporting.
PhaseMP: Robust 3D Pose Estimation via Phase-conditioned Human Motion Prior
Authors: Mingyi Shi, Sebastian Starke, Yuting Ye, Taku Komura, Jungdam Won
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We present a novel motion prior, called PhaseMP, modeling a probability distribution on pose transitions conditioned by a frequency domain feature extracted from a periodic autoencoder. The phase feature further enforces the pose transitions to be unidirectional (i.e. no backward movement in time), from which more stable and natural motions can be generated. Specifically, our motion prior can be useful for accurately estimating 3D human motions in the presence of challenging input data, including long periods of spatial and temporal occlusion, as well as noisy sensor measurements. Through a comprehensive evaluation, we demonstrate the efficacy of our novel motion prior, showcasing its superiority over existing state-of-the-art methods by a significant margin across various applications, including video-to-motion and motion estimation from sparse sensor data, and etc.
NLOS-NeuS: Non-line-of-sight Neural Implicit Surface
Authors: Yuki Fujimura, Takahiro Kushida, Takuya Funatomi, Yasuhiro Mukaigawa
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Non-line-of-sight (NLOS) imaging is conducted to infer invisible scenes from indirect light on visible objects. The neural transient field (NeTF) was proposed for representing scenes as neural radiance fields in NLOS scenes. We propose NLOS neural implicit surface (NLOS-NeuS), which extends the NeTF to neural implicit surfaces with a signed distance function (SDF) for reconstructing three-dimensional surfaces in NLOS scenes. We introduce two constraints as loss functions for correctly learning an SDF to avoid non-zero level-set surfaces. We also introduce a lower bound constraint of an SDF based on the geometry of the first-returning photons. The experimental results indicate that these constraints are essential for learning a correct SDF in NLOS scenes. Compared with previous methods with discretized representation, NLOS-NeuS with the neural continuous representation enables us to reconstruct smooth surfaces while preserving fine details in NLOS scenes. To the best of our knowledge, this is the first study on neural implicit surfaces with volume rendering in NLOS scenes. Project page: https://yfujimura. github.io/nlos-neus/
Unsupervised Object Localization with Representer Point Selection
Authors: Yeonghwan Song, Seokwoo Jang, Dina Katabi, Jeany Son
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning. Existing unsupervised and self-supervised object localization methods often utilize class-agnostic activation maps or self-similarity maps of a pre-trained model. Although these maps can offer valuable information for localization, their limited ability to explain how the model makes predictions remains challenging. In this paper, we propose a simple yet effective unsupervised object localization method based on representer point selection, where the predictions of the model can be represented as a linear combination of representer values of training points. By selecting representer points, which are the most important examples for the model predictions, our model can provide insights into how the model predicts the foreground object by providing relevant examples as well as their importance. Our method outperforms the state-of-the-art unsupervised and self-supervised object localization methods on various datasets with significant margins and even outperforms recent weakly supervised and few-shot methods.
SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics
Authors: Sriram Ravindran, Debraj Basu
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for locating foreground objects. Recent methods have also incorporated intuitive priors and demonstrated value in unsupervised methods for object partitioning. In this paper, we propose SEMPART, which jointly infers coarse and fine bi-partitions over an image's DINO-based semantic graph. Furthermore, SEMPART preserves fine boundary details using graph-driven regularization and successfully distills the coarse mask semantics into the fine mask. Our salient object detection and single object localization findings suggest that SEMPART produces high-quality masks rapidly without additional post-processing and benefits from co-optimizing the coarse and fine branches.
Flatness-Aware Minimization for Domain Generalization
Authors: Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, Peng Cui
Published in: IEEE/CVF International Conference on Computer Vision (ICCV), 2023 (Main Conference Track), 2023

Abstract: Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets. Based on the perspective of loss landscape flatness, we propose a novel approach, Flatness-Aware Minimization for Domain Generalization (FAD), which can efficiently optimize both zeroth-order and first-order flatness simultaneously for DG. We provide theoretical analyses of the FAD's out-of-distribution (OOD) generalization error and convergence. Our experimental results demonstrate the superiority of FAD on various DG datasets.