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Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization

Learning to Exploit the Sequence-Specific Prior Knowledge fo...

引用

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

作者： Qin, Haina Han, Longfei Xiong, Weihua Wang, Juan Ma, Wentao Li, Bing Hu, Weiming Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Beijing Technol & Business Univ Beijing Peoples R China Zeku Technol Shanghai Peoples R China

ISBN: (纸本)9798350301298

The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks.

关键词： Low-level vision

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Mining and Unifying Heterogeneous Contrastive Relations for Weakly-Supervised Actor-Action Segmentation

Mining and Unifying Heterogeneous Contrastive Relations for ...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Duan, Bin Tang, Hao Sun, Changchang Zhu, Ye Yan, Yan IIT Chicago IL 60616 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350318920;9798350318937

We introduce a novel weakly-supervised video actor-action segmentation (VAAS) framework, where only video-level tags are available. Previous VAAS methods follow a synthesize-and-refine scheme, i.e., they first synthesize the pseudo-segmentation and recursively refine the segmentation. However, this process requires significant time costs and heavily relies on the quality of the initial segmentation. Unlike existing works, our method hierarchically mines contrastive relations to supplement each other for learning a visually-plausible segmentation model. Specifically, three contrastive relations are abstracted from the pixel-level and frame-level, i.e., low-level edge-aware, class-activation map aware, and semantic tag-aware relations. Then, the discovered contrastive relations are unified into a universal objective for training the segmentation model, regardless of their heterogeneity. Moreover, we incorporate motion cues and unlabeled samples to increase the discriminative power and robustness of the segmentation model. Extensive experiments indicate that our proposed method produces reasonable segmentation.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Discriminator-Cooperated Feature Map Distillation for GAN Compression

Discriminator-Cooperated Feature Map Distillation for GAN Co...

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

作者： Hu, Tie Lin, Mingbao You, Lizhou Chao, Fei Ji, Rongrong Xiamen Univ Sch Informat MAC Lab Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Xiamen Peoples R China Tencent Youtu Lab Shanghai Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China

ISBN: (纸本)9798350301298

Despite excellent performance in image generation, Generative Adversarial Networks (GANs) are notorious for its requirements of enormous storage and intensive computation. As an awesome "performance maker", knowledge distillation is demonstrated to be particularly efficacious in exploring low-priced GANs. In this paper, we investigate the irreplaceability of teacher discriminator and present an inventive discriminator-cooperated distillation, abbreviated as DCD, towards refining better feature maps from the generator. In contrast to conventional pixel-to-pixel match methods in feature map distillation, our DCD utilizes teacher discriminator as a transformation to drive intermediate results of the student generator to be perceptually close to corresponding outputs of the teacher generator. Furthermore, in order to mitigate mode collapse in GAN compression, we construct a collaborative adversarial training paradigm where the teacher discriminator is from scratch established to co-train with student generator in company with our DCD. Our DCD shows superior results compared with existing GAN compression methods. For instance, after reducing over 40x MACs and 80x parameters of CycleGAN, we well decrease FID metric from 61.53 to 48.24 while the current SoTA method merely has 51.92. This work's source code has been made accessible at https://***/poopit/DCD-official.

关键词： Efficient and scalable vision

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ConQueR: Query Contrast Voxel-DETR for 3D Object Detection

ConQueR: Query Contrast Voxel-DETR for 3D Object Detection

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

作者： Zhu, Benjin Wang, Zhe Shi, Shaoshuai Xu, Hang Hong, Lanqing Li, Hongsheng Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China Max Planck Inst Informat Saarbrucken Germany Huawei Noahs Ark Lab Montreal PQ Canada Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Although DETR-based 3D detectors simplify the detection pipeline and achieve direct sparse predictions, their performance still lags behind dense detectors with post-processing for 3D object detection from point clouds. DETRs usually adopt a larger number of queries than GTs (e.g., 300 queries v.s. similar to 40 objects in Waymo) in a scene, which inevitably incur many false positives during inference. In this paper, we propose a simple yet effective sparse 3D detector, named Query Contrast Voxel-DETR (ConQueR), to eliminate the challenging false positives, and achieve more accurate and sparser predictions. We observe that most false positives are highly overlapping in local regions, caused by the lack of explicit supervision to discriminate locally similar queries. We thus propose a Query Contrast mechanism to explicitly enhance queries towards their best-matched GTs over all unmatched query predictions. This is achieved by the construction of positive and negative GT-query pairs for each GT, and a contrastive loss to enhance positive GT-query pairs against negative ones based on feature similarities. ConQueR closes the gap of sparse and dense 3D detectors, and reduces similar to 60% false positives. Our single-frame ConQueR achieves 71.6 mAPH/L2 on the challenging Waymo Open Dataset validation set, outperforming previous sota methods by over 2.0 mAPH/L2. Code

关键词： detection recognition: Categorization retrieval

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Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation

Orthogonal Annotation Benefits Barely-supervised Medical Ima...

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

作者： Cai, Heng Li, Shumeng Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Natl Inst Healthcare Data Sci Nanjing Peoples R China Southest Univ Nanjing Peoples R China Shandong Womens Univ Jinan Peoples R China

ISBN: (纸本)9798350301298

Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views. These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation. Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation. Then, we perform registration to obtain the initial pseudo labels for sparsely labeled volumes. Subsequently, by introducing unlabeled volumes, we propose a dual-network paradigm named Dense-Sparse Cotraining (DeSCO) that exploits dense pseudo labels in early stage and sparse labels in later stage and meanwhile forces consistent output of two networks. Experimental results on three benchmark datasets validated our effectiveness in performance and efficiency in annotation. For example, with only 10 annotated slices, our method reaches a Dice up to 86.93% on KiTS19 dataset. Our code and models are available at https:// github. com/ HengCai-NJU/DeSCO.

关键词： cell microscopy Medical and biological vision

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RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors

RIDCP: Revitalizing Real Image Dehazing via High-Quality Cod...

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

作者： Wu, Rui-Qi Duan, Zheng-Peng Guo, Chun-Le Chai, Zhi Li, Chongyi Nankai Univ VCIP CS Tianjin Peoples R China Hisilicon Technol Co Ltd Shenzhen Guangdong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data and introducing more robust priors into the network. Specifically, (1) instead of adopting the de facto physical scattering model, we rethink the degradation of real hazy images and propose a phenomenological pipeline considering diverse degradation types. (2) We propose a Real Image Dehazing network via high-quality Codebook Priors (RIDCP). Firstly, a VQGAN is pre-trained on a large-scale high-quality dataset to obtain the discrete codebook, encapsulating high-quality priors (HQPs). After replacing the negative effects brought by haze with HQPs, the decoder equipped with a novel normalized feature alignment module can effectively utilize high-quality features and produce clean results. However, although our degradation pipeline drastically mitigates the domain gap between synthetic and real data, it is still intractable to avoid it, which challenges HQPs matching in the wild. Thus, we re-calculate the distance when matching the features to the HQPs by a controllable matching operation, which facilitates finding better counterparts. We provide a recommendation to control the matching based on an explainable solution. Users can also flexibly adjust the enhancement degree as per their preference. Extensive experiments verify the effectiveness of our data synthesis pipeline and the superior performance of RIDCP in real image dehazing. Code and data are released at https://***/projects/RIDCP.

关键词： Low-level vision

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Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement at 100k Steps-Per-Second

Galactic: Scaling End-to-End Reinforcement Learning for Rear...

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

作者： Berges, Vincent-Pierre Szot, Andrew Chaplot, Devendra Singh Gokaslan, Aaron Mottaghi, Roozbeh Batra, Dhruv Undersander, Eric Meta AI FAIR San Francisco CA 94105 USA Georgia Tech Atlanta GA USA Cornell Univ Ithaca NY USA

ISBN: (纸本)9798350301298

We present Galactic, a large-scale simulation and reinforcement-learning (RL) framework for robotic mobile manipulation in indoor environments. Specifically, a Fetch robot (equipped with a mobile base, 7DoF arm, RGBD camera, egomotion, and onboard sensing) is spawned in a home environment and asked to rearrange objects - by navigating to an object, picking it up, navigating to a target location, and then placing the object at the target location. Galactic is fast. In terms of simulation speed (rendering + physics), Galactic achieves over 421,000 steps-per-second (SPS) on an 8-GPU node, which is 54x faster than Habitat 2.0 [55] (7699 SPS). More importantly, Galactic was designed to optimize the entire rendering+physics+RL interplay since any bottleneck in the interplay slows down training. In terms of simulation+RL speed (rendering + physics + inference + learning), Galactic achieves over 108,000 SPS, which 88x faster than Habitat 2.0 (1243 SPS). These massive speed-ups not only drastically cut the wall-clock training time of existing experiments, but also unlock an unprecedented scale of new experiments. First, Galactic can train a mobile pick skill to > 80% accuracy in under 16 minutes, a 100x speedup compared to the over 24 hours it takes to train the same skill in Habitat 2.0. Second, we use Galactic to perform the largest-scale experiment to date for rearrangement using 5B steps of experience in 46 hours, which is equivalent to 20 years of robot experience. This scaling results in a single neural network composed of task-agnostic components achieving 85% success in GeometricGoal rearrangement, compared to 0% success reported in Habitat 2.0 for the same approach. The code is available at ***/facebookresearch/galactic.

关键词： Embodied vision: Active agents simulation

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UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

UniDexGrasp: Universal Robotic Dexterous Grasping via Learni...

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

作者： Xu, Yinzhen Wan, Weikang Zhang, Jialiang Liu, Haoran Shan, Zikang Shen, Hao Wang, Ruicheng Geng, Haoran Weng, Yijia Chen, Jiayi Liu, Tengyu Yi, Li Wang, He Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen AI Beijing Peoples R China Stanford Univ Stanford CA USA Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud. For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60% on thousands of object instances, which significantly out-performs all baselines, meanwhile showing only a minimal generalization gap.

关键词： Embodied vision: Active agents simulation

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CAMOT: Camera Angle-aware Multi-Object Tracking

CAMOT: Camera Angle-aware Multi-Object Tracking

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Limanta, Felix Uto, Kuniaki Shinoda, Koichi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

This paper proposes CAMOT, a simple camera angle estimator for multi-object tracking to tackle two problems: 1) occlusion and 2) inaccurate distance estimation in the depth direction. Under the assumption that multiple objects are located on a flat plane in each video frame, CAMOT estimates the camera angle using object detection. In addition, it gives the depth of each object, enabling pseudo-3D MOT. We evaluated its performance by adding it to various 2D MOT methods on the MOT17 and MOT20 datasets and confirmed its effectiveness. Applying CAMOT to ByteTrack, we obtained 63.8% HOTA, 80.6% MOTA, and 78.5% IDF1 in MOT17, which are state-of-the-art results. Its computational cost is significantly lower than the existing deep-learning-based depth estimators for tracking.

关键词： Algorithms Video recognition and understanding

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Learning from Noisy Labels with Decoupled Meta Label Purifier

Learning from Noisy Labels with Decoupled Meta Label Purifie...

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

作者： Tu, Yuanpeng Zhang, Boshen Li, Yuxi Liu, Liang Li, Jian Wang, Yabiao Wang, Chengjie Zhao, Cai Rong Tongji Univ Dept Elect & Informat Engn Shanghai Peoples R China Tencent YouTu Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

challenging since DNN can easily memorize inaccurate labels, leading to poor generalization ability. Recently, the meta-learning based label correction strategy is widely adopted to tackle this problem via identifying and correcting potential noisy labels with the help of a small set of clean validation data. Although training with purified labels can effectively improve performance, solving the meta-learning problem inevitably involves a nested loop of bi-level optimization between model weights and hyper-parameters (i.e., label distribution). As compromise, previous methods resort to a coupled learning process with alternating update. In this paper, we empirically find such simultaneous optimization over both model weights and label distribution can not achieve an optimal routine, consequently limiting the representation ability of backbone and accuracy of corrected labels. From this observation, a novel multi-stage label purifier named DMLP is proposed. DMLP decouples the label correction process into label-free representation learning and a simple meta label purifier, In this way, DMLP can focus on extracting discriminative feature and label correction in two distinctive stages. DMLP is a plug-and-play label purifier, the purified labels can be directly reused in naive end-to-end network retraining or other robust learning methods, where state-of-the-art results are obtained on several synthetic and real-world noisy datasets, especially under high noise levels. Code is available at https://***/yuanpengtu/DMLP.

关键词： detection recognition: Categorization retrieval

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