pwc-archive/papers-with-abstracts huggingface dataset:
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware Synthesis
Authors: Yuwei Yang, Munawar Hayat, Zhao Jin, Hongyuan Zhu, Yinjie Lei
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Diversity, Point Cloud Segmentation, Segmentation, Semantic Segmentation, Zero-Shot Semantic Segmentation

    This paper proposes a feature synthesis approach for zero-shot semantic segmentation of 3D point clouds, enabling generalization to previously unseen categories. Given only the class-level semantic information for unseen objects, we strive to enhance the correspondence, alignment and consistency between the visual and semantic spaces, to synthesise diverse, generic and transferable visual features. We develop a masked learning strategy to promote diversity within the same class visual features and enhance the separation between different classes. We further cast the visual features into a prototypical space to model their distribution for alignment with the corresponding semantic space. Finally, we develop a consistency regularizer to preserve the semantic-visual relationships between the real-seen features and synthetic-unseen features. Our approach shows considerable semantic segmentation gains on ScanNet, S3DIS and SemanticKITTI benchmarks. Our code is available at: https://github.com/leolyj/3DPC-GZSL
Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields
Authors: Zelin Gao, Weichen Dai, Yu Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grain gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.
NeRFrac: Neural Radiance Fields through Refractive Surface
Authors: Yifan Zhan, Shohei Nobuhara, Ko Nishino, Yinqiang Zheng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: NeRF, Novel View Synthesis

    Neural Radiance Fields (NeRF) is a popular neural expression for novel view synthesis. By querying spatial points and view directions, a multilayer perceptron (MLP) can be trained to output the volume density and radiance at each point, which lets us render novel views of the scene. The original NeRF and its recent variants, however, target opaque scenes dominated by diffuse reflection surfaces and cannot handle complex refractive surfaces well. We introduce NeRFrac to realize neural novel view synthesis of scenes captured through refractive surfaces, typically water surfaces. For each queried ray, an MLP-based Refractive Field is trained to estimate the distance from the ray origin to the refractive surface. A refracted ray at each intersection point is then computed by Snell's Law, given the input ray and the approximated local normal. Points of the scene are sampled along the refracted ray and are sent to a Radiance Field for further radiance estimation. We show that from a sparse set of images, our model achieves accurate novel view synthesis of the scene underneath the refractive surface and simultaneously reconstructs the refractive surface. We evaluate the effectiveness of our method with synthetic and real scenes seen through water surfaces. Experimental results demonstrate the accuracy of NeRFrac for modeling scenes seen through wavy refractive surfaces.
SGAligner: 3D Scene Alignment with Scene Graphs
Authors: Sayan Deb Sarkar, Ondrej Miksik, Marc Pollefeys, Daniel Barath, Iro Armeni
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contrastive Learning, Knowledge Graphs

    Building 3D scene graphs has recently emerged as a topic in scene representation for several embodied AI applications to represent the world in a structured and rich manner. With their increased use in solving downstream tasks (e.g., navigation and room rearrangement), can we leverage and recycle them for creating 3D maps of environments, a pivotal step in agent operation? We focus on the fundamental problem of aligning pairs of 3D scene graphs whose overlap can range from zero to partial and can contain arbitrary changes. We propose SGAligner, the first method for aligning pairs of 3D scene graphs that is robust to in-the-wild scenarios (i.e., unknown overlap - if any - and changes in the environment). We get inspired by multi-modality knowledge graphs and use contrastive learning to learn a joint, multi-modal embedding space. We evaluate on the 3RScan dataset and further showcase that our method can be used for estimating the transformation between pairs of 3D scenes. Since benchmarks for these tasks are missing, we create them on this dataset. The code, benchmark, and trained models are available on the project website.
Space-time Prompting for Video Class-incremental Learning
Authors: Yixuan Pei, Zhiwu Qing, Shiwei Zhang, Xiang Wang, Yingya Zhang, Deli Zhao, Xueming Qian
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning

    Recently, prompt-based learning has made impressive progress on image class-incremental learning, but it still lacks sufficient exploration in the video domain. In this paper, we will fill this gap by learning multiple prompts based on a powerful image-language pre-trained model, i.e., CLIP, making it fit for video class-incremental learning (VCIL). For this purpose, we present a space-time prompting approach (ST-Prompt) which contains two kinds of prompts, i.e., task-specific prompts and task-agnostic prompts. The task-specific prompts are to address the catastrophic forgetting problem by learning multi-grained prompts, i.e., spatial prompts, temporal prompts and comprehensive prompts, for accurate task identification. The task-agnostic prompts maintain a globally-shared prompt pool, which can empower the pre-trained image models with temporal perception abilities by exchanging contexts between frames. By this means, ST-Prompt can transfer the plentiful knowledge in the image-language pre-trained models to the VCIL task with only a tiny set of prompts to be optimized. To evaluate ST-Prompt, we conduct extensive experiments on three standard benchmarks. The results show that ST-Prompt can significantly surpass the state-of-the-art VCIL methods, especially it gains 9.06% on HMDB51 dataset under the 1*25 stage setting.
HDG-ODE: A Hierarchical Continuous-Time Model for Human Pose Forecasting
Authors: Yucheng Xing, Xin Wang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Human Pose Forecasting, Pose Estimation

    Recently, human pose estimation has attracted more and more attention due to its importance in many real applications. Although many efforts have been put on extracting 2D poses from static images, there are still some severe problems to be solved. A critical one is occlusion, which is more obvious in multi-person scenarios and makes it even more difficult to recover the corresponding 3D poses. When we consider a sequence of images, the temporal correlation among the contexts can be utilized to help us ease the problem, but most of the current works only rely on discrete-time models and estimate the joint locations of all people within a whole sparse graph. In this paper, we propose a new framework, Hierarchical Dynamic Graph Ordinary Differential Equation (HDG-ODE), to tackle the 3D pose forecasting task from 2D skeleton representations in videos. Our framework adopts ODE, a continuous-time model, as the base to predict the 3D joint positions at any time. Considering the structural-property of the skeleton data in representing human poses and the possible irregularity caused by occlusion, we propose the use of dynamic graph convolution as the basic operator. To reduce the computational complexity introduced by the sparsity of the pose graph, our model takes a hierarchical structure where the encoding process at the observation timestamp is done in a cascade manner while the propagation between observations is conducted in parallel. The performance studies on several datasets demonstrate that our model is effective and can out-perform other methods with fewer parameters.
Sparse Instance Conditioned Multimodal Trajectory Prediction
Authors: Yonghao Dong, Le Wang, Sanping Zhou, Gang Hua
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Future prediction, Pedestrian Trajectory Prediction, Prediction, Trajectory Prediction

    Pedestrian trajectory prediction is critical in many vision tasks but challenging due to the multimodality of the future trajectory. Most existing methods predict multimodal trajectories conditioned by goals (future endpoints) or instances (all future points). However, goal-conditioned methods ignore the intermediate process and instance-conditioned methods ignore the stochasticity of pedestrian motions. In this paper, we propose a simple yet effective Sparse Instance Conditioned Network (SICNet), which gives a balanced solution between goal-conditioned and instance-conditioned methods. Specifically, SICNet learns comprehensive sparse instances, i.e., representative points of the future trajectory, through a mask generated by a long short-term memory encoder and uses the memory mechanism to store and retrieve such sparse instances. Hence SICNet can decode the observed trajectory into the future prediction conditioned on the stored sparse instance. Moreover, we design a memory refinement module that refines the retrieved sparse instances from the memory to reduce memory recall errors. Extensive experiments on ETH-UCY and SDD datasets show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate the superiority of our method compared with goal-conditioned and instance-conditioned approaches.
Rethinking Safe Semi-supervised Learning: Transferring the Open-set Problem to A Close-set One
Authors: Qiankun Ma, Jiyao Gao, Bo Zhan, Yunpeng Guo, Jiliu Zhou, Yan Wang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Conventional semi-supervised learning (SSL) lies in the close-set assumption that the labeled and unlabeled sets contain data with the same seen classes, called in-distribution (ID) data. In contrast, safe SSL investigates a more challenging open-set problem where unlabeled set may involve some out-of-distribution (OOD) data with unseen classes, which could harm the performance of SSL. When we are experimenting with the mainstream safe SSL methods, we have a surprising finding that all OOD data show a clear tendency to gather in the feature space. This inspires us to solve the safe SSL problem from a fresh perspective. Specifically, for a classification task with K seen classes, we utilize a prototype network not only to generate K prototypes of all seen classes, but also explicitly model an additional prototype for the OOD data, transferring the K-way classification on the open-set to the (K+1)-way on the close-set. In this way, the typical SSL techniques (e.g., consistency regularization and pseudo labeling) can be applied to tackle the safe SSL problem without additional consideration of OOD data processing like other safe SSL methods do. Particularly, considering the possible low-confidence pseudo labels, we further propose an iterative negative learning (INL) paradigm to enforce the network learning knowledge from complementary labels on wider classes, improving the network's classification performance. Extensive experiments on four benchmark datasets show that our approach remarkably lifts the performance on safe SSL and outperforms the state-of-the-art methods.
Lightweight Image Super-Resolution with Superpixel Token Interaction
Authors: Aiping Zhang, Wenqi Ren, Yi Liu, Xiaochun Cao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Super-Resolution, Superpixels, Super-Resolution

    Transformer-based methods have demonstrated impressive results on single-image super-resolution (SISR) task. However, self-attention mechanism is computationally expensive when applied to the entire image. As a result, current approaches divide low-resolution input images into small patches, which are processed separately and then fused to generate high-resolution images. Nevertheless, this conventional regular patch division is too coarse and lacks interpretability, resulting in artifacts and non-similar structure interference during attention operations. To address these challenges, we propose a novel super token interaction network (SPIN). Our method employs superpixels to cluster local similar pixels to form the explicable local regions and utilizes intra-superpixel attention to enable local information interaction. It is interpretable because only similar regions complement each other and dissimilar regions are excluded. Moreover, we design a superpixel cross-attention module to facilitate information propagation via the surrogation of superpixels. Extensive experiments demonstrate that the proposed SPIN model performs favorably against the state-of-the-art SR methods in terms of accuracy and lightweight. Code is available at https://github.com/ArcticHare105/SPIN.
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited Annotations
Authors: Qiming Xia, Jinhao Deng, Chenglu Wen, Hai Wu, Shaoshuai Shi, Xin Li, Cheng Wang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Object Detection, Contrastive Learning, object-detection, Object Detection, Pseudo Label

    Recently, 3D object detection with sparse annotations has received great attention. However, current detectors usually perform poorly under very limited annotations. To address this problem, we propose a novel Contrastive Instance feature mining method, named CoIn. To better identify indistinguishable features learned through limited supervision, we design a Multi-Class contrastive learning module (MCcont) to enhance feature discrimination.  Meanwhile, we propose a feature-level pseudo-label mining framework consisting of an instance feature mining module (InF-Mining) and a Labeled-to-Pseudo contrastive learning module (LPcont). These two modules exploit latent instances in feature space to supervise the training of detectors with limited annotations. Extensive experiments with KITTI dataset, Waymo open dataset, and nuScenes dataset show that under limited annotations, our method greatly improves the performance of baseline detectors: CenterPoint, Voxel-RCNN, and CasA. Combining CoIn with an iterative training strategy, we propose a CoIn++ pipeline, which requires only 2% annotations in the KITTI dataset to achieve performance comparable to the fully supervised methods. The code is available at https://github.com/xmuqimingxia/CoIn.
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
Authors: Jiachen Lu, Renyuan Peng, Xinyue Cai, Hang Xu, Hongyang Li, Feng Wen, Wei zhang, Li Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a significant challenge due to the conflicting underlying combination of Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road topological connectivity) structures. Existing methods struggle to merge the two types of data domains effectively, but few of them address it properly. Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence. Further than modeling an auto-regressive sequence-to-sequence Transformer model to understand RoadNet Sequence, we decouple the dependency of RoadNet Sequence into a mixture of auto-regressive and non-autoregressive dependency. Building on this, our proposed non-autoregressive sequence-to-sequence approach leverages non-autoregressive dependencies while fixing the gap towards auto-regressive dependencies, resulting in success on both efficiency and accuracy. Extensive experiments on nuScenes dataset demonstrate the superiority of RoadNet Sequence representation and the non-autoregressive approach compared to existing state-of-the-art alternatives.
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation
Authors: Yun Wang, Cheng Chi, Min Lin, Xin Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Autonomous Driving, Computational Efficiency, Scene Flow Estimation

    Scene flow estimation, which predicts the 3D displacements of point clouds, is a fundamental task in autonomous driving. Most methods have adopted a coarse-to-fine structure to balance computational efficiency with accuracy, particularly when handling large displacements. However, inaccuracies in the initial coarse layer's scene flow estimates may accumulate, leading to incorrect final estimates. To alleviate this, we introduce a novel Iterative Hierarchical Network----IHNet. This approach circulates high-resolution estimated information (scene flow and feature) from the preceding iteration back to the low-resolution layer of the current iteration. Serving as a guide, the high-resolution estimated scene flow, instead of initializing the scene flow from zero, provides a more precise center for low-resolution layer to identify matches. Meanwhile, the decoder's feature at the high-resolution layer can contribute essential movement information. Furthermore, based on the recurrent structure, we design a resampling scheme to enhance the correspondence between points across two consecutive frames. By employing the previous estimated scene flow to fine-tune the target frame's coordinates, we can significantly reduce the correspondence discrepancy between two frame points, a problem often caused by point sparsity. Following this adjustment, we continue to estimate the scene flow using the newly updated coordinates, along with the reencoded feature. Our approach outperforms the recent state-of-the-art method WSAFlowNet by 20.1% on FlyingThings3D and 56.0% on KITTI scene flow datasets according to EPE3D metric. The code is available at https://github.com/wangyunlhr/IHNet.
INT2: Interactive Trajectory Prediction at Intersections
Authors: Zhijie Yan, Pengfei Li, Zheng Fu, Shaocong Xu, Yongliang Shi, Xiaoxue Chen, Yuhang Zheng, Yang Li, Tianyu Liu, Chuxuan Li, Nairui Luo, Xu Gao, Yilun Chen, Zuoxu Wang, Yifeng Shi, Pengfei Huang, Zhengxiao Han, Jirui Yuan, Jiangtao Gong, Guyue Zhou, Hang Zhao, Hao Zhao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Autonomous Driving, Motion Forecasting, Prediction, Trajectory Prediction

    Motion forecasting is an important component in autonomous driving systems. One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents. To this end, we present a large-scale interactive trajectory prediction dataset named INT2 for INTeractive trajectory prediction at INTersections. INT2 includes 612,000 scenes, each lasting 1 minute, containing up to 10,200 hours of data. The agent trajectories are auto-labeled by a high-performance offline temporal detection and fusion algorithm, whose quality is further inspected by human judges. Vectorized semantic maps and traffic light information are also included in INT2. Additionally, the dataset poses an interesting domain mismatch challenge.  For each intersection, we treat rush-hour and non-rush-hour segments as different domains. We benchmark the best open-sourced interactive trajectory prediction method on INT2 and Waymo Open Motion, under in-domain and cross-domain settings. The dataset, code and models are publicly available at https://github.com/AIR-DISCOVER/INT2.
Joint Implicit Neural Representation for High-fidelity and Compact Vector Fonts
Authors: Chia-Hao Chen, Ying-Tian Liu, Zhifei Zhang, Yuan-Chen Guo, Song-Hai Zhang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Font Generation

    Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical scenarios. In this paper, we propose to learn vector fonts from pixelated font images utilizing a joint neural representation that consists of a signed distance field (SDF) and a probabilistic corner field (CF) to capture shape corner details. To achieve smooth shape interpolation on the learned shape manifold, we establish connections between the two fields for better alignment. We further design a vectorization process to extract high-quality and compact vector fonts from our joint neural representation. Experiments demonstrate that our method can generate more visually appealing vector fonts with a higher level of compactness compared to existing alternatives.
Improved Knowledge Transfer for Semi-Supervised Domain Adaptation via Trico Training Strategy
Authors: Ba Hung Ngo, Yeon Jeong Chae, Jung Eun Kwon, Jae Hyeon Park, Sung In Cho
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Adaptation, Semi-supervised Domain Adaptation, Transfer Learning

    The motivation of the semi-supervised domain adaptation (SSDA) is to train a model by leveraging knowledge acquired from the plentiful labeled source combined with extremely scarce labeled target data to achieve the lowest error on the unlabeled target data at the testing time. However, due to inter-domain and intra-domain discrepancies, the improvement of classification accuracy is limited. To solve these, we propose the Trico-training method that utilizes a multilayer perceptron (MLP) classifier and two graph convolutional network (GCN) classifiers called inter-view GCN and intra-view GCN classifiers. The first co-training strategy exploits a correlation between MLP and inter-view GCN classifiers to minimize the inter-domain discrepancy, in which the inter-view GCN classifier provides its pseudo labels to teach the MLP classifier, which encourages class representation alignment across domains. In contrast, the MLP classifier gives feedback to the inter-view GCN classifier by using a new concept, 'pseudo-edge', for neighbor's feature aggregation. Doing this increases the data structure mining ability of the inter-view GCN classifier; thus, the quality of generated pseudo labels is improved. The second co-training strategy between MLP and intra-view GCN is conducted in a similar way to reduce the intra-domain discrepancy by enhancing the correlation between labeled and unlabeled target data. Due to an imbalance in classification accuracy between inter-view and intra-view GCN classifiers, we propose the third co-training strategy that encourages them to cooperate to address this problem. We verify the effectiveness of the proposed method on three standard SSDA benchmark datasets: Office-31, Office-Home, and DomainNet. The extended experimental results show that our method surpasses the prior state-of-the-art approaches in SSDA.