pwc-archive/papers-with-abstracts huggingface dataset:
Automated Knowledge Distillation via Monte Carlo Tree Search
Authors: Lujun Li, Peijie Dong, Zimian Wei, Ya Yang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: image-classification, Image Classification, Knowledge Distillation, object-detection, Object Detection, Semantic Segmentation

    In this paper, we present Auto-KD, the first automated search framework for optimal knowledge distillation design. Traditional distillation techniques typically require handcrafted designs by experts and extensive tuning costs for different teacher-student pairs. To address these issues, we empirically study different distillers, finding that they can be decomposed, combined, and simplified. Based on these observations, we build our uniform search space with advanced operations in transformations, distance functions, and hyperparameters components. For instance, the transformation parts are optional for global, intra-spatial, and inter-spatial operations, such as attention, mask, and multi-scale. Then, we introduce an effective search strategy based on the Monte Carlo tree search, modeling the search space as a Monte Carlo Tree (MCT) to capture the dependency among options. The MCT is updated using test loss and representation gap of student trained by candidate distillers as the reward for better exploration-exploitation balance. To accelerate the search process, we exploit offline processing without teacher inference, sparse training for student, and proxy settings based on distillation properties. In this way, our Auto-KD only needs small costs to search for optimal distillers before the distillation phase. Moreover, we expand Auto-KD for multi-layer and multi-teacher scenarios with training-free weighted factors. Our method is promising yet practical, and extensive experiments demonstrate that it generalizes well to different CNNs and Vision Transformer models and attains state-of-the-art performance across a range of vision tasks, including image classification, object detection, and semantic segmentation. Code is provided at https://github.com/lilujunai/Auto-KD.
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning
Authors: Tamasha Malepathirana, Damith Senanayake, Saman Halgamuge
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, Incremental Learning, Non-exemplar-based Class Incremental Learning, Quantization

    Catastrophic forgetting; the loss of old knowledge upon acquiring new knowledge, is a pitfall faced by deep neural networks in real-world applications. Many prevailing solutions to this problem rely on storing exemplars (previously encountered data), which may not be feasible in applications with memory limitations or privacy constraints. Therefore, the recent focus has been on Non-Exemplar based Class Incremental Learning (NECIL) where a model incrementally learns about new classes without using any past exemplars. However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap. We propose NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization, a framework that reduces this class overlap in NECIL. We draw inspiration from Neural Gas to learn the topological relationships in the feature space, identifying the neighboring classes that are most likely to get confused with each other. This neighborhood information is utilized to enforce strong separation between the neighboring classes as well as to generate old class representative prototypes that can better aid in obtaining a discriminative decision boundary between old and new classes. Our comprehensive experiments on CIFAR-100, TinyImageNet, and ImageNet-Subset demonstrate that NAPA-VQ outperforms the State-of-the-art NECIL methods by an average improvement of 5%, 2%, and 4% in accuracy and 10%, 3%, and 9% in forgetting respectively. Our code can be found in https://github.com/TamashaM/NAPA-VQ.git.
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario
Authors: Zhixuan Li, Weining Ye, Juan Terven, Zachary Bennett, Ying Zheng, Tingting Jiang, Tiejun Huang
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Amodal Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

    Amodal Instance Segmentation (AIS) endeavors to accurately deduce complete object shapes that are partially or fully occluded. However, the inherent ill-posed nature of single-view datasets poses challenges in determining occluded shapes. A multi-view framework may help alleviate this problem, as humans often adjust their perspective when encountering occluded objects. At present, this approach has not yet been explored by existing methods and datasets. To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation. MUVA provides comprehensive annotations, including multi-view amodal/visible segmentation masks, 3D models, and depth maps, making it the largest image-level AIS dataset in terms of both the number of images and instances. Additionally, we propose a new method for aggregating representative features across different instances and views, which demonstrates promising results in accurately predicting occluded objects from one viewpoint by leveraging information from other viewpoints. Besides, we also demonstrate that MUVA can benefit the AIS task in real-world scenarios.
MiniROAD: Minimal RNN Framework for Online Action Detection
Authors: Joungbin An, Hyolim Kang, Su Ho Han, Ming-Hsuan Yang, Seon Joo Kim
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Detection, Online Action Detection

    Online Action Detection (OAD) is the task of identifying actions in streaming videos without access to future frames. Much effort has been devoted to effectively capturing long-range dependencies, with transformers receiving the spotlight for their ability to capture long-range temporal structures. In contrast, RNNs have received less attention lately, due to their lower performance compared to recent methods that utilize transformers. In this paper, we investigate the underlying reasons for the inferior performance of RNNs compared to transformer-based algorithms. Our findings indicate that the discrepancy between training and inference is the primary hindrance to the effective training of RNNs. To address this, we propose applying non-uniform weights to the loss computed at each time step, which allows the RNN model to learn from the predictions made in an environment that better resembles the inference stage. Extensive experiments on three benchmark datasets, THUMOS, TVSeries, and FineAction demonstrate that a minimal RNN-based model trained with the proposed methodology performs equally or better than the existing best methods with a significant increase in efficiency. The code is available at https://github.com/jbistanbul/MiniROAD.
Occ^2Net: Robust Image Matching Based on 3D Occupancy Estimation for Occluded Regions
Authors: Miao Fan, Mingrui Chen, Chen Hu, Shuchang Zhou
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Retrieval, Inductive Bias, Pose Estimation, Retrieval, Simultaneous Localization and Mapping

    Image matching is a fundamental and critical task in various visual applications, such as Simultaneous Localization and Mapping (SLAM) and image retrieval, which require accurate pose estimation. However, most existing methods ignore the occlusion relations between objects caused by camera motion and scene structure. In this paper, we propose Occ^2Net, a novel image matching method that models occlusion relations using 3D occupancy and infers matching points in occluded regions.  Thanks to the inductive bias encoded in the Occupancy Estimation (OE) module, it greatly simplifies bootstrapping of a multi-view consistent 3D representation that can then integrate information from multiple views. Together with an Occlusion-Aware (OA) module, it incorporates attention layers and rotation alignment to enable matching between occluded and visible points.  We evaluate our method on both real-world and simulated datasets and demonstrate its superior performance over state-of-the-art methods on several metrics, especially in occlusion scenarios.
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning
Authors: Haoyang Cheng, Haitao Wen, Xiaoliang Zhang, Heqian Qiu, Lanxiao Wang, Hongliang Li
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Continual Self-Supervised Learning, Self-Supervised Learning

    Self-supervised learning has attracted a lot of attention recently, which is able to learn powerful representations without any manual annotations. However, self-supervised learning needs to develop the ability to continuously learn to cope with a variety of real-world challenges, i.e., Continual Self-Supervised Learning (CSSL). Catastrophic forgetting is a notorious problem in CSSL, where the model tends to forget the learned knowledge. In practice, simple rehearsal or regularization will bring extra negative effects while alleviating catastrophic forgetting in CSSL, e.g., overfitting on the rehearsal samples or hindering the model from encoding fresh information. In order to address catastrophic forgetting without overfitting on the rehearsal samples, we propose Augmentation Stability Rehearsal (ASR) in this paper, which selects the most representative and discriminative samples by estimating the augmentation stability for rehearsal. Meanwhile, we design a matching strategy for ASR to dynamically update the rehearsal buffer. In addition, we further propose Contrastive Continuity on Augmentation Stability Rehearsal (C2ASR) based on ASR. We show that C2ASR is an upper bound of the Information Bottleneck (IB) principle, which suggests that C2ASR essentially preserves as much information shared among seen task streams as possible to prevent catastrophic forgetting and dismisses the redundant information between previous task streams and current task stream to free up the ability to encode fresh information. Our method obtains a great achievement compared with state-of-the-art CSSL methods on a variety of CSSL benchmarks.
Cross-modal Scalable Hierarchical Clustering in Hyperbolic space
Authors: Teng Long, Nanne van Noord
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Activity Recognition, Clustering, Self-Supervised Learning

    Hierarchical clustering is a natural approach to discover ontologies from data. Yet, existing approaches are hampered by their inability to scale to large datasets and the discrete encoding of the hierarchy. We introduce scalable Hyperbolic Hierarchical Clustering (sHHC) which overcomes these limitations by learning continuous hierarchies in hyperbolic space. Our hierarchical clustering is of high quality and can be obtained in a fraction of the runtime.  Additionally, we demonstrate the strength of sHHC on a downstream cross-modal self-supervision task. By using the discovered hierarchies from sound and vision to construct continuous hierarchical pseudo-labels we can efficiently optimize a network for activity recognition and obtain competitive performance compared to recent self-supervised learning models. Our findings demonstrate the strength of Hyperbolic Hierarchical Clustering and its potential for Self-Supervised Learning.
MUter: Machine Unlearning on Adversarially Trained Models
Authors: Junxu Liu, Mingsheng Xue, Jian Lou, XiaoYu Zhang, Li Xiong, Zhan Qin
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Machine Unlearning

    Machine unlearning is an emerging task of removing the influence of selected training datapoints from a trained model upon data deletion requests, which echoes the widely enforced data regulations mandating the Right to be Forgotten. Many unlearning methods have been proposed recently, achieving significant efficiency gains over the naive baseline of retraining from scratch. However, existing methods focus exclusively on unlearning from standard training models and do not apply to adversarial training models (ATMs) despite their popularity as effective defenses against adversarial examples. During adversarial training, the training data are involved in not only an outer loop for minimizing the training loss, but also an inner loop for generating the adversarial perturbation. Such bi-level optimization greatly complicates the influence measure for the data to be deleted and renders the unlearning more challenging than standard model training with single-level optimization.  This paper proposes a new approach called MUter for unlearning from ATMs. We derive a closed-form unlearning step underpinned by a total Hessian-related data influence measure, while existing methods can mis-capture the data influence associated with the indirect Hessian part. We further alleviate the computational cost by introducing a series of approximations and conversions to avoid the most computationally demanding parts of Hessian inversions. The efficiency and effectiveness of MUter have been validated through experiments on four datasets using both linear and neural network models.
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery
Authors: Rongyu Chen, Linlin Yang, Angela Yao
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: 3D Hand Pose Estimation, 3D Human Pose Estimation, 3D Human Reconstruction, Diversity, Human Mesh Recovery, Multi-Hypotheses 3D Human Pose Estimation

    For monocular RGB-based 3D pose and shape estimation, multiple solutions are often feasible due to factors like occlusion and truncation. This work presents a multi-hypothesis probabilistic framework by optimizing the Kullback-Leibler divergence (KLD) between the data and model distribution. Our formulation reveals a connection between the pose entropy and diversity in the multiple hypotheses that has been neglected by previous works. For a comprehensive evaluation, besides the best hypothesis (BH) metric, we factor in visibility for evaluating diversity. Additionally, our framework is label-friendly, in that it can be learned from only partial 2D keypoints, e.g., those that are visible. Experiments on both ambiguous and real-world benchmarks demonstrate that our method outperforms other state-of-the-art multi-hypothesis methods in a comprehensive evaluation. The project page is at https://gloryyrolg.github.io/MHEntropy.
Weakly-Supervised Action Segmentation and Unseen Error Detection in Anomalous Instructional Videos
Authors: Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Behzad Dariush
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Action Segmentation, Segmentation

    We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos. In the absence of an appropriate dataset for this task, we introduce the Anomalous Toy Assembly (ATA) dataset, which comprises 1152 untrimmed videos of 32 participants assembling three different toys, recorded from four different viewpoints. The training set comprises 27 participants who assemble toys in an expected and consistent manner, while the test and validation sets comprise 5 participants who display sequential anomalies in their task. We introduce a weakly labeled segmentation algorithm that is a generalization of the constrained Viterbi algorithm and identifies potential anomalous moments based on the difference between future anticipation and current recognition results. The proposed method is not restricted by the training transcripts during testing, allowing for the inference of anomalous action sequences while maintaining real-time performance. Based on these segmentation results, we also introduce a baseline to detect pre-defined human errors, and benchmark results on the ATA dataset. Experiments were conducted on the ATA and CSV datasets, demonstrating that the proposed method outperforms the state-of-the-art in segmenting anomalous videos under both online and offline conditions.
Simple and Effective Out-of-Distribution Detection via Cosine-based Softmax Loss
Authors: SoonCheol Noh, DongEon Jeong, Jee-Hyong Lee
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Out-of-Distribution Detection

    Deep learning models need to detect out-of-distribution (OOD) data in the inference stage because they are trained to estimate the train distribution and infer the data sampled from the distribution. Many methods have been proposed, but they have some limitations, such as requiring additional data, input processing, or high computational cost. Moreover, most methods have hyperparameters to be set by users, which have a significant impact on the detection rate. We propose a simple and effective OOD detection method by combining the feature norm and the Mahalanobis distance obtained from classification models trained with the cosine-based softmax loss. Our method is practical because it does not use additional data for training, is about three times faster when inferencing than the methods using the input processing, and is easy to apply because it does not have any hyperparameters for OOD detection. We confirm that our method is superior to or at least comparable to state-of-the-art OOD detection methods through the experiments.
Lens Parameter Estimation for Realistic Depth of Field Modeling
Authors: Dominique Piché-Meunier, Yannick Hold-Geoffroy, Jianming Zhang, Jean-François Lalonde
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: parameter estimation

    We present a method to estimate the depth of field effect from a single image. Most existing methods related to this task provide either a per-pixel estimation of blur and/or depth. Instead, we go further and propose to use a lens-based representation that models the depth of field using two parameters: the blur factor and focus disparity. Those two parameters, along with the signed defocus representation, result in a more intuitive and linear representation which we solve using a novel weighting network. Furthermore, our method explicitly enforces consistency between the estimated defocus blur, the lens parameters, and the depth map. Finally, we train our deep-learning-based model on a mix of real images with synthetic depth of field and fully synthetic images. These improvements result in a more robust and accurate method, as demonstrated by our state-of-the-art results. In particular, our lens parametrization enables several applications, such as 3D staging for AR environments and seamless object compositing.
CDAC: Cross-domain Attention Consistency in Transformer for Domain Adaptive Semantic Segmentation
Authors: Kaihong Wang, Donghyun Kim, Rogerio Feris, Margrit Betke
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

    While transformers have greatly boosted performance in semantic segmentation, domain adaptive transformers are not yet well explored. We identify that the domain gap can cause discrepancies in self-attention. Due to this gap, the transformer attends to spurious regions or pixels, which deteriorates accuracy on the target domain. We propose Cross-Domain Attention Consistency (CDAC), to perform adaptation on attention maps using cross-domain attention layers that share features between source and target domains. Specifically, we impose consistency between predictions from cross-domain attention and self-attention modules to encourage similar distributions across domains in both the attention and output of the model, i.e., attention-level and output-level alignment. We also enforce consistency in attention maps between different augmented views to further strengthen the attention-based alignment. Combining these two components, CDAC mitigates the discrepancy in attention maps across domains and further boosts the performance of the transformer under unsupervised domain adaptation settings. Our method is evaluated on various widely used benchmarks and outperforms the state-of-the-art baselines, including GTAV-to-Cityscapes by 1.3 and 1.5 percent point (pp) and Synthia-to-Cityscapes by 0.6 pp and 2.9 pp when combining with two competitive Transformer-based backbones, respectively. Our code will be publicly available at https://github.com/wangkaihong/CDAC.
DreamPose: Fashion Video Synthesis with Stable Diffusion
Authors: Johanna Karras, Aleksander Holynski, Ting-Chun Wang, Ira Kemelmacher-Shlizerman
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1

    We present DreamPose, a diffusion-based method for generating animated fashion videos from still images. Given an image and a sequence of human body poses, our method synthesizes a video containing both human and fabric motion. To achieve this, we transform a pretrained text-to-image model (Stable Diffusion) into a pose-and-image guided video synthesis model, using a novel finetuning strategy, a set of architectural changes to support the added conditioning signals, and techniques to encourage temporal consistency. We fine-tune on a collection of fashion videos from the UBC Fashion dataset. We evaluate our method on a variety of clothing styles and poses, and demonstrate that our method produces state-of-the-art results on fashion video animation. Video results are available at www.grail.cs.washington.edu/projects/dreampose.
Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing
Authors: Sivabalan Manivasagam, Ioan Andrei Bârsan, Jingkang Wang, Ze Yang, Raquel Urtasun
Date: 2023-01-01 00:00:00
Proceeding: ICCV 2023 1
Tasks: Motion Planning

    Testing the full autonomy system in simulation is the safest and most scalable way to evaluate autonomous vehicle performance before deployment. This requires simulating sensor inputs such as LiDAR. To be effective, it is essential that the simulation has low domain gap with the real world. That is, the autonomy system in simulation should perform exactly the same way it would in the real world for the same scenario. To date, there has been limited analysis into what aspects of LiDAR phenomena affect autonomy performance. It is also difficult to evaluate the domain gap of existing LiDAR simulators, as they operate on fully synthetic scenes. In this paper, we propose a novel "paired-scenario" approach to evaluating the domain gap of a LiDAR simulator by reconstructing digital twins of real world scenarios. We can then simulate LiDAR in the scene and compare it to the real LiDAR. We leverage this setting to analyze what aspects of LiDAR simulation, such as pulse phenomena, scanning effects, and asset quality, affect the domain gap with respect to the autonomy system, including perception, prediction, and motion planning, and analyze how modifications to the simulated LiDAR influence each part. We identify key aspects that are important to model, such as motion blur, material reflectance, and the accurate geometric reconstruction of traffic participants. This helps provide research directions for improving LiDAR simulation and autonomy robustness to these effects. For more information, please visit the project website: https://waabi.ai/lidar-dg