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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

GAPartNet: Cross-Category Domain-Generalizable Object Percep...

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

作者： Geng, Haoran Xu, Helin Zhao, Chengyang Xu, Chao Yi, Li Huang, Siyuan Wang, He Peking Univ CFCS Beijing Peoples R China Peking Univ Sch EECS Beijing Peoples R China Beijing Inst Gen Artificial Intelligence Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350301298

For years, researchers have been devoted to generalizable object perception and manipulation, where cross-category generalizability is highly desired yet underexplored. In this work, we propose to learn such cross-category skills via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (lids, handles, etc.) in 27 object categories, we construct a large-scale part-centric interactive dataset, GAPartNet, where we provide rich, part-level annotations (semantics, poses) for 8,489 part instances on 1,166 objects. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the significant domain gaps between seen and unseen object categories, we propose a robust 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both the simulator and the real world.

关键词： Embodied vision: Active agents simulation

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Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

Efficient Frequency Domain-based Transformers for High-Quali...

引用

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

作者： Kong, Lingshun Dong, Jiangxin Ge, Jianjun Li, Mingqiang Pan, Jinshan Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Elect Technol Grp Corp Informat Sci Acad Beijing Peoples R China

ISBN: (纸本)9798350301298

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches.

关键词： Low-level vision

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Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision

Mapping Degeneration Meets Label Evolution: Learning Infrare...

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

作者： Ying, Xinyi Liu, Li Wang, Yingqian Li, Ruojing Chen, Nuo Lin, Zaiping Sheng, Weidong Zhou, Shilin Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt to achieve infrared small target detection with point-level supervision. Interestingly, during the training phase supervised by point labels, we discover that CNNs first learn to segment a cluster of pixels near the targets, and then gradually converge to predict groundtruth point labels. Motivated by this "mapping degeneration" phenomenon, we propose a label evolution framework named label evolution with single point supervision (LESPS) to progressively expand the point label by leveraging the intermediate predictions of CNNs. In this way, the network predictions can finally approximate the updated pseudo labels, and a pixel-level target mask can be obtained to train CNNs in an end-to-end manner. We conduct extensive experiments with insightful visualizations to validate the effectiveness of our method. Experimental results show that CNNs equipped with LESPS can well recover the target masks from corresponding point labels, and can achieve over 70% and 95% of their fully supervised performance in terms of pixel-level intersection over union (IoU) and object-level probability of detection (Pd), respectively. Code is available at https://***/XinyiYing/LESPS.

关键词： detection recognition: Categorization retrieval

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Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images

Sparse Multi-Modal Graph Transformer with Shared-Context Pro...

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

作者： Nakhli, Ramin Moghadam, Puria Azadi Mi, Haoyang Farahani, Hossein Baras, Alexander Gilks, Blake Bashashati, Ali Univ British Columbia Vancouver BC Canada Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9798350301298

Processing giga-pixel whole slide histopathology images (WSI) is a computationally expensive task. Multiple instance learning (MIL) has become the conventional approach to process WSIs, in which these images are split into smaller patches for further processing. However, MIL-based techniques ignore explicit information about the individual cells within a patch. In this paper, by defining the novel concept of shared-context processing, we designed a multi-modal Graph Transformer (AMIGO) that uses the cellular graph within the tissue to provide a single representation for a patient while taking advantage of the hierarchical structure of the tissue, enabling a dynamic focus between cell-level and tissue-level information. We benchmarked the performance of our model against multiple state-of-the-art methods in survival prediction and showed that ours can significantly outperform all of them including hierarchical vision Transformer (ViT). More importantly, we show that our model is strongly robust to missing information to an extent that it can achieve the same performance with as low as 20% of the data. Finally, in two different cancer datasets, we demonstrated that our model was able to stratify the patients into low-risk and high-risk groups while other state-of-the-art methods failed to achieve this goal. We also publish a large dataset of immunohistochemistry images (InUIT) containing 1,600 tissue microarray (TMA) cores from 188 patients along with their survival information, making it one of the largest publicly available datasets in this context.

关键词： cell microscopy Medical and biological vision

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Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects

Unknown Sniffer for Object Detection: Don't Turn a Blind Eye...

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

作者： Liang, Wenteng Xue, Feng Liu, Yihao Zhong, Guofeng Ming, Anlong Beijing Univ Posts & Telecommunicat Beijing Peoples R China Chongqing Univ Posts & Telecommunicat Chongqing Peoples R China

ISBN: (纸本)9798350301298

The recently proposed open-world object and open-set detection have achieved a breakthrough in finding never-seen-before objects and distinguishing them from known ones. However, their studies on knowledge transfer from known classes to unknown ones are not deep enough, resulting in the scanty capability for detecting unknowns hidden in the background. In this paper, we propose the unknown sniffer (UnSniffer) to find both unknown and known objects. Firstly, the generalized object confidence (GOC) score is introduced, which only uses known samples for supervision and avoids improper suppression of unknowns in the background. Significantly, such confidence score learned from known objects can be generalized to unknown ones. Additionally, we propose a negative energy suppression loss to further suppress the non-object samples in the background. Next, the best box of each unknown is hard to obtain during inference due to lacking their semantic information in training. To solve this issue, we introduce a graph-based determination scheme to replace hand-designed non-maximum suppression (NMS) post-processing. Finally, we present the Unknown Object Detection Benchmark, the first publicly benchmark that encompasses precision evaluation for unknown detection to our knowledge. Experiments show that our method is far better than the existing state-of-the-art methods. Code is available at: https://***/Went-Liang/UnSniffer.

关键词： detection recognition: Categorization retrieval

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CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss

CaPriDe Learning: Confidential and Private Decentralized Lea...

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

作者： Tastan, Nurbek Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Large volumes of data required to train accurate deep neural networks (DNNs) are seldom available with any single entity. Often, privacy concerns prevent entities from sharing data with each other or with a third-party learning service provider. While cross-silo federated learning (FL) allows collaborative learning of large DNNs without sharing the data itself, most existing cross-silo FL algorithms have an unacceptable utility-privacy trade-off. In this work, we propose a framework called Confidential and Private Decentralized (CaPriDe) learning, which optimally leverages the power of fully homomorphic encryption (FHE) to enable collaborative learning without compromising on the confidentiality and privacy of data. In CaPridDe learning, participating entities release their private data in an encrypted form allowing other participants to perform inference in the encrypted domain. The crux of CaPriDe learning is mutual knowledge distillation between multiple local models through a novel distillation loss, which is an approximation of the Kullback-Leibler (KL) divergence between the local predictions and encrypted inferences of other participants on the same data that can be computed in the encrypted domain. Extensive experiments on three datasets show that CaPriDe learning can improve the accuracy of local models without any central coordination, provide strong guarantees of data confidentiality and privacy, and has the ability to handle statistical heterogeneity. Constraints on the model architecture (arising from the need to be FHE-friendly), limited scalability, and computational complexity of encrypted domain inference are the main limitations of the proposed approach. The code can be found at https://***/tnurbek/capride-learning.

关键词： accountability ethics in vision fairness privacy Transparency

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BAEFormer: Bi-directional and Early Interaction Transformers for Bird's Eye View Semantic Segmentation

BAEFormer: Bi-directional and Early Interaction Transformers...

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

作者： Pan, Cong He, Yonghao Peng, Junran Zhang, Qian Sui, Wei Zhang, Zhaoxiang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Future Technol Beijing Peoples R China Horizon Robot Beijing Peoples R China Huawei Inc Shenzhen Guangdong Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Bird's Eye View (BEV) semantic segmentation is a critical task in autonomous driving. However, existing Transformer-based methods confront difficulties in transforming Perspective View (PV) to BEV due to their unidirectional and posterior interaction mechanisms. To address this issue, we propose a novel Bi-directional and Early Interaction Transformers framework named BAEFormer, consisting of (i) an early-interaction PV-BEV pipeline and (ii) a bi-directional cross-attention mechanism. Moreover, we find that the image feature maps' resolution in the cross-attention module has a limited effect on the final performance. Under this critical observation, we propose to enlarge the size of input images and downsample the multiview image features for cross-interaction, further improving the accuracy while keeping the amount of computation controllable. Our proposed method for BEV semantic segmentation achieves state-of-the-art performance in real-time inference speed on the nuScenes dataset, i.e., 38.9 mIoU at 45 FPS on a single A100 GPU.

关键词： 3D from multi-view and sensors

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Egocentric Action recognition by Capturing Hand-Object Contact and Object State

Egocentric Action Recognition by Capturing Hand-Object Conta...

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

作者： Shiota, Tsukasa Takagi, Motohiro Kumagai, Kaori Seshimo, Hitoshi Aono, Yushi NTT Corp NTT Human Informat Labs Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Improving the performance of egocentric action recognition (EAR) requires accurately capturing interactions between actors and objects. In this paper, we propose two learning methods that enable recognition models to capture hand-object contact and object state change. We introduce Hand-Object Contact Learning (HOCL), which enables the model to focus on hand-object contact during actions, and Object State Learning (OSL), which enables the model to focus on object state changes caused by hand actions. Evaluation using a CNN-based model and a transformer-based model on the EGTEA, MECCANO, and EPIC-KITCHENS 100 datasets demonstrated the effectiveness of applying HOCL and OSL. Their application improved overall accuracy by up to 2.24% on EGTEA, 3.97% on MECCANO, and 1.49% on EPIC-KITCHENS 100. In addition, HOCL and OSL improved the performance on data with small training samples and one from unfamiliar scenes. Qualitative analysis revealed that their application enabled the models to precisely capture the interaction between actor and object.

关键词： Algorithms Video recognition and understanding

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Equiangular Basis Vectors

Equiangular Basis Vectors

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

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350301298

We propose Equiangular Basis Vectors (EBVs) for classification tasks. In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. The learning objective of these methods can be summarized as mapping the learned feature representations to the samples' label space. While in metric learning approaches, the main objective is to learn a transformation function that maps training data points from the original space to a new space where similar points are closer while dissimilar points become farther apart. Different from previous methods, our EBVs generate normalized vector embeddings as "predefined classifiers" which are required to not only be with the equal status between each other, but also be as orthogonal as possible. By minimizing the spherical distance of the embedding of an input between its categorical EBV in training, the predictions can be obtained by identifying the categorical EBV with the smallest distance during inference. Various experiments on the ImageNet-1K dataset and other downstream tasks demonstrate that our method outperforms the general fully connected classifier while it does not introduce huge additional computation compared with classical metric learning methods. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge, and our code is open-source and available at https://***/NJUST-VIPGroup/Equiangular-Basis-Vectors.

关键词： detection recognition: Categorization retrieval

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Self-supervised Implicit Glyph Attention for Text recognition

Self-supervised Implicit Glyph Attention for Text Recognitio...

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

作者： Guan, Tongkun Gu, Chaochen Tu, Jingzheng Yang, Xue Feng, Qi Zhao, Yudi Shen, Wei Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9798350301298

The attention mechanism has become the de facto module in scene text recognition (STR) methods, due to its capability of extracting character-level representations. These methods can be summarized into implicit attention based and supervised attention based, depended on how the attention is computed, i.e., implicit attention and supervised attention are learned from sequence-level text annotations and or character-level bounding box annotations, respectively. Implicit attention, as it may extract coarse or even incorrect spatial regions as character attention, is prone to suffering from an alignment-drifted issue. Supervised attention can alleviate the above issue, but it is character category-specific, which requires extra laborious character-level bounding box annotations and would be memory-intensive when handling languages with larger character categories. To address the aforementioned issues, we propose a novel attention mechanism for STR, self-supervised implicit glyph attention (SIGA). SIGA delineates the glyph structures of text images by jointly self-supervised text segmentation and implicit attention alignment, which serve as the supervision to improve attention correctness without extra character-level annotations. Experimental results demonstrate that SIGA performs consistently and significantly better than previous attention-based STR methods, in terms of both attention correctness and final recognition performance on publicly available context benchmarks and our contributed contextless benchmarks.

关键词： detection recognition: Categorization retrieval

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