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Towards Exemplar-Free Continual Learning in vision Transformers: an Account of Attention, Functional and Weight Regularization

Towards Exemplar-Free Continual Learning in Vision Transform...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pelosin, Francesco Jha, Saurav Torsello, Andrea Raducanu, Bogdan van de Weijer, Joost Ca Foscari Univ Venice Italy Univ New South Wales Sydney NSW Australia Comp Vis Ctr Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we investigate the continual learning of vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) - while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learners.(1)

关键词： Learning systems Weight measurement Image recognition Surgery Benchmark testing Transformers Stability analysis

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Region-Aware Pretraining for Open-Vocabulary Object Detection with vision Transformers

Region-Aware Pretraining for Open-Vocabulary Object Detectio...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim, Dahun Angelova, Anelia Kuo, Weicheng Google Res Brain Team Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We present Region-aware Open-vocabulary vision Transformers (RO-ViT) - a contrastive image-text pretraining recipe to bridge the gap between image-level pretraining and open-vocabulary object detection. At the pretraining phase, we propose to randomly crop and resize regions of positional embeddings instead of using the whole image positional embeddings. This better matches the use of positional embeddings at region-level in the detection finetuning phase. In addition, we replace the common softmax cross entropy loss in contrastive learning with focal loss to better learn the informative yet difficult examples. Finally, we leverage recent advances in novel object proposals to improve open-vocabulary detection finetuning. We evaluate our full model on the LVIS and COCO open-vocabulary detection benchmarks and zero-shot transfer. RO-ViT achieves a state-of-the-art 32.1 AP(r) on LVIS, surpassing the best existing approach by +5.8 points in addition to competitive zero-shot transfer detection. Surprisingly, RO-ViT improves the image-level representation as well and achieves the state of the art on 9 out of 12 metrics on COCO and Flickr image-text retrieval benchmarks, outperforming competitive approaches with larger models.

关键词： language reasoning vision

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Hephaestus: A large scale multitask dataset towards InSAR understanding

Hephaestus: A large scale multitask dataset towards InSAR un...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bountos, Nikolaos Ioannis Papoutsis, Ioannis Michail, Dimitrios Karavias, Andreas Elias, Panagiotis Parcharidis, Isaak Natl Observ Athens Inst Astron Astrophys Space Applicat & Remote Sen Athens Greece Harokopio Univ Athens Dept Informat & Telemat Athens Greece Harokopio Univ Athens Dept Geog Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, and on the geotechnical properties of man-made structures. However, there are only a limited number of applications that exploit the abundance of InSAR data and deep learning methods to extract such knowledge. The main barrier has been the lack of a large curated and annotated InSAR dataset, which would be costly to create and would require an interdisciplinary team of experts experienced on InSAR data interpretation. In this work, we put the effort to create and make available the first of its kind, manually annotated dataset that consists of 19,919 individual Sentinel-1 interferograms acquired over 44 different volcanoes globally, which are split into 216,106 InSAR patches. The annotated dataset is designed to address different computer vision problems, including volcano state classification, semantic segmentation of ground deformation, detection and classification of atmospheric signals in InSAR imagery, interferogram captioning, text to InSAR generation, and InSAR image quality assessment.

关键词： Deep learning Image quality computer vision Image segmentation Semantics Volcanoes Task analysis

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Practical Network Acceleration with Tiny Sets

Practical Network Acceleration with Tiny Sets

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Guo-Hua Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

Due to data privacy issues, accelerating networks with tiny training sets has become a critical need in practice. Previous methods mainly adopt filter-level pruning to accelerate networks with scarce training samples. In this paper, we reveal that dropping blocks is a fundamentally superior approach in this scenario. It enjoys a higher acceleration ratio and results in a better latency-accuracy performance under the few-shot setting. To choose which blocks to drop, we propose a new concept namely recoverability to measure the difficulty of recovering the compressed network. Our recoverability is efficient and effective for choosing which blocks to drop. Finally, we propose an algorithm named PRACTISE to accelerate networks using only tiny sets of training images. PRACTISE outperforms previous methods by a significant margin. For 22% latency reduction, PRACTISE surpasses previous methods by on average 7% on ImageNet-1k. It also enjoys high generalization ability, working well under data-free or out-of-domain data settings, too. Our code is at https://***/DoctorKey/Practise.

关键词： Efficient and scalable vision

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LightPainter: Interactive Portrait Relighting with Freehand Scribble

LightPainter: Interactive Portrait Relighting with Freehand ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mei, Yiqun Zhang, He Zhang, Xuaner Zhang, Jianming Shu, Zhixin Wang, Yilin Wei, Zijun Yan, Shi Jung, HyunJoon Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9798350301298

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively manipulate portrait lighting effect with ease. This is achieved by two conditional neural networks, a delighting module that recovers geometry and albedo optionally conditioned on skin tone, and a scribble-based module for relighting. To train the relighting module, we propose a novel scribble simulation procedure to mimic real user scribbles, which allows our pipeline to be trained without any human annotations. We demonstrate high-quality and flexible portrait lighting editing capability with both quantitative and qualitative experiments. User study comparisons with commercial lighting editing tools also demonstrate consistent user preference for our method.

关键词： vision + graphics

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Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data 18

Improving 2D Human Pose Estimation in Rare Camera Views with...

引用

18th International conference on Automatic Face and Gesture recognition (FG)

作者： Purkrabek, Miroslav Matas, Jiri Czech Tech Univ Fac Elect Engn Dept Cybernet Visual Recognit Grp Prague Czech Republic

ISBN: (纸本)9798350394948;9798350394955

Methods and datasets for human pose estimation focus predominantly on side- and front-view scenarios. We overcome the limitation by leveraging synthetic data and introduce RePoGen (RarE POses GENerator), an SMPL-based method for generating synthetic humans with comprehensive control over pose and view. Experiments on top-view datasets and a new dataset of real images with diverse poses show that adding the RePoGen data to the COCO dataset outperforms previous approaches to top- and bottom-view pose estimation without harming performance on common views. An ablation study shows that anatomical plausibility, a property prior research focused on, is not a prerequisite for effective performance. The introduced dataset and the corresponding code are available on the project website(1).

关键词： computer vision

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JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields

JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radi...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Xi Courant, Robin Shi, Jinglei Marchand, Eric Christie, Marc Univ Rennes CNRS INRIA IRISA Rennes France Ecole Polytech IP Paris LIX Paris France Nankai Univ VCIP CS Tianjin Peoples R China

ISBN: (纸本)9798350301298

This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip. To this end, we rely on an implicit-neural-representation (INR) in a way to compute a clip that shares the same cinematic features as the reference clip. We propose a general formulation of a camera optimization problem in an INR that computes extrinsic and intrinsic camera parameters as well as timing. By leveraging the differentiability of neural representations, we can back-propagate our designed cinematic losses measured on proxy estimators through a NeRF network to the proposed cinematic parameters directly. We also introduce specific enhancements such as guidance maps to improve the overall quality and efficiency. Results display the capacity of our system to replicate well known camera sequences from movies, adapting the framing, camera parameters and timing of the generated video clip to maximize the similarity with the reference clip.

关键词： vision + graphics

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ABCD : Arbitrary Bitwise Coefficient for De-quantization

ABCD : Arbitrary Bitwise Coefficient for De-quantization

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Han, Woo Kyoung Lee, Byeonghun Park, Sang Hyun Jin, Kyong Hwan Daegu Gyeongbuk Inst Sci & Technol DGIST Daegu South Korea

ISBN: (纸本)9798350301298

Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://***/WooKyoungHan/ABCD

关键词： Low-level vision

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FFCV: Accelerating Training by Removing Data Bottlenecks

FFCV: Accelerating Training by Removing Data Bottlenecks

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Leclerc, Guillaume Ilyas, Andrew Engstrom, Logan Park, Sung Min Salman, Hadi Madry, Aleksander MIT Cambridge MA 02139 USA

ISBN: (纸本)9798350301298

We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and just-in-time compilation to (a) make data loading and transfer significantly more efficient, ensuring that GPUs can reach full utilization;and (b) offload as much data processing as possible to the CPU asynchronously, freeing GPU cycles for training. Using FFCV, we train ResNet-18 and ResNet-50 on the ImageNet dataset with a state-of-the-art tradeoff between accuracy and training time. For example, across the range of ResNet-50 models we test, we obtain the same accuracy as the best baselines in half the time. We demonstrate FFCV's performance, ease-of-use, extensibility, and ability to adapt to resource constraints through several case studies. Detailed installation instructions, documentation, and Slack support channel are available at https://***/.

关键词： Efficient and scalable vision

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sRGB Real Noise Synthesizing with Neighboring Correlation-Aware Noise Model

sRGB Real Noise Synthesizing with Neighboring Correlation-Aw...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fu, Zixuan Guo, Lanqing Wen, Bihan Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350301298

Modeling and synthesizing real noise in the standard RGB (sRGB) domain is challenging due to the complicated noise distribution. While most of the deep noise generators proposed to synthesize sRGB real noise using an end-to-end trained model, the lack of explicit noise modeling degrades the quality of their synthesized noise. In this work, we propose to model the real noise as not only dependent on the underlying clean image pixel intensity, but also highly correlated to its neighboring noise realization within the local region. Correspondingly, we propose a novel noise synthesizing framework by explicitly learning its neighboring correlation on top of the signal dependency. With the proposed noise model, our framework greatly bridges the distribution gap between synthetic noise and real noise. We show that our generated "real" sRGB noisy images can be used for training supervised deep denoisers, thus to improve their real denoising results with a large margin, comparing to the popular classic denoisers or the deep denoisers that are trained on other sRGB noise generators. The code will be available at https://***/xuan611/sRGB-Real-Noise-Synthesizing.

关键词： Low-level vision

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