GenAI4ELab/papercli-papers huggingface dataset:
Incremental Task Learning with Incremental Rank Updates
Authors: Rakib Hyder; Ken Shao; Boyu Hou; Panos Markopoulos; Ashley Prater-Bennette; M. Salman Asif
Venue: ECCV 2022 (main)

Abstract: "Incremental Task learning (ITL) is a category of continual learning that seeks to train a single network for multiple tasks (one after another), where training data for each task is only available during the training of that task. Neural networks tend to forget older tasks when they are trained for the newer tasks; this property is often known as catastrophic forgetting. To address this issue, ITL methods use episodic memory, parameter regularization, masking and pruning, or extensible network structures. In this paper, we propose a new incremental task learning framework based on low-rank factorization. In particular, we represent the network weights for each layer as a linear combination of several rank-1 matrices. To update the network for a new task, we learn a rank-1 (or low-rank) matrix and add that to the weights of every layer. We also introduce an additional selector vector that assigns different weights to the low-rank matrices learned for the previous tasks. We show that our approach performs better than the current state-of-the-art methods in terms of accuracy and forgetting. Our method also offers better memory efficiency compared to episodic memory- and mask-based approaches. Our code will be available at https://github.com/CSIPlab/task-increment-rank-update.git."
Batch-Efficient EigenDecomposition for Small and Medium Matrices
Authors: Yue Song; Nicu Sebe; Wei Wang
Venue: ECCV 2022 (main)

Abstract: "EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in the deep neural networks. In this paper, we propose a QR-based ED method dedicated to the application scenarios of computer vision. Our proposed method performs the ED entirely by batched matrix/vector multiplication, which processes all the matrices simultaneously and thus fully utilizes the power of GPUs. Our technique is based on the explicit QR iterations by Givens rotation with double Wilkinson shifts. With several acceleration techniques, the time complexity of QR iterations is reduced from $O{(}n^5{)}$ to $O{(}n^3{)}$. The numerical test shows that for small and medium batched matrices (\emph{e.g.,} $dim{<}32$) our method can be much faster than the Pytorch SVD function. Experimental results on visual recognition and image generation demonstrate that our methods also achieve competitive performances."
Ensemble Learning Priors Driven Deep Unfolding for Scalable Video Snapshot Compressive Imaging
Authors: Chengshuai Yang; Shiyu Zhang; Xin Yuan
Venue: ECCV 2022 (main)

Abstract: "Snapshot compressive imaging (SCI) can record the 3D datacube by a 2D measurement and from this 2D measurement to reconstruct the desired 3D information by algorithms. The reconstruction algorithm thus plays a vital role in SCI. Recently, deep learning (DL) has demonstrated outstanding performance in reconstruction, leading to better results than conventional optimization based methods. Therefore, it is desired to improve DL reconstruction performance for SCI. Existing DL algorithms are limited by two bottlenecks: 1) a high accuracy network is usually large and requires a long running time; 2) DL algorithms are limited by scalability, i.e., a well trained network in general can not be applied to new systems. Towards this end, this paper proposes to use ensemble learning priors in DL to keep high reconstruction speed and accuracy in a single network. Furthermore, we develop the scalable learning approach during training to empower DL to handle data of different sizes without additional training. Extensive results on both simulation and real datasets demonstrate the superiority of our proposed algorithm. The code and model will be released."
Approximate Discrete Optimal Transport Plan with Auxiliary Measure Method
Authors: Dongsheng An; Na Lei; Xianfeng Gu
Venue: ECCV 2022 (main)

Abstract: "Optimal transport (OT) between two measures plays an essential role in many fields, ranging from economy, biology to machine learning and artificial intelligence. Conventional discrete OT problem can be solved using linear programming (LP). Unfortunately, due to the large scale and the intrinsic non-linearity, achieving discrete OT plan with adequate accuracy and efficiency is challenging. Generally speaking, the OT plan is highly sparse. This work proposes an auxiliary measure method to use the semi-discrete OT maps to estimate the sparsity of the discrete OT plan with squared Euclidean cost. Although obtaining the accurate semi-discrete OT maps is difficult, we can find the sparsity information through computing the approximate semi-discrete OT maps by convex optimization. The sparsity information can be further incorporated into the downstream LP optimization to greatly reduce the computational complexity and improve the accuracy. We also give a theoretic error bound between the estimated transport plan and the OT plan in terms of Wasserstein distance. Experiments on both synthetic data and color transfer tasks demonstrate the accuracy and efficiency of the proposed method."
A Comparative Study of Graph Matching Algorithms in Computer Vision
Authors: Stefan Haller; Lorenz Feineis; Lisa Hutschenreiter; Florian Bernard; Carsten Rother; Dagmar Kainmüller; Paul Swoboda; Bogdan Savchynskyy
Venue: ECCV 2022 (main)

Abstract: "The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: (i) popular problem instances are exactly solvable in substantially less than 1 second, and, therefore, are insufficient for future empirical evaluations; (ii) the most popular baseline methods are highly inferior to the best available methods; (iii) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices."
Improving Generalization in Federated Learning by Seeking Flat Minima
Authors: Debora Caldarola; Barbara Caputo; Marco Ciccone
Venue: ECCV 2022 (main)

Abstract: "Models trained in federated settings often suffer from degraded performances and fail at generalizing, especially when facing heterogeneous scenarios. In this work, we investigate such behavior through the lens of geometry of the loss and Hessian eigenspectrum, linking the model’s lack of generalization capacity to the sharpness of the solution. Motivated by prior studies connecting the sharpness of the loss surface and the generalization gap, we show that i) training clients locally with Sharpness-Aware Minimization (SAM) or its adaptive version (ASAM) and ii) averaging stochastic weights (SWA) on the server-side can substantially improve generalization in Federated Learning and help bridging the gap with centralized models. By seeking parameters in neighborhoods having uniform low loss, the model converges towards flatter minima and its generalization significantly improves in both homogeneous and heterogeneous scenarios. Empirical results demonstrate the effectiveness of those optimizers across a variety of benchmark vision datasets (e.g. CIFAR, Landmarks-User-160k, IDDA) and tasks (large scale classification, semantic segmentation, domain generalization)."
Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search: Tight or Not
Authors: Liangzu Peng; Mahyar Fazlyab; René Vidal
Venue: ECCV 2022 (main)

Abstract: "The rotation search problem aims to find a 3D rotation that best aligns a given number of point pairs. To induce robustness against outliers for rotation search, prior work considers truncated least-squares (TLS), which is a non-convex optimization problem, and its semidefinite relaxation (SDR) as a tractable alternative. Whether or not this SDR is theoretically tight in the presence of noise, outliers, or both has remained largely unexplored. We derive conditions that characterize the tightness of this SDR, showing that the tightness depends on the noise level, the truncation parameters of TLS, and the outlier distribution (random or clustered). In particular, we give a short proof for the tightness in the noiseless and outlier-free case, as opposed to the lengthy analysis of prior work."
Transfer without Forgetting
Authors: Matteo Boschini; Lorenzo Bonicelli; Angelo Porrello; Giovanni Bellitto; Matteo Pennisi; Simone Palazzo; Concetto Spampinato; Simone Calderara
Venue: ECCV 2022 (main)

Abstract: "This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge transfer during later tasks. On this ground, we propose Transfer without Forgetting (TwF), a hybrid approach building upon a fixed pretrained sibling network, which continuously propagates the knowledge inherent in the source domain through a layer-wise loss term. Our experiments indicate that TwF steadily outperforms other CL methods across a variety of settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety of datasets and different buffer sizes."
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
Authors: Farshid Varno; Marzie Saghayi; Laya Rafiee Sevyeri; Sharut Gupta; Stan Matwin; Mohammad Havaei
Venue: ECCV 2022 (main)

Abstract: "In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communicated to a central hub for aggregation. While FL is an appealing decentralized training paradigm, heterogeneity among data from different clients can cause the local optimization to drift away from the global objective. In order to estimate and therefore remove this drift, variance reduction techniques have been incorporated into FL optimization recently. However, these approaches inaccurately estimate the clients’ drift and ultimately fail to remove it properly. In this work, we propose an adaptive algorithm that accurately estimates drift across clients. In comparison to previous works, our approach necessitates less storage and communication bandwidth, as well as lower compute costs. Additionally, our proposed methodology induces stability by constraining the norm of estimates for client drift, making it more practical for large scale FL. Experimental findings demonstrate that the proposed algorithm converges significantly faster and achieves higher accuracy than the baselines across various FL benchmarks."
Tackling Long-Tailed Category Distribution under Domain Shifts
Authors: Xiao Gu; Yao Guo; Zeju Li; Jianing Qiu; Qi Dou; Yuxuan Liu; Benny Lo; Guang-Zhong Yang
Venue: ECCV 2022 (main)

Abstract: "Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2) the test data is drawn from different conditional distributions P(X|Y). Existing approaches cannot handle the scenario where both issues exist, which however is common for real-world applications. In this study, we took a step forward and looked into the problem of long-tailed classification under domain shifts. We designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Furthermore, we adopted a meta-learning framework which integrates these three blocks to improve domain generalization on unseen target domains. Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS. We evaluated our method on the two datasets and extensive experimental results demonstrate that our proposed method can achieve superior performance over state-of-the-art long-tailed/domain generalization approaches and the combinations. Source codes and datasets can be found at our project page https://xiaogu.site/LTDS."
Doubly-Fused ViT: Fuse Information from Vision Transformer Doubly with Local Representation
Authors: Li Gao; Dong Nie; Bo Li; Xiaofeng Ren
Venue: ECCV 2022 (main)

Abstract: "Vision Transformer (ViT) has recently emerged as a new paradigm for computer vision tasks, but is not as efficient as convolutional neural networks (CNN). In this paper, we propose an efficient ViT architecture, named Doubly-Fused ViT (DFvT), where we feed low-resolution feature maps to self-attention (SA) to achieve larger context with efficiency (by moving downsampling prior to SA), and enhance it with fine-detailed spatial information. SA is a powerful mechanism that extracts rich context information, thus could and should operate at a low spatial resolution. To make up for the loss of details, convolutions are fused into the main ViT pipeline, without incurring high computational costs. In particular, a Context Module (CM), consisting of fused downsampling operator and subsequent SA, is introduced to effectively capture global features with high efficiency. A Spatial Module (SM) is proposed to preserve fine-grained spatial information. To fuse the heterogeneous features, we specially design a Dual AtteNtion Enhancement (DANE) module to selectively fuse low-level and high-level features. Experiments demonstrate that DFvT achieves state-of-the-art accuracy with much higher efficiency across a spectrum of different model sizes. Ablation study validates the effectiveness of our designed components."
Improving Vision Transformers by Revisiting High-Frequency Components
Authors: Jiawang Bai; Li Yuan; Shu-Tao Xia; Shuicheng Yan; Zhifeng Li; Wei Liu
Venue: ECCV 2022 (main)

Abstract: "The transformer models have shown promising effectiveness in dealing with various vision tasks. However, compared with training Convolutional Neural Network (CNN) models, training Vision Transformer (ViT) models is more difficult and relies on the large-scale training set. To explain this observation we make a hypothesis that \textit{ViT models are less effective in capturing the high-frequency components of images than CNN models}, and verify it by a frequency analysis. Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e.g., RandAugment) can be attributed to the better usage of the high-frequency components. Then, to compensate for this insufficient ability of ViT models, we propose HAT, which directly augments high-frequency components of images via adversarial training. We show that HAT can consistently boost the performance of various ViT models (e.g., +1.2% for ViT-B, +0.5% for Swin-B), and especially enhance the advanced model VOLO-D5 to 87.3% that only uses ImageNet-1K data, and the superiority can also be maintained on out-of-distribution data and transferred to downstream tasks. The code is available at: https://github.com/jiawangbai/HAT."
Recurrent Bilinear Optimization for Binary Neural Networks
Authors: Sheng Xu; Yanjing Li; Tiancheng Wang; Teli Ma; Baochang Zhang; Peng Gao; Yu Qiao; Jinhu Lü; Guodong Guo
Venue: ECCV 2022 (main)

Abstract: "Binary Neural Networks (BNNs) show great promise for real-world embedded devices. As one of the critical steps to achieve a powerful BNN, the scale factor calculation plays an essential role in reducing the performance gap to their real-valued counterparts. However, existing BNNs neglect the intrinsic bilinear relationship of real-valued weights and scale factors, resulting in a sub-optimal model caused by an insufficient training process. To address this issue, Recurrent Bilinear Optimization is proposed to improve the learning process of BNNs (RBONNs) by associating the intrinsic bilinear variables in the back propagation process. Our work is the first attempt to optimize BNNs from the bilinear perspective. Specifically, we employ a recurrent optimization and Density-ReLU to sequentially backtrack the sparse real-valued weight filters, which will be sufficiently trained and reach their performance limits based on a controllable learning process. We obtain robust RBONNs, which show impressive performance over state-of-the-art BNNs on various models and datasets. Particularly, on the task of object detection, RBONNs have great generalization performance. Our code is open-sourced on https://github.com/SteveTsui/RBONN."
Neural Architecture Search for Spiking Neural Networks
Authors: Youngeun Kim; Yuhang Li; Hyoungseob Park; Yeshwanth Venkatesha; Priyadarshini Panda
Venue: ECCV 2022 (main)

Abstract: "Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. However, most prior SNN methods use ANN-like architectures (e.g., VGG-Net or ResNet), which could provide sub-optimal performance for temporal sequence processing of binary information in SNNs. To address this, in this paper, we introduce a novel Neural Architecture Search (NAS) approach for finding better SNN architectures. Inspired by recent NAS approaches that find the optimal architecture from activation patterns at initialization, we select the architecture that can represent diverse spike activation patterns across different data samples without training. Moreover, to further leverage the temporal information among the spikes, we search for feed forward connections as well as backward connections (i.e., temporal feedback connections) between layers. Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information. We conduct extensive experiments on three image recognition benchmarks where we show that SNASNet achieves state-of-the-art performance with significantly lower timesteps (5 timesteps). Code is available at Github."
Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification
Authors: Yang Liu; Lei Zhou; Pengcheng Zhang; Xiao Bai; Lin Gu; Xiaohan Yu; Jun Zhou; Edwin R. Hancock
Venue: ECCV 2022 (main)

Abstract: "Object categories are often grouped into a multi-granularity taxonomic hierarchy. Classifying objects at coarser-grained hierarchy requires global and common characteristics, while finer-grained hierarchy classification relies on local and discriminative features. Therefore, humans should also subconsciously focus on different object regions when classifying different hierarchies. This granularity-wise attention is confirmed by our collected human real-time gaze data on different hierarchy classifications. To leverage this mechanism, we propose a Cross-Hierarchical Region Feature (CHRF) learning framework. Specifically, we first design a region feature mining module that imitates humans to learn different granularity-wise attention regions with multi-grained classification tasks. To explore how human attention shifts from one hierarchy to another, we further present a cross-hierarchical orthogonal fusion module to enhance the region feature representation by blending the original feature and an orthogonal component extracted from adjacent hierarchies. Experiments on five hierarchical fine-grained datasets demonstrate the effectiveness of CHRF compared with the state-of-the-art methods. Ablation study and visualization results also consistently verify the advantages of our human attention-oriented modules. The code and dataset are available at https://github.com/visiondom/CHRF."
DaViT: Dual Attention Vision Transformers
Authors: Mingyu Ding; Bin Xiao; Noel Codella; Ping Luo; Jingdong Wang; Lu Yuan
Venue: ECCV 2022 (main)

Abstract: In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective vision transformer architecture that is able to capture global context while maintaining computational efficiency. We propose approaching the problem from an orthogonal angle: exploiting self-attention mechanisms with both "spatial tokens" and "channel tokens". With spatial tokens, the spatial dimension defines the token scope, and the channel dimension defines the token feature dimension. With channel tokens, we have the inverse: the channel dimension defines the token scope, and the spatial dimension defines the token feature dimension. We further group tokens along the sequence direction for both spatial and channel tokens to maintain the linear complexity of the entire model. We show that these two self-attentions complement each other: (i) since each channel token contains an abstract representation of the entire image, the channel attention naturally captures global interactions and representations by taking all spatial positions into account when computing attention scores between channels; (ii) the spatial attention refines the local representations by performing fine-grained interactions across spatial locations, which in turn helps the global information modeling in channel attention. Extensive experiments show our DaViT achieves state-of-the-art performance on four different tasks with efficient computations. Without extra data, DaViT-Tiny, DaViT-Small, and DaViT-Base achieve 82.8%, 84.2%, and 84.6% top-1 accuracy on ImageNet-1K with 28.3M, 49.7M, and 87.9M parameters, respectively. When we further scale up DaViT with 1.5B weakly supervised image and text pairs, DaViT-Gaint reaches 90.4% top-1 accuracy on ImageNet-1K.
Optimal Transport for Label-Efficient Visible-Infrared Person Re-identification
Authors: Jiangming Wang; Zhizhong Zhang; Mingang Chen; Yi Zhang; Cong Wang; Bin Sheng; Yanyun Qu; Yuan Xie
Venue: ECCV 2022 (main)

Abstract: "Visible-infrared person re-identification (VI-ReID) has been a key enabler for night intelligent monitoring system. However, the extensive laboring efforts significantly limit its applications. In this paper, we raise a new label-efficient training pipeline for VI-ReID. Our observation is: RGB ReID datasets have rich annotation information and annotating infrared images is expensive due to the lack of color information. In our approach, it includes two key steps: 1) We utilize the standard unsupervised domain adaptation technique to generate the pseudo labels for visible subset with the help of well-annotated RGB datasets; 2) We propose an optimal-transport strategy trying to assign pseudo labels from visible to infrared modality. In our framework, each infrared sample owns a label assignment choice, and each pseudo label requires unallocated images. By introducing uniform sample-wise and label-wise prior, we achieve a desirable assignment plan that allows us to find matched visible and infrared samples, and thereby facilitates cross-modality learning. Besides, a prediction alignment loss is designed to eliminate the negative effects brought by the incorrect pseudo labels. Extensive experimental results on benchmarks demonstrate the effectiveness of our approach. Code will be released at https://github.com/wjm-wjm/OTLA-ReID."