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SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large vision Language Models

SC-Tune: Unleashing Self-Consistent Referential Comprehensio...

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

作者： Yue, Tongtian Cheng, Jie Guo, Longteng Dai, Xingyuan Zhao, Zijia He, Xingjian Xiong, Gang Lv, Yisheng Liu, Jing CASIA Lab Cognit & Decis Intelligence Complex Syst Beijing Peoples R China CASIA State Key Lab Multimodal Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Recent trends in Large vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations, posing limitations on their practical applicability and potential. To address this gap, we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments, we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://***/ivattyue/SC-Tune.

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Towards real-world navigation with deep differentiable planners

Towards real-world navigation with deep differentiable plann...

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

作者： Ishida, Shu Henriques, Joao F. Univ Oxford Visual Geometry Grp Oxford England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We train embodied neural networks to plan and navigate unseen complex 3D environments, emphasising real-world deployment. Rather than requiring prior knowledge of the agent or environment, the planner learns to model the state transitions and rewards. lb avoid the potentially hazardous trial-and-error of reinforcement learning, we focus on differentiable planners such as Value Iteration Networks (VIN), which are trained offline from safe expert demonstrations. Although they work well in small simulations, we address two major limitations that hinder their deployment. First, we observed that current differentiable planners struggle to plan long-term in environments with a high branching complexity. While they should ideally learn to assign low rewards to obstacles to avoid collisions, these penalties are not strong enough to guarantee collision-free operation. We thus impose a structural constraint on the value iteration, which explicitly learns to model impossible actions and noisy motion. Secondly, we extend the model to plan exploration with a limited perspective camera under translation and fine rotations, which is crucial for real robot deployment. Our proposals significantly improve semantic navigation and exploration on several 2D and 3D environments, succeeding in settings that are otherwise challenging for differentiable planners. As far as we know, we are the first to successfully apply them to the difficult Active vision Dataset, consisting of real images captured from a robot.(1)

关键词： Representation learning Three-dimensional displays Navigation Robot vision systems Semantics Neural networks Reinforcement learning

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Open-set Fine-grained Retrieval via Prompting vision-Language Evaluator

Open-set Fine-grained Retrieval via Prompting Vision-Languag...

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

作者： Wang, Shijie Chang, Jianlong Li, Haojie Wang, Zhihui Ouyang, Wanli Tian, Qi Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Huawei Cloud & AI Shenzhen Peoples R China Shandong Univ Sci & Technol Coll Comp & Engn Qingdao Peoples R China Univ Sydney Sense Time Comp Vis Res Grp Sydney NSW Australia

ISBN: (纸本)9798350301298

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to retrieve unknown subcategories during evaluation. However, current works focus on close-set visual concepts, where all the subcategories are pre-defined, and make it hard to capture discriminative knowledge from unknown subcategories, consequently failing to handle unknown subcategories in open-world scenarios. In this work, we propose a novel Prompting vision-Language Evaluator (PLEor) framework based on the recently introduced contrastive language-image pretraining (CLIP) model, for open-set fine-grained retrieval. PLEor could leverage pre-trained CLIP model to infer the discrepancies encompassing both pre-defined and unknown subcategories, called category-specific discrepancies, and transfer them to the backbone network trained in the close-set scenarios. To make pre-trained CLIP model sensitive to category-specific discrepancies, we design a dual prompt scheme to learn a vision prompt specifying the category-specific discrepancies, and turn random vectors with category names in a text prompt into category-specific discrepancy descriptions. Moreover, a vision-language evaluator is proposed to semantically align the vision and text prompts based on CLIP model, and reinforce each other. In addition, we propose an open-set knowledge transfer to transfer the category-specific discrepancies into the backbone network using knowledge distillation mechanism. Quantitative and qualitative experiments show that our PLEor achieves promising performance on open-set fine-grained datasets.

关键词： vision applications and systems

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Clusformer: A Transformer based Clustering Approach to Unsupervised Large-scale Face and Visual Landmark recognition

Clusformer: A Transformer based Clustering Approach to Unsup...

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

作者： Xuan-Bac Nguyen Duc Toan Bui Chi Nhan Duong Bui, Tien D. Luu, Khoa VinAI Res Hanoi Vietnam Concordia Univ Montreal PQ Canada Univ Arkansas Fayetteville AR 72701 USA

ISBN: (纸本)9781665445092

The research in automatic unsupervised visual clustering has received considerable attention over the last couple years. It aims at explaining distributions of unlabeled visual images by clustering them via a parameterized model of appearance. Graph Convolutional Neural Networks (GCN) have recently been one of the most popular clustering methods. However, it has reached some limitations. Firstly, it is quite sensitive to hard or noisy samples. Secondly, it is hard to investigate with various deep network models due to its computational training time. Finally, it is hard to design an end-to-end training model between the deep feature extraction and GCN clustering modeling. This work therefore presents the Clusformer, a simple but new perspective of Transformer based approach, to automatic visual clustering via its unsupervised attention mechanism. The proposed method is able to robustly deal with noisy or hard samples. It is also flexible and effective to collaborate with different deep network models with various model sizes in an end-to-end framework. The proposed method is evaluated on two popular large-scale visual databases, i.e. Google Landmark and MS-Celeb1M face database, and outperforms prior unsupervised clustering methods. Code will be available at https: //***/VinAIResearch/Clusformer

关键词： Training Visualization Face recognition Computational modeling Clustering methods Transformers Encoding

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PA3D: Pose-Action 3D Machine for Video recognition 32

PA3D: Pose-Action 3D Machine for Video Recognition

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

作者： Yan, An Wang, Yali Li, Zhifeng Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Tencent AI Lab Bellevue WA USA Univ Calif San Diego La Jolla CA 92093 USA Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Recent studies have witnessed the successes of using 3D CNNs for video action ***, most 3D models are built upon RGB and opticalflow streams, which may not fully exploit pose dynamics, i.e., an important cue of modeling human actions. To fill this gap, we propose a concise Pose-Action 3D Machine (PA3D), which can effectively encode multiple pose modalities within a unified 3D framework, and consequently learn spatio-temporalpose representationsfor action recognition. More specifically, we introduce a novel temporal pose convolution to aggregate spatial poses over frames. Unlike the classical temporal convolution, our operation can explicitly learn the pose motions that are discriminativeto recognize human actions. Extensive experiments on three popular benchmarks (i.e., JHMDB, HMDB, and Charades)show that, PA3D outperforms the recentpose-based approaches. Furthermore, PA3D is highly complementary to the recent 3D CNNs, e.g., 13D. Multi-stream fusion achieves the state-of-the-artperformance on all evaluateddata sets.

关键词： Action recognition

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L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

L2G: A Simple Local-to-Global Knowledge Transfer Framework f...

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

作者： Jiang, Peng-Tao Yang, Yuqi Hou, Qibin Wei, Yunchao Nankai Univ CS TMCC Tianjin Peoples R China Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://***/PengtaoJiang/L2G.

关键词： Knowledge engineering Image segmentation computer vision Codes Shape Annotations Semantics

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Few-Shot Object recognition from Machine-Labeled Web Images 30

Few-Shot Object Recognition from Machine-Labeled Web Images

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Zhongwen Zhu, Linchao Yang, Yi Univ Technol Sydney CAI Sydney NSW Australia

ISBN: (纸本)9781538604571

With the tremendous advances made by Convolutional Neural Networks (ConvNets) on object recognition, we can now easily obtain adequately reliable machine-labeled annotations easily from predictions by off-the-shelf ConvNets. In this work, we present an "abstraction memory" based framework for few-shot learning, building upon machinelabeled image annotations. Our method takes large-scale machine-annotated dataset (e.g., OpenImages) as an external memory bank. In the external memory bank, the information is stored in the memory slots in the form of keyvalue, in which image feature is regarded as the key and the label embedding serves as the value. When queried by the few-shot examples, our model selects visually similar data from the external memory bank and writes the useful information obtained from related external data into another memory bank, i.e. abstraction memory. Long Short-Term Memory (LSTM) controllers and attention mechanisms are utilized to guarantee the data written to the abstraction memory correlates with the query example. The abstraction memory concentrates information from the external memory bank to make the few-shot recognition effective. In the experiments, we first confirm that our model can learn to conduct few-shot object recognition on clean humanlabeled data from the ImageNet dataset. Then, we demonstrate that with our model, machine-labeled image annotations are very effective and abundant resources for performing object recognition on novel categories. Experimental results show that our proposed model with machine-labeled annotations achieves great results, with only a 1% difference in accuracy between the machine-labeled annotations and the human-labeled annotations.

关键词： Memory Bank external memory object recognition ANNOTATIONS Banks image feature Abstraction

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Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Tracking 31

Learning Attentions: Residual Attentional Siamese Network fo...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Qiang Teng, Zhu Xing, Junliang Gao, Jin Hu, Weiming Maybank, Stephen University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Information Systems Birkbeck College University of London UK

ISBN: (纸本)9781538664209

Offline training for object tracking has recently shown great potentials in balancing tracking accuracy and speed. However, it is still difficult to adapt an offline trained model to a target tracked online. This work presents a Residual Attentional Siamese Network (RASNet) for high performance object tracking. The RASNet model reformulates the correlation filter within a Siamese tracking framework, and introduces different kinds of the attention mechanisms to adapt the model without updating the model online. In particular, by exploiting the offline trained general attention, the target adapted residual attention, and the channel favored feature attention, the RASNet not only mitigates the over-fitting problem in deep network training, but also enhances its discriminative capacity and adaptability due to the separation of representation learning and discriminator learning. The proposed deep architecture is trained from end to end and takes full advantage of the rich spatial temporal information to achieve robust visual tracking. Experimental results on two latest benchmarks, OTB-2015 and VOT2017, show that the RASNet tracker has the state-of-the-art tracking accuracy while runs at more than 80 frames per second.

关键词： Target tracking Correlation Feature extraction Training Adaptation models Visualization Object tracking

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Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon

Shadows can be Dangerous: Stealthy and Effective Physical-wo...

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

作者： Zhong, Yiqi Liu, Xianming Zhai, Deming Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid colors. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack.

关键词： Printing Laser theory computer vision Codes Perturbation methods Machine vision Machine learning

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M²SGD: Learning to Learn ImportantWeights

M<SUP>2</SUP>SGD: Learning to Learn ImportantWeights

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

作者： Kuo, Nicholas I-Hsien Harandi, Mehrtash Fourrier, Nicolas Walder, Christian Ferraro, Gabriela Suominen, Hanna Australian Natl Univ RSCS Canberra ACT Australia Monash Univ ECSE Clayton Vic Australia CSIRO Data61 Canberra ACT Australia Vole Univ Leonard de Vinci Paris France Univ Turku Dept Future Technol Turku Finland

ISBN: (纸本)9781728193601

Meta-learning concerns rapid knowledge acquisition. One popular approach cast optimisation as a learning problem and it has been shown that learnt neural optimisers updated base learners more quickly than their hand-crafted counterparts. In this paper, we learn an optimisation rule that sparsely updates the learner parameters and removes redundant weights. We present Masked Meta-SGD (M2SGD), a neural optimiser which is not only capable of updating learners quickly, but also capable of removing 83.71% weights for ResNet20s. We release our codes at https://***/Nic5472K/CLvision2020_cvpr_M2SGD.

关键词： computer vision

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