pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Sim2Real Gap Using Digital Twins
Authors: Sruthi Sudhakar, Jon Hanzelka, Josh Bobillot, Tanmay Randhavane, Neel Joshi, Vibhav Vineet
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

    It is very time consuming to create datasets for training computer vision models. An emerging alternative is to use synthetic data, but if the synthetic data is not similar enough to the real data, the performance is typically below that of training with real data. Thus using synthetic data still requires a large amount of time, money, and skill as one needs to author the data carefully. In this paper, we seek to understand which aspects of this authoring process are most critical. We present an analysis of which factors of variation between simulated and real data are most important. We capture images of YCB objects to create a novel YCB-Real dataset. We then create a novel synthetic "digital twin" dataset, YCB-Synthetic, which matches the YCB-Real dataset and includes variety of artifacts added to the synthetic data. We study the affects of these artifacts on our dataset and two existing published datasets on two different computer vision tasks: object detection and instance segmentation. We provide an analysis of the cost-benefit trade-offs between artist time for fixing artifacts and trained model accuracy. We plan to release this dataset (images and 3D assets) so they can be further used by the community.
Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning
Authors: Shuo He, Guowu Yang, Lei Feng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Partial Label Learning

    In partial-label learning (PLL), each training example has a set of candidate labels, among which only one is the true label. Most existing PLL studies focus on the instance-independent (II) case, where the generation of candidate labels is only dependent on the true label. However, this II-PLL paradigm could be unrealistic, since candidate labels are usually generated according to the specific features of the instance. Therefore, instance-dependent PLL (ID-PLL) has attracted increasing attention recently. Unfortunately, existing ID-PLL studies lack an insightful perception of the intrinsic challenge in ID-PLL. In this paper, we start with an empirical study of the dynamics of label disambiguation in both II-PLL and ID-PLL. We found that the performance degradation of ID-PLL stems from the inaccurate supervision caused by massive under-disambiguated (UD) examples that do not achieve complete disambiguation. To solve this problem, we propose a novel two-stage PLL framework including selective disambiguation and candidate-aware thresholding. Specifically, we first choose a part of well-disambiguated (WD) examples based on the magnitude of normalized entropy (NE) and integrate harmless complementary supervision from the remaining ones to train two networks. Next, the remaining examples whose NE is lower than the specific class-wise WD-NE threshold are selected as additional WD ones. Meanwhile, the remaining UD examples, whose NE is lower than the self-adaptive UD-NE threshold and whose predictions from two networks are agreed, are also regarded as WD ones for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.
DPS-Net: Deep Polarimetric Stereo Depth Estimation
Authors: Chaoran Tian, Weihong Pan, Zimo Wang, Mao Mao, Guofeng Zhang, Hujun Bao, Ping Tan, Zhaopeng Cui
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Depth Estimation, Stereo Depth Estimation

    Stereo depth estimation usually struggles to deal with textureless scenes for both traditional and learning-based methods due to the inherent dependence on image correspondence matching. In this paper, we propose a novel neural network, i.e., DPS-Net, to exploit both the prior geometric knowledge and polarimetric information for depth estimation with two polarimetric stereo images. Specifically, we construct both RGB and polarization correlation volumes to fully leverage the multi-domain similarity between polarimetric stereo images. Since inherent ambiguities exist in the polarization images, we introduce the iso-depth cost explicitly into the network to solve these ambiguities. Moreover, we design a cascaded dual-GRU architecture to recurrently update the disparity and effectively fuse both the multi-domain correlation features and the iso-depth cost. Besides, we present new synthetic and real polarimetric stereo datasets for evaluation. Experimental results demonstrate that our method outperforms the state-of-the-art stereo depth estimation methods.
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking
Authors: Rui Li, Baopeng Zhang, Jun Liu, Wei Liu, Jian Zhao, Zhu Teng
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Active Learning, Diversity, Multi-Object Tracking, Object Tracking

    The existing one-stage multi-object tracking (MOT) algorithms have achieved satisfactory performance benefiting from a large amount of labeled data. However, acquiring plenty of laborious annotated frames is not practical in real applications. To reduce the cost of human annotations, we propose Heterogeneous Diversity driven Active Multi-Object Tracking (HD-AMOT), to infer the most informative frames for any MOT tracker by observing the heterogeneous cues of samples. HD-AMOT defines the diversified informative representation by encoding the geometric and semantic information, and formulates the frame inference strategy as a Markov decision process to learn an optimal sampling policy based on the designed informative representation. Specifically, HD-AMOT consists of a diversified informative representation module as well as an informative frame selection network. The former produces the signal characterizing the diversity and distribution of frames, and the latter receives the signal and conducts multi-frame cooperation to enable batch frame sampling. Extensive experiments conducted on the MOT15, MOT17, MOT20, and Dancetrack datasets demonstrate the efficacy and effectiveness of HD-AMOT. Experiments show that under 50% budget our HD-AMOT can achieve similar or even higher performance as fully-supervised learning.
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale Upsampling
Authors: Guangyuan Li, Lei Zhao, Jiakai Sun, Zehua Lan, Zhanjie Zhang, Jiafu Chen, Zhijie Lin, Huaizhong Lin, Wei Xing
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Super-Resolution

    Recently, several methods have explored the potential of multi-contrast magnetic resonance imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR methods. However, existing approaches still have two shortcomings: (1) They can only address fixed integer upsampling scales, such as 2x, 3x, and 4x, which require training and storing the corresponding model separately for each upsampling scale in clinic. (2) They lack direct interaction among different windows as they adopt the square window (e.g., 8x8) transformer network architecture, which results in inadequate modelling of longer-range dependencies. Moreover, the relationship between reference images and target images is not fully mined. To address these issues, we develop a novel network for multi-contrast MRI arbitrary-scale SR, dubbed as McASSR. Specifically, we design a rectangle-window cross-attention transformer to establish longer-range dependencies in MR images without increasing computational complexity and fully use reference information. Besides, we propose the reference-aware implicit attention as an upsampling module, achieving arbitrary-scale super-resolution via implicit neural representation, further fusing supplementary information of the reference image. Extensive and comprehensive experiments on both public and clinical datasets show that our McASSR yields superior performance over SOTA methods, demonstrating its great potential to be applied in clinical practice.
IntentQA: Context-aware Video Intent Reasoning
Authors: Jiapeng Li, Ping Wei, Wenjuan Han, Lifeng Fan
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Contrastive Learning, Video Question Answering

    In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video intent reasoning, which has become increasingly important for AI with its advantages in equipping AI agents with the capability of reasoning beyond mere recognition in daily tasks. We also contribute a large-scale VideoQA dataset for this task. We propose a Context-aware Video Intent Reasoning model (CaVIR) consisting of i) Video Query Language (VQL) for better cross-modal representation of the situational context, ii) Contrastive Learning module for utilizing the contrastive context, and iii) Commonsense Reasoning module for incorporating the commonsense context. Comprehensive experiments on this challenging task demonstrate the effectiveness of each model component, the superiority of our full model over other baselines, and the generalizability of our model to a new VideoQA task. The dataset and codes are open-sourced at: https://github.com/JoseponLee/IntentQA.git
Bidirectional Alignment for Domain Adaptive Detection with Transformers
Authors: Liqiang He, Wei Wang, Albert Chen, Min Sun, Cheng-Hao Kuo, Sinisa Todorovic
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Object, object-detection, Object Detection

    We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature representations. Since different image parts and objects may exhibit various degrees of domain-specific characteristics, directly applying GRL on a global image or object representation may not be suitable. Our proposed BiADT explicitly estimates token-wise domain-invariant and domain-specific features in the image and object token sequences. BiADT has a novel deformable attention and self-attention, aimed at bi-directional domain alignment and mutual information minimization. These two objectives reduce the domain gap in domain-invariant representations, and simultaneously increase the distinctiveness of domain-specific features. Our experiments show that BiADT achieves very competitive performance to SOTA consistently on Cityscapes-to-FoggyCityscapes, Sim10K-to-Citiscapes and Cityscapes-to-BDD100K, outperforming the strong baseline, AQT, by 2.0, 2.1, and 2.4 in mAP50, respectively.
SKiT: a Fast Key Information Video Transformer for Online Surgical Phase Recognition
Authors: Yang Liu, Jiayu Huo, Jingjing Peng, Rachel Sparks, Prokar Dasgupta, Alejandro Granados, Sebastien Ourselin
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Online surgical phase recognition, Surgical phase recognition

    This paper introduces SKiT, a fast Key information Transformer for phase recognition of videos. Unlike previous methods that rely on complex models to capture long-term temporal information, SKiT accurately recognizes high-level stages of videos using an efficient key pooling operation. This operation records important key information by retaining the maximum value recorded from the beginning up to the current video frame, with a time complexity of O(1). Experimental results on Cholec80 and AutoLaparo surgical datasets demonstrate the ability of our model to recognize phases in an online manner. SKiT achieves higher performance than state-of-the-art methods with an accuracy of 92.5% and 82.9% on Cholec80 and AutoLaparo, respectively, while running the temporal model eight times faster (  7ms v.s. 55ms) than LoViT, which uses ProbSparse to capture global information. We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition. To sum up, we propose an effective and efficient model for surgical phase recognition that leverages key global information. This has an intrinsic value when performing this task in an online manner on long surgical videos for stable real-time surgical recognition systems.
Visible-Infrared Person Re-Identification via Semantic Alignment and Affinity Inference
Authors: Xingye Fang, Yang Yang, Ying Fu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Person Re-Identification

    Visible-infrared person re-identification (VI-ReID) focuses on matching the pedestrian images of the same identity captured by different modality cameras. The part-based methods achieve great success by extracting fine-grained features from feature maps. But most existing part-based methods employ horizontal division to obtain part features suffering from misalignment caused by irregular pedestrian movements. Moreover, most current methods use Euclidean or cosine distance of the output features to measure the similarity without considering the pedestrian relationships. Misaligned part features and naive inference methods both limit the performance of existing works. We propose a Semantic Alignment and Affinity Inference framework (SAAI), which aims to align latent semantic part features with the learnable prototypes and improve inference with affinity information. Specifically, we first propose semantic-aligned feature learning that employs the similarity between pixel-wise features and learnable prototypes to aggregate the latent semantic part features. Then, we devise an affinity inference module to optimize the inference with pedestrian relationships. Comprehensive experimental results conducted on the SYSU-MM01 and RegDB datasets demonstrate the favorable performance of our SAAI framework. Our code will be released at https://github.com/xiaoye-hhh/SAAI.
A Game of Bundle Adjustment - Learning Efficient Convergence
Authors: Amir Belder, Refael Vivanti, Ayellet Tal
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Bundle adjustment is the common way to solve localization and mapping. It is an iterative process in which a system of non-linear equations is solved using two optimization methods, weighted by a damping factor. In the classic approach, the latter is chosen heuristically by the Levenberg-Marquardt algorithm on each iteration. This might take many iterations, making the process computationally expensive, which might be harmful to real-time applications. We propose to replace this heuristic by viewing the problem in a holistic manner, as a game, and formulating it as a reinforcement-learning task. We set an environment which solves the non-linear equations and train an agent to choose the damping factor in a learned manner. We demonstrate that our approach considerably reduces the number of iterations required to reach the bundle adjustment's convergence, on both synthetic and real-life scenarios. We show that this reduction benefits the classic approach and can be integrated with other bundle adjustment acceleration methods.
MAAL: Multimodality-Aware Autoencoder-Based Affordance Learning for 3D Articulated Objects
Authors: Yuanzhi Liang, Xiaohan Wang, Linchao Zhu, Yi Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: MME, Object

    Inferring affordance for 3D articulated objects is a challenging and practical problem. It is a primary problem for applying robots to real-world scenarios. The exploration can be summarized as figuring out where to act and how to act. Correspondingly, the task mainly requires producing actionability scores, action proposals, and success likelihood scores according to the given 3D object information and robotic information. Current works usually directly process multi-modal inputs with early fusion and apply critic networks to produce scores, which leads to insufficient multi-modal learning ability and inefficiently iterative training in multiple stages. This paper proposes a novel Multimodality-Aware Autoencoder-based affordance Learning (MAAL) for the 3D object affordance problem. It is an efficient pipeline, trained in one go, and only requires a few positive samples in training data. More importantly, MAAL contains a MultiModal Energized Encoder (MME) for better multi-modal learning. It comprehensively models all multi-modal inputs from 3D objects and robotic actions. Jointly considering information from multiple modalities, the encoder further learns interactions between robots and objects. MME empowers the better multi-modal learning ability for understanding object affordance. Experimental results and visualizations, based on a large-scale dataset PartNet-Mobility, show the effectiveness of MAAL in learning multi-modal data and solving the 3D articulated object affordance problem.
Enhancing Non-line-of-sight Imaging via Learnable Inverse Kernel and Attention Mechanisms
Authors: Yanhua Yu, Siyuan Shen, Zi Wang, Binbin Huang, Yuehan Wang, Xingyue Peng, Suan Xia, Ping Liu, Ruiqian Li, Shiying Li
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    Recovering information from non-line-of-sight (NLOS) imaging is a computationally-intensive inverse problem. Most physics-based NLOS imaging methods address the complexity of this problem by assuming three-bounce reflections and no self-occlusion. However, these assumptions may break down for objects with large depth variations, preventing physics-based algorithms from accurately reconstructing the details and high-frequency information. On the other hand, while learning-based methods can avoid these assumptions, they may struggle to reconstruct details without specific designs due to the spectral bias of neural networks. To overcome these issues, we propose a novel approach that enhances physics-based NLOS imaging methods by introducing a learnable inverse kernel in the Fourier domain and using an attention mechanism to improve the neural network to learn high-frequency information. Our method is evaluated on publicly available and new synthetic datasets, demonstrating its commendable performance compared to prior physics-based and learning-based methods, especially for objects with large depth variations. Moreover, our approach generalizes well to real data and can be applied to tasks such as classification and depth reconstruction. We will make our code and dataset publicly available: https://sci2020.github.io.
Environment Agnostic Representation for Visual Reinforcement Learning
Authors: Hyesong Choi, Hunsang Lee, Seongwon Jeong, Dongbo Min
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Deep Reinforcement Learning, Domain Generalization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

    Generalization capability of vision-based deep reinforcement learning (RL) is indispensable to deal with dynamic environment changes that exist in visual observations. The high-dimensional space of the visual input, however, imposes challenges in adapting an agent to unseen environments. In this work, we propose Environment Agnostic Reinforcement learning (EAR), which is a compact framework for domain generalization of the visual deep RL. Environment-agnostic features (EAFs) are extracted by leveraging three novel objectives based on feature factorization, reconstruction, and episode-aware state shifting, so that policy learning is accomplished only with vital features. EAR is a simple single-stage method with a low model complexity and a fast inference time, ensuring a high reproducibility, while attaining state-of-the-art performance in the DeepMind Control Suite and DrawerWorld benchmarks.
Attention Discriminant Sampling for Point Clouds
Authors: Cheng-Yao Hong, Yu-Ying Chou, Tyng-Luh Liu
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: object-detection, Object Detection, Scene Segmentation, Semantic Similarity, Semantic Textual Similarity

    This paper describes an attention-driven approach to 3-D point cloud sampling. We establish our method based on a structure-aware attention discriminant analysis that explores geometric and semantic relations embodied among points and their clusters. The proposed attention discriminant sampling (ADS) starts by efficiently decomposing a given point cloud into clusters to implicitly encode its structural and geometric relatedness among points. By treating each cluster as a structural component, ADS then draws on evaluating two levels of self-attention: within-cluster and between-cluster. The former reflects the semantic complexity entailed by the learned features of points within each cluster, while the latter reveals the semantic similarity between clusters. Driven by structurally preserving the point distribution, these two aspects of self-attention help avoid sampling redundancy and decide the number of sampled points in each cluster. Extensive experiments demonstrate that ADS significantly improves classification performance to 95.1% on ModelNet40 and 87.5% on ScanObjectNN and achieves 86.9% mIoU on ShapeNet Part Segmentation. For scene segmentation, ADS yields 91.1% accuracy on S3DIS with higher mIoU to the state-of-the-art and 75.6% mIoU on ScanNetV2. Furthermore, ADS surpasses the state-of-the-art with 55.0% mAP50 on ScanNetV2 object detection.
SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection
Authors: Zhihao Sun, Haoran Jiang, Danding Wang, Xirong Li, Juan Cao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Manipulation, Image Manipulation Detection

    Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.