Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this paper, we propose a new continual learning approach, Advanced Null Space (AdNS), to balance the stability and plasticity without storing any old data of previous tasks. Specifically, to obtain better stability, AdNS makes use of low-rank approximation to obtain a novel null space and projects the gradient onto the null space to prevent the interference on the past tasks. To control the generation of the null space, we introduce a non-uniform constraint strength to further reduce forgetting. Furthermore, we present a simple but effective method, intra-task distillation, to improve the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.
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Novelty detection is a key capability for practical machine learning in the real world, where models operate in non-stationary conditions and are repeatedly exposed to new, unseen data. Yet, most current novelty detection approaches have been developed exclusively for static, offline use. They scale poorly under more realistic, continual learning regimes in which data distribution shifts occur. To address this critical gap, this paper proposes incDFM (incremental Deep Feature Modeling), a self-supervised continual novelty detector. The method builds a statistical model over the space of intermediate features produced by a deep network, and utilizes feature reconstruction errors as uncertainty scores to guide the detection of novel samples. Most importantly, incDFM estimates the statistical model incrementally (via several iterations within a task), instead of a single-shot. Each time it selects only the most confident novel samples which will then guide subsequent recruitment incrementally. For a certain task where the ML model encounters a mixture of old and novel data, the detector flags novel samples to incorporate them to old knowledge. Then the detector is updated with the flagged novel samples, in preparation for a next task. To quantify and benchmark performance, we adapted multiple datasets for continual learning: CIFAR-10, CIFAR-100, SVHN, iNaturalist, and the 8-dataset. Our experiments show that incDFM achieves state of the art continual novelty detection performance. Furthermore, when examined in the greater context of continual learning for classification, our method is successful in minimizing catastrophic forgetting and error propagation.
Patch attack, which introduces a perceptible but localized change to the input image, has gained significant momentum in recent years. In this paper, we propose a unified framework to analyze certified patch defense tasks (including both certified detection and certified recovery) using the recently emerged vision transformer. In addition to the existing patch defense setting where only one patch is considered, we provide the very first study on developing certified detection against the \emph{dual patch attack}, in which the attacker is allowed to adversarially manipulate pixels in two different regions. Benefiting from the recent progress in self-supervised vision transformers (\ie, masked autoencoder), our method achieves state-of-the-art performance in both certified detection and certified recovery of adversarial patches. For certified detection, we improve the performance by up to $\app16\%$ on ImageNet without additional training for a single adversarial patch, and for the first time, can also tackle the more challenging dual patch setting. Our method largely \emph{closes the gap} between detection-based certified robustness and clean image accuracy. For certified recovery, our approach improves certified accuracy by $\app2\%$ on ImageNet across all attack sizes, attaining the new state-of-the-art performance.
Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed by two seemingly opposed approaches: improving the realism of simulation or foregoing realism entirely via domain randomization. In this paper, we show that the recent progress in neural rendering enables a new unified approach we call Photo-realistic Neural Domain Randomization (PNDR). We propose to learn a composition of neural networks that acts as a physics-based ray tracer generating high-quality renderings from scene geometry alone. Our approach is modular, composed of different neural networks for materials, lighting, and rendering, thus enabling randomization of different key image generation components in a differentiable pipeline. Once trained, our method can be combined with other methods and used to generate photo-realistic image augmentations online and significantly more efficiently than via traditional ray-tracing. We demonstrate the usefulness of PNDR through two downstream tasks: 6D object detection and monocular depth estimation. Our experiments show that training with PNDR enables generalization to novel scenes and significantly outperforms the state of the art in terms of real-world transfer.
Scene Text Recognition (STR) has many important applications in computer vision. Complex backgrounds continue to be a big challenge for STR because they interfere with text feature extraction. Many existing methods use attentional regions, bounding boxes or polygons to reduce such interference. However, the text regions located by these methods still contain much undesirable background interference. In this paper, we propose a Background-Insensitive approach BINet by explicitly leveraging the text Semantic Segmentation (SSN) to extract texts more accurately. SSN is trained on a set of existing segmentation data, whose volume is only 0.03% of STR training data. This prevents the large-scale pixel-level annotations of the STR training data. To effectively utilize the segmentation cues, we design new segmentation refinement and embedding blocks for refining text-masks and reinforcing visual features. Additionally, we propose an efficient pipeline that utilizes Synthetic Initialization (SI) for STR models trained only on real data (1.7% of STR training data), instead of on both synthetic and real data from scratch. Experiments show that the proposed method can recognize text from complex backgrounds more effectively, achieving state-of-the-art performance on several public datasets.
The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames. However, its sequential frame-wise matching property fundamentally suffers from the intermediate interruptions in a video, such as object occlusions, fast camera movements, and abrupt light changes. Moreover, it typically overlooks temporal information beyond the two frames for matching. In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames, and the short clip tracking avoids the conventional error-prone long-term track memory management. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching. Given the state-of-the-art tracking-by-detection tracker, QDTrack, we showcase how the tracking performance improves with our new tracking formulation. In addition, we designed a novel Transformer-based clip tracker tailored for the effective inter-clip association. We evaluate our proposals on two tracking benchmarks, TAO and MOT17 that have complementary characteristics and challenges each other. Our new tracking formulation greatly improves association quality, leading to a new state-of-the-art in TAO.
VL-LTR: Learning Class-Wise Visual-Linguistic Representation for Long-Tailed Visual Recognition
PDF ↗Recently, computer vision foundation models such as CLIP and ALI-GN, have shown impressive generalization capabilities on various downstream tasks. But their abilities to deal with the long-tailed data still remain to be proved. In this work, we present a novel framework based on pre-trained visual-linguistic models for long-tailed recognition (LTR), termed VL-LTR, and conduct empirical studies on the benefits of introducing text modality for long-tailed recognition tasks. Compared to existing approaches, the proposed VL-LTR has the following merits. (1) Our method can not only learn visual representation from images but also learn corresponding linguistic representation from noisy class-level text descriptions collected from the Internet; (2) Our method can effectively use the learned visual-linguistic representation to improve the visual recognition performance, especially for classes with fewer image samples. We also conduct extensive experiments and set the new state-of-the-art performance on widely-used LTR benchmarks. Notably, our method achieves 77.2\% overall accuracy on ImageNet-LT, which significantly outperforms the previous best method by over 17 points, and is close to the prevailing performance training on the full ImageNet. Code shall be released.
A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent architectures that incorporate priors either about the input data or of specific tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT. In this paper, we revisit the supervised training of ViTs. Our procedure builds upon and simplifies a recipe introduced for training ResNet-50. It includes a new simple data-augmentation procedure with only 3 augmentations, closer to the practice in self-supervised learning. Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully supervised training recipes for ViT. It also reveals that the performance of our ViT trained with supervision is comparable to that of more recent architectures. Our results could serve as better baselines for recent self-supervised approaches demonstrated on ViT.
Despite their success, deep networks have been shown to be highly susceptible to perturbations, often causing significant drops in accuracy. In this paper, we investigate model robustness on perturbed inputs by studying the performance of internal sub-networks (subnets). Interestingly, we observe that most subnets show particularly poor robustness against perturbations. More importantly, these weak subnets are correlated with the overall lack of robustness. Tackling this phenomenon, we propose a new training procedure that identifies and enhances weak subnets (EWS) to improve robustness. Specifically, we develop a search algorithm to find particularly weak subnets and explicitly strengthen them via knowledge distillation from the full network. We show that EWS greatly improves both robustness against corrupted images as well as accuracy on clean data. Being complementary to popular data augmentation methods, EWS consistently improves robustness when combined with these approaches. To highlight the flexibility of our approach, we combine EWS also with popular adversarial training methods resulting in improved adversarial robustness.
Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has, if truth, a significant influence on the fields of FSCIL. In this paper, we show through empirical results that adopting the data replay is surprisingly favorable. However, storing and replaying old data can lead to a privacy concern. To address this issue, we alternatively propose using data-free replay that can synthesize data by a generator without accessing real data. In observing the the effectiveness of uncertain data for knowledge distillation, we impose entropy regularization in the generator training to encourage more uncertain examples. Moreover, we propose to relabel the generated data with one-hot-like labels. This modification allows the network to learn by solely minimizing the cross-entropy loss, which mitigates the problem of balancing different objectives in the conventional knowledge distillation approach. Finally, we show extensive experimental results and analysis on CIFAR-100, miniImageNet and CUB-200 to demonstrate the effectiveness of our proposed one.
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.
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.
Ensemble Learning Priors Driven Deep Unfolding for Scalable Video Snapshot Compressive Imaging
PDF ↗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.
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.
Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks. However, their ability to generate images with specifications corresponding to the classes and databases learned during Continual Learning (CL) is not well understood and catastrophic forgetting remains a significant challenge. In this paper, we firstly analyze the forgetting behaviour of VAEs by developing a new theoretical framework that formulates CL as a dynamic optimal transport problem. This framework proves approximate bounds to the data likelihood without requiring the task information and explains how the prior knowledge is lost during the training process. We then propose a novel memory buffering approach, namely the Online Cooperative Memorization (OCM) framework, which consists of a Short-Term Memory (STM) that continually stores recent samples to provide future information for the model, and a Long-Term Memory (LTM) aiming to preserve a wide diversity of samples. The proposed OCM transfers certain samples from STM to LTM according to the information diversity selection criterion without requiring any supervised signals. The OCM framework is then combined with a dynamic VAE expansion mixture network for further enhancing its performance.
Traditional approaches for training a computer vision models requires collecting and labelling vast amounts of imagery under a diverse set of scene configurations and properties. This process is incredibly time-consuming, and it is challenging to ensure that the captured data distribution maps well to the target domain of an application scenario. In recent years, synthetic data has emerged as a way to address both of these issues. However, current approaches either require human experts to manually tune each scene property or use automatic methods that provide little to no control; this requires rendering large amounts random data variations, which is slow and is often suboptimal for the target domain. We present the first fully differentiable synthetic data generation pipeline that uses Neural Radiance Fields (NeRFs) in a closed-loop with a target application’s loss function to generate data, on demand, with no human labor, to maximise accuracy for a target task. We illustrate the effectiveness of our method with synthetic and real-world object detection experiments. In addition, we evaluate on a new ""YCB-in-the-Wild"" dataset that provides a test scenario for object detection with varied pose in real-world environments.
Class-Incremental Learning (CIL) struggles with catastrophic forgetting when learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging without access to the training data of previously learned classes. Though recent DFCIL works introduce techniques such as model inversion to synthesize data for previous classes, they fail to overcome forgetting due to the severe domain gap between the synthetic and real data. To address this issue, this paper proposes relation-guided representation learning (RRL) for DFCIL, dubbed R-DFCIL. In RRL, we introduce relational knowledge distillation to flexibly transfer the structural relation of new data from the old model to the current model. Our RRL-boosted DFCIL can guide the current model to learn representations of new classes better compatible with representations of previous classes, which greatly reduces forgetting while improving plasticity. To avoid the mutual interference between representation and classifier learning, we employ local rather than global classification loss during RRL. After RRL, the classification head is refined with global class-balanced classification loss to address the data imbalance issue as well as learn the decision boundaries between new and previous classes. Extensive experiments on CIFAR100, Tiny-ImageNet200, and ImageNet100 demonstrate that our R-DFCIL significantly surpasses previous approaches and achieves a new state-of-the-art performance for DFCIL.
Learning to optimize (L2O) has gained increasing attention since it demonstrates a promising path to automating and accelerating the optimization of complicated problems. Unlike manually crafted classical optimizers, L2O parameterizes and learns optimization rules in a data-driven fashion. However, the primary barrier, scalability, persists for this paradigm: as the typical L2O models create massive memory overhead due to unrolled computational graphs, it disables L2O’s applicability to large-scale tasks. To overcome this core challenge, we propose a new scalable learning to optimize (SL2O) framework which (i) first constrains the network updates in a tiny subspace and (ii) then explores learning rules on top of it. Thanks to substantially reduced trainable parameters, learning optimizers for large-scale networks with a single GPU become feasible for the first time, showing that the scalability roadblock of applying L2O to training large models is now removed. Comprehensive experiments on various network architectures (i.e., ResNets, VGGs, ViTs) and datasets (i.e., CIFAR, ImageNet, E2E) across vision and language tasks, consistently validate that SL2O can achieve significantly faster convergence speed and competitive performance compared to analytical optimizers. For example, our approach converges 3.41 4.60 times faster on CIFAR-10/100 with ResNet-18, and 1.24 times faster on ViTs, at nearly no performance loss. Codes are included in the supplement.
Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty indoor space and a list of decorations with layout determined by user, we aim to synthesize a new image of the same space with desired furnishing and decorations. Neural scene decoration can be applied to create conceptual interior designs in a simple yet effective manner. Our attempt to this research problem is a novel scene generation architecture that transforms an empty scene and an object layout into a realistic furnished scene photograph. We demonstrate the performance of our proposed method by comparing it with conditional image synthesis baselines built upon prevailing image translation approaches both qualitatively and quantitatively. We conduct extensive experiments to further validate the plausibility and aesthetics of our generated scenes.
Object reconstruction from 3D point clouds has achieved impressive progress in the computer vision and computer graphics research field. However, reconstruction from time-varying point clouds (a.k.a. 4D point clouds) is generally overlooked. In this paper, we propose a new network architecture, namely RFNet-4D, that jointly reconstruct objects and their motion flows from 4D point clouds. The key insight is that simultaneously performing both tasks via learning spatial and temporal features from a sequence of point clouds can leverage individual tasks, leading to improved overall performance. To prove this ability, we design a temporal vector field learning module using unsupervised learning approach for flow estimation, leveraged by supervised learning of spatial structures for object reconstruction. Extensive experiments and analyses on benchmark dataset validated the effectiveness and efficiency of our method. As shown in experimental results, our method achieves state-of-the-art performance on both flow estimation and object reconstruction while performing much faster than existing methods in both training and inference. Our code and data are available at https://github.com/hkust-vgd/RFNet-4D.