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Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual recognition Problems

Learning Common Rationale to Improve Self-Supervised Represe...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yangyang Shu Anton Van den Hengel Lingqiao Liu School of Computer Science The University of Adelaide

Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings. 1 1 The source code will be publicly available at:https://***/GANPerf/LCR

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Differentiable Diffusion for Dense Depth Estimation from Multi-view Images

Differentiable Diffusion for Dense Depth Estimation from Mul...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Khan, Numair Kim, Min H. Tompkin, James Brown Univ Providence RI 02912 USA Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

We present a method to estimate dense depth by optimizing a sparse set of points such that their diffusion into a depth map minimizes a multi-view repmjection error from RGB supervision. We optimize point positions, depths, and weights with respect to the loss by differential splatting that models points as Gaussians with analytic transmittance. Further;we develop an efficient optimization routine that can simultaneously optimize the 50k+ points required for complex scene reconstruction. We validate our routine using ground truth data and show high reconstruction quality. Then, we apply this to light field and wider baseline images via self supervision, and show improvements in both average and outlier error for depth maps diffused from inaccurate sparse points. Finally, we compare qualitative and quantitative results to image processing and deep learning methods.

关键词： Deep learning computer vision Analytical models Estimation Rendering (computer graphics) Propagation losses Light fields

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A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

A New Comprehensive Benchmark for Semi-supervised Video Anom...

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conference on computer vision and pattern recognition (cvpr)

作者： Congqi Cao Yue Lu Peng Wang Yanning Zhang ASGO School of Computer Science Northwestern Polytechnical University China

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://***.

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HDR Imaging with Spatially Varying Signal-to-Noise Ratios

HDR Imaging with Spatially Varying Signal-to-Noise Ratios

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yiheng Chi Xingguang Zhang Stanley H. Chan School of Electrical and Computer Engineering Purdue University

While today's high dynamic range (HDR) image fusion algorithms are capable of blending multiple exposures, the acquisition is often controlled so that the dynamic range within one exposure is narrow. For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying. Existing image denoising algorithms and HDR fusion algorithms both fail to handle this situation, leading to severe limitations in low-light HDR imaging. This paper presents two contributions. Firstly, we identify the source of the problem. We find that the issue is associated with the co-existence of (1) spatially varying signal-to-noise ratio, especially the excessive noise due to very dark regions, and (2) a wide luminance range within each exposure. We show that while the issue can be handled by a bank of denoisers, the complexity is high. Secondly, we propose a new method called the spatially varying high dynamic range (SV-HDR) fusion network to simultaneously denoise and fuse images. We introduce a new exposure-shared block within our custom-designed multi-scale transformer framework. In a variety of testing conditions, the performance of the proposed SV-HDR is better than the existing methods.

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Randomized Adversarial Training via Taylor Expansion

Randomized Adversarial Training via Taylor Expansion

引用

conference on computer vision and pattern recognition (cvpr)

作者： Gaojie Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang State Key Laboratory of Computer Science Institute of Software CAS Beijing China University of Liverpool Liverpool UK Lancaster University Lancaster UK

In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the most successful methods. To deal with both the robustness against adversarial examples and the accuracy over clean examples, many works develop enhanced adversarial training methods to achieve various trade-offs between them [[19], [38], [80]], Leveraging over the studies [8], [32] that smoothed update on weights during training may help find flat minima and improve generalization, we suggest reconciling the robustness-accuracy trade-off from another perspective, i.e., by adding random noise into deterministic weights. The randomized weights enable our design of a novel adversarial training method via Taylor expansion of a small Gaussian noise, and we show that the new adversarial training method can flatten loss landscape and find flat minima. With PGD, CW, and Auto Attacks, an extensive set of experiments demonstrate that our method enhances the state-of-the-art adversarial training methods, boosting both robustness and clean accuracy. The code is available at https://***/Alexkael/Randomized-Adversarial-Training.

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Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection

Pixels, Regions, and Objects: Multiple Enhancement for Salie...

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conference on computer vision and pattern recognition (cvpr)

作者： Yi Wang Ruili Wang Xin Fan Tianzhu Wang Xiangjian He DUT-RU International School of Information Science and Engineering Dalian University of Technology China School of Mathematical and Computational Sciences Massey University New Zealand School of Computer Science University of Nottingham Ningbo China Ningbo China

Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. To address this issue, we propose a novel approach called Multiple Enhancement Network (MENet) that adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms of HVS. A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features. A flexible multiscale feature enhancement module (ME-Module) is then designed to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence. An iterative training strategy is used to enhance boundary features and adaptive features in the dual-branch decoder of MENet. Comprehensive evaluations on six challenging benchmark datasets show that MENet achieves state-of-the-art results. Both the codes and results are publicly available at https://***/yiwangtz/MENet.

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Texture-Guided Saliency Distilling for Unsupervised Salient Object Detection

Texture-Guided Saliency Distilling for Unsupervised Salient ...

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conference on computer vision and pattern recognition (cvpr)

作者： Huajun Zhou Bo Qiao Lingxiao Yang Jianhuang Lai Xiaohua Xie School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China

Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies on the noisy saliency pseudo labels that have been generated from traditional handcraft methods or pre-trained networks. To cope with the noisy labels problem, a class of methods focus on only easy samples with reliable labels but ignore valuable knowledge in hard samples. In this paper, we propose a novel USOD method to mine rich and accurate saliency knowledge from both easy and hard samples. First, we propose a Confidence-aware Saliency Distilling (CSD) strategy that scores samples conditioned on samples' confidences, which guides the model to distill saliency knowledge from easy samples to hard samples progressively. Second, we propose a Boundary-aware Texture Matching (BTM) strategy to refine the boundaries of noisy labels by matching the textures around the predicted boundaries. Extensive experiments on RGB, RGB-D, RGB-T, and video SOD benchmarks prove that our method achieves state-of-the-art USOD performance. Code is available at ***/moothes/A2S-v2.

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GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material recognition

GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Mat...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Shi, Fengmin Guo, Jie Zhang, Haonan Yang, Shan Wang, Xiying Guo, Yanwen Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China IQIYI Intelligence Chongqing Heilongjiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck and the sound captured from either modal sound synthesis (for virtual objects) or real measurements (for real objects). Besides global geometries, our dataset also takes local geometries around different hitpoints into consideration. This local information is less explored in existing datasets. We demonstrate that local geometry has a greater impact on the sound than the global geometry and offers more cues in material recognition. To extract features from different modalities and perform proper fusion, we propose a new deep neural network GLAVNet that comprises multiple branches and a well-designed fusion module. Once trained on GLAudio, our GLAVNet provides state-of-the-art performance on material identification and supports fine-grained material categorization.

关键词： Geometry Deep learning Visualization computer vision Feature extraction pattern recognition Task analysis

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Evolved Part Masking for Self-Supervised Learning

Evolved Part Masking for Self-Supervised Learning

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conference on computer vision and pattern recognition (cvpr)

作者： Zhanzhou Feng Shiliang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Peng Cheng Laboratory

Existing Masked Image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those patterns resort to different criteria to mask local regions, sticking to a fixed pattern leads to limited vision cues modeling capability. This paper proposes an evolved part-based masking to pursue more general visual cues modeling in self-supervised learning. Our method is based on an adaptive part partition module, which leverages the vision model being trained to construct a part graph, and partitions parts with graph cut. The accuracy of partitioned parts is on par with the capability of the pretrained model, leading to evolved mask patterns at different training stages. It generates simple patterns at the initial training stage to learn low-level visual cues, which hence evolves to eliminate accurate object parts to reinforce the learning of object semantics and contexts. Our method does not require extra pretrained models or annotations, and effectively ensures the training efficiency by evolving the training difficulty. Experiment results show that it substantially boosts the performance on various tasks including image classification, object detection, and semantic segmentation. For example, it outperforms the recent MAE by 0.69% on imageNet-1K classification and 1.61% on ADE20K segmentation with the same training epochs.

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Text2Scene: Text-driven Indoor Scene Stylization with Part-Aware Details

Text2Scene: Text-driven Indoor Scene Stylization with Part-A...

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conference on computer vision and pattern recognition (cvpr)

作者： Inwoo Hwang Hyeonwoo Kim Young Min Kim Department of Electrical and Computer Engineering Seoul National University Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects.

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