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Blind Video Deflickering by Neural Filtering with a Flawed Atlas

Blind Video Deflickering by Neural Filtering with a Flawed A...

引用

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

作者： Lei, Chenyang Ren, Xuanchi Zhang, Zhaoxiang Chen, Qifeng HKISI CAS CAIR Hong Kong Peoples R China Princeton Univ Princeton NJ 08544 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada CASIA Beijing Peoples R China HKUST Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Many videos contain flickering artifacts;common causes of flicker include video processing algorithms, video generation algorithms, and capturing videos under specific situations. Prior work usually requires specific guidance such as the flickering frequency, manual annotations, or extra consistent videos to remove the flicker. In this work, we propose a general flicker removal framework that only receives a single flickering video as input without additional guidance. Since it is blind to a specific flickering type or guidance, we name this "blind deflickering." The core of our approach is utilizing the neural atlas in cooperation with a neural filtering strategy. The neural atlas is a unified representation for all frames in a video that provides temporal consistency guidance but is flawed in many cases. To this end, a neural network is trained to mimic a filter to learn the consistent features (e.g., color, brightness) and avoid introducing the artifacts in the atlas. To validate our method, we construct a dataset that contains diverse real-world flickering videos. Extensive experiments show that our method achieves satisfying deflickering performance and even outperforms baselines that use extra guidance on a public benchmark. The source code is publicly available at https://***/deflicker.

关键词： Low-level vision

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

引用

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

作者： Zhang, Wenjin Li, Keyi Yang, Sen Yuan, Sifan Marsic, Ivan Sippel, Genevieve J. Kim, Mary S. Burd, Randall S. Rutgers State Univ New Brunswick NJ 08901 USA Waymo Mountain View CA USA Childrens Natl Hosp Washington DC USA

ISBN: (纸本)9798350365474

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： medical activity recognition self-supervised learning video understanding

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Train-Once-for-All Personalization

Train-Once-for-All Personalization

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

作者： Chen, Hong-You Li, Yandong Cui, Yin Zhang, Mingda Chao, Wei-Lun Zhang, Li Ohio State Univ Columbus OH 43210 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e.g., for accurately classifying different subsets of objects. One baseline approach is to train a "generic" model for classifying a wide range of objects, followed by class selection. In our experiments, we however found it suboptimal, perhaps because the model's weights are kept frozen without being personalized. To address this drawback, we propose Train-once-for-All PERsonalization (TAPER), a framework that is trained just once and can later customize a model for different end-users given their task descriptions. TAPER learns a set of "basis" models and a mixer predictor, such that given the task description, the weights (not the predictions!) of the basis models can be on the fly combined into a single "personalized" model. Via extensive experiments on multiple recognition tasks, we show that TAPER consistently outperforms the baseline methods in achieving a higher personalized accuracy. Moreover, we show that TAPER can synthesize a much smaller model to achieve comparable performance to a huge generic model, making it "deployment-friendly" to resource-limited end devices. Interestingly, even without end-users' task descriptions, TAPER can still be specialized to the deployed context based on its past predictions, making it even more "personalization-friendly".

关键词： Efficient and scalable vision

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Beyond Appearances: Material Segmentation with Embedded Spectral Information from RGB-D imagery

Beyond Appearances: Material Segmentation with Embedded Spec...

引用

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

作者： Perez, Fabian Rueda-Chacon, Hoover Univ Ind Santander Bucaramanga Colombia

ISBN: (纸本)9798350365474

In the realm of computer vision, material segmentation of natural scenes represents a challenge, driven by the complex and diverse appearances of materials. Traditional approaches often rely on RGB images, which can be deceptive given the variability in appearances due to different lighting conditions. Other methods, that employ polarization or spectral imagery, offer a more reliable material differentiation but their cost and accessibility restrict their everyday usage. In this work, we propose a deep learning framework that bridges the gap between high-fidelity material segmentation and the practical constraints of data acquisition. Our approach leverages a training strategy that employs a paired RGBD-spectral data to incorporate spectral information directly within the neural network. This encoding process is facilitated by a Spectral Feature Mapper (SFM) layer, a novel module that embeds unique spectral characteristics into the network, thus enabling the network to infer materials from standard RGB-D images. Once trained, the model allows to conduct material segmentation on widely available devices without the need for direct spectral data input. In addition, we generate the 3D point cloud from the RGB-D image pair, to provide a richer spatial context for scene understanding. Through simulations using available datasets, and real experiments conducted with an iPad Pro, our method demonstrates superior performance in material segmentation compared to other methods. Code is available at: https://***/Factral/ Spectral-material-segmentation

关键词： Convolutional Neural Networks Hyperspectral Images LiDAR Material recognition Material Segmentation Point Clouds RGB-D

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MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-...

引用

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

作者： Jiang, Jingjing Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9798350301298

Recently, finetuning pretrained vision-language models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in VQA. However, as VLMs scale, it becomes computationally expensive, storage inefficient, and prone to overfitting when tuning full model parameters for a specific task in low-resource settings. Although current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, we reparameterize expert weights in a low-rank subspace and share part of the weights inside and across MixPHM. Moreover, based on our quantitative analysis of representation redundancy, we propose Redundancy Regularization, which facilitates MixPHM to reduce task-irrelevant redundancy while promoting task-relevant correlation. Experiments conducted on VQA v2, GQA, and OK-VQA with different low-resource settings show that our MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning.

关键词： and reasoning language vision

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How to Benchmark vision Foundation Models for Semantic Segmentation?

How to Benchmark Vision Foundation Models for Semantic Segme...

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

作者： Kerssies, Tommie de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350365474

Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are finetuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to finetune the ViT-B variants of VFMs with a 16 x 16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page(1).

关键词： benchmark semantic segmentation vision foundation models

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NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for vision Transformers

NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Qua...

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

作者： Liu, Yijiang Yang, Huanrui Dong, Zhen Keutzer, Kurt Du, Li Zhang, Shanghang Nanjing Univ Nanjing Peoples R China Univ Calif Berkeley Berkeley CA USA Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China

ISBN: (纸本)9798350301298

The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead of tuning the quantizer to better fit the complicated activation distribution, this paper proposes NoisyQuant, a quantizer-agnostic enhancement for the post-training activation quantization performance of vision transformers. We make a surprising theoretical discovery that for a given quantizer, adding a fixed Uniform noisy bias to the values being quantized can significantly reduce the quantization error under provable conditions. Building on the theoretical insight, NoisyQuant achieves the first success on actively altering the heavy-tailed activation distribution with additive noisy bias to fit a given quantizer. Extensive experiments show NoisyQuant largely improves the post-training quantization performance of vision transformer with minimal computation overhead. For instance, on linear uniform 6-bit activation quantization, NoisyQuant improves SOTA top-1 accuracy on ImageNet by up to 1.7%, 1.1% and 0.5% for ViT, DeiT, and Swin Transformer respectively, achieving on-par or even higher performance than previous nonlinear, mixed-precision quantization.

关键词： Efficient and scalable vision

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Visual recognition-Driven Image Restoration for Multiple Degradation with Intrinsic Semantics Recovery

Visual Recognition-Driven Image Restoration for Multiple Deg...

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

作者： Yang, Zizheng Huang, Jie Chang, Jiahao Zhou, Man Yu, Hu Zhang, Jinghao Zhao, Feng Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

关键词： Low-level vision

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CompletionFormer: Depth Completion with Convolutions and vision Transformers

CompletionFormer: Depth Completion with Convolutions and Vis...

引用

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

作者： Zhang, Youmin Guo, Xianda Poggi, Matteo Zhu, Zheng Huang, Guan Mattoccia, Stefano Univ Bologna Bologna Italy PhiGent Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model makes it hard for the network to model the long-range relationship between pixels. While recent fully Transformer-based architecture has reported encouraging results with the global receptive field, the performance and efficiency gaps to the well-developed CNN models still exist because of its deteriorative local feature details. This paper proposes a Joint Convolutional Attention and Transformer block (JCAT), which deeply couples the convolutional attention layer and vision Transformer into one block, as the basic unit to construct our depth completion model in a pyramidal structure. This hybrid architecture naturally benefits both the local connectivity of convolutions and the global context of the Transformer in one single model. As a result, our Completion-Former outperforms state-of-the-art CNNs-based methods on the outdoor KITTI Depth Completion benchmark and indoor NYUv2 dataset, achieving significantly higher efficiency (nearly 1/3 FLOPs) compared to pure Transformer-based methods. Code is available at https://***/youmi-zym/CompletionFormer.

关键词： 3D from multi-view and sensors

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POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any...

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

作者： Fan, Zhiwen Pan, Panwang Wang, Peihao Jiang, Yifan Xu, Dejia Wang, Zhangyang ByteDance Beijing Peoples R China Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350365474

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied agents and downstream 3D vision tasks. These limitations mainly come from the necessity of 3D models, closed-category detection, and a large number of densely annotated support views. To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE). The proposed approach POPE enables zero-shot 6DoF object pose estimation for any target object in any scene, while only a single reference is adopted as the support view. To achieve this, POPE lever-ages the power of the pre-trained large-scale 2D foundation model, employs a framework with hierarchical feature representation and 3D geometry principles. Moreover, it estimates the relative camera pose between object prompts and the target object in new views, enabling both two-view and multi-view 6DoF pose estimation tasks. Comprehensive experimental results demonstrate that POPE exhibits unrivaled robust performance in zero-shot settings, by achieving a significant reduction in the averaged Median Pose Error by 52.38% and 50.47% on the LINEMOD [22] and OnePose [54] datasets, respectively. We also conduct more challenging testings in causally captured images (see Figure 1), which further demonstrates the robustness of POPE.

关键词： Object recognition

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