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Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

Adaptive Sparse Convolutional Networks with Global Context E...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Bowei Du Yecheng Huang Jiaxin Chen Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional network (CEASC). It first develops a context-enhanced group normalization (CE-GN) layer, by replacing the statistics based on sparsely sampled features with the global contextual ones, and then designs an adaptive multi-layer masking strategy to generate optimal mask ratios at distinct scales for compact foreground coverage, promoting both the accuracy and efficiency. Extensive experimental results on two major benchmarks, i.e. VisDrone and UAVDT, demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the inference procedure when plugging into the typical state-of-the-art detection frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is available at https://***/Cuogeihong/CEASC.

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RMLVQA: A Margin Loss Approach For Visual Question Answering with Language Biases

RMLVQA: A Margin Loss Approach For Visual Question Answering...

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conference on computer vision and pattern recognition (CVPR)

作者： Abhipsa Basu Sravanti Addepalli R. Venkatesh Babu Vision and AI Lab Indian Institute of Science Bangalore

Visual Question Answering models have been shown to suffer from language biases, where the model learns a correlation between the question and the answer, ignoring the image. While early works attempted to use question-only models or data augmentations to reduce this bias, we propose an adaptive margin loss approach having two components. The first component considers the frequency of answers within a question type in the training data, which addresses the concern of the class-imbalance causing the language biases. However, it does not take into account the answering difficulty of the samples, which impacts their learning. We address this through the second component, where instance-specific margins are learnt, allowing the model to distinguish between samples of varying complexity. We introduce a bias-injecting component to our model, and compute the instance-specific margins from the confidence of this component. We combine these with the estimated margins to consider both answer-frequency and task-complexity in the training loss. We show that, while the margin loss is effective for out-of-distribution (ood) data, the bias-injecting component is essential for generalising to in-distribution (id) data. Our proposed approach, Robust Margin Loss for Visual Question Answering (RMLVQA) 1 1 Code available at https://***/val-iisc/RMLVQA improves upon the existing state-of-the-art results when compared to augmentation-free methods on benchmark VQA datasets suffering from language biases, while maintaining competitive performance on id data, making our method the most robust one among all comparable methods.

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Teaching Structured vision & Language Concepts to vision & Language Models

Teaching Structured Vision & Language Concepts to Vision & L...

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conference on computer vision and pattern recognition (CVPR)

作者： Sivan Doveh Assaf Arbelle Sivan Harary Eli Schwartz Roei Herzig Raja Giryes Rogerio Feris Rameswar Panda Shimon Ullman Leonid Karlinsky IBM Research Weizmann Institute of Science Tel-Aviv University MIT-IBM Watson AI Lab

vision and Language ( $VL$ ) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a challenge. We introduce the collective notion of Structured vision & Language Concepts (SVLC) which includes object attributes, relations, and states which are present in the text and visible in the image. Recent studies have shown that even the best $VL$ models struggle with SVLC. A possible way of fixing this issue is by collecting dedicated datasets for teaching each SVLC type, yet this might be expensive and time-consuming. Instead, we propose a more elegant data-driven approach for enhancing $VL$ models' understanding of SVLCs that makes more effective use of existing $VL$ pre-training datasets and does not require any additional data. While automatic understanding of image structure still remains largely unsolved, language structure is much better modeled and understood, allowing for its effective utilization in teaching $VL$ models. In this paper, we propose various techniques based on language structure understanding that can be used to manipulate the textual part of off-the-shelf paired $VL$ datasets. $VL$ models trained with the updated data exhibit a significant improvement of up to 15% in their SVLC understanding with only a mild degradation in their zero-shot capabilities both when training from scratch or fine-tuning a pre-trained model. Our code and pretrained models are available at: https://***/SivanDoveh/TSVLC

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Fusing Pre-Trained Language Models with Multimodal Prompts through Reinforcement Learning

Fusing Pre-Trained Language Models with Multimodal Prompts t...

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conference on computer vision and pattern recognition (CVPR)

作者： Youngjae Yu Jiwan Chung Heeseung Yun Jack Hessel Jae Sung Park Ximing Lu Rowan Zellers Prithviraj Ammanabrolu Ronan Le Bras Gunhee Kim Yejin Choi Department of Artificial Intelligence Yonsei University Department of Computer Science and Engineering Seoul National University Allen Institute for Artificial Intelligence Paul G. Allen School of Computer Science University of Washington OpenAI

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6] ethical norms [25]), and larger models like GPT-3 [7] mani-fest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ‡ ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use rein-forcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning; these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://***/JiwanChung/esper.

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PlaneDepth: Self-Supervised Depth Estimation via Orthogonal Planes

PlaneDepth: Self-Supervised Depth Estimation via Orthogonal ...

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conference on computer vision and pattern recognition (CVPR)

作者： Ruoyu Wang Zehao Yu Shenghua Gao ShanghaiTech University University of Tübingen Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai Engineering Research Center of Energy Efficient and Custom AI IC

Multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MDE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the PlaneDepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. PlaneDepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://***/svip-lab/PlaneDepth.

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Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment

Towards Better Gradient Consistency for Neural Signed Distan...

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conference on computer vision and pattern recognition (CVPR)

作者： Baorui Ma Junsheng Zhou Yu-Shen Liu Zhizhong Han School of Software BNRist Tsinghua University Beijing China Department of Computer Science Wayne State University Detroit USA

Neural signed distance functions (SDFs) have shown remarkable capability in representing geometry with details. However, without signed distance supervision, it is still a challenge to infer SDFs from point clouds or multi-view images using neural networks. In this paper, we claim that gradient consistency in the field, indicated by the parallelism of level sets, is the key factor affecting the inference accuracy. Hence, we propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency. Our novelty lies in that we can align all level sets to the zero level set by constraining gradients at queries and their projections on the zero level set in an adaptive way. Our insight is to propagate the zero level set to everywhere in the field through consistent gradients to eliminate uncertainty in the field that is caused by the discreteness of 3D point clouds or the lack of observations from multi-view images. Our proposed loss is a general term which can be used upon different methods to infer SDFs from 3D point clouds and multi-view images. Our numerical and visual comparisons demonstrate that our loss can significantly improve the accuracy of SDFs inferred from point clouds or multiview images under various benchmarks. Code and data are available at https://***/mabaorui/TowardsBetterGradient.

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SIEDOB: Semantic Image Editing by Disentangling Object and Background

SIEDOB: Semantic Image Editing by Disentangling Object and B...

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conference on computer vision and pattern recognition (CVPR)

作者： Wuyang Luo Su Yang Xinjian Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University School of Computer Science and Technology China University of Petroleum

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic Image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://***/WuyangLuo/SIEDOB.

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FeatER: An Efficient Network for Human Reconstruction via Feature Map-Based TransformER

FeatER: An Efficient Network for Human Reconstruction via Fe...

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conference on computer vision and pattern recognition (CVPR)

作者： Ce Zheng Matias Mendieta Taojiannan Yang Guo-Jun Qi Chen Chen Center for Research in Computer Vision University of Central Florida OPPO Seattle Research Center USA Westlake University

Recently, vision transformers have shown great success in a set of human reconstruction tasks such as 2D/3D human pose estimation (2D/3D HPE) and human mesh reconstruction (HMR) tasks. In these tasks, feature map representations of the human structural information are often extracted first from the image by a CNN (such as HRNet), and then further processed by transformer to predict the heatmaps for HPE or HMR. However, existing transformer architectures are not able to process these feature map inputs directly, forcing an unnatural flattening of the location-sensitive human structural information. Furthermore, much of the performance benefit in recent HPE and HMR methods has come at the cost of ever-increasing computation and memory needs. Therefore, to simultaneously address these problems, we propose FeatER, a novel transformer design that preserves the inherent structure of feature map representations when modeling attention while reducing memory and computational costs. Taking advantage of FeatER, we build an efficient network for a set of human reconstruction tasks including 2D HPE, 3D HPE, and HMR. A feature map reconstruction module is applied to improve the performance of the estimated human pose and mesh. Extensive experiments demonstrate the effectiveness of FeatER on various human pose and mesh datasets. For instance, FeatER outperforms the SOTA method Mesh- Graphormer by requiring 5% of Params and 16% of MACs on Human3.6M and 3DPW datasets. The project webpage is https://***/feater_page/.

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Amin Ranem Camila Gonzá lez Anirban Mukhopadhyay GRIS Technical University of Darmstadt Darmstadt Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical vision Transformers

MixMAE: Mixed and Masked Autoencoder for Efficient Pretraini...

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conference on computer vision and pattern recognition (CVPR)

作者： Jihao Liu Xin Huang Jinliang Zheng Yu Liu Hongsheng Li CUHK MMLab SenseTime Research CPII under InnoHK

In this paper, we propose Mixed and Masked AutoEncoder (MixMAE), a simple but efficient pretraining method that is applicable to various hierarchical vision Transformers. Existing masked image modeling (MIM) methods for hierarchical vision Transformers replace a random subset of input tokens with a special [MASK] symbol and aim at reconstructing original image tokens from the corrupted image. However, we find that using the [MASK] symbol greatly slows down the training and causes pretraining-finetuning inconsistency, due to the large masking ratio (e.g., 60% in SimMIM). On the other hand, MAE does not introduce [MASK] tokens at its encoder at all but is not applicable for hierarchical vision Transformers. To solve the issue and accelerate the pretraining of hierarchical models, we replace the masked tokens of one image with visible tokens of another image, i.e., creating a mixed image. We then conduct dual reconstruction to reconstruct the two original images from the mixed input, which significantly improves efficiency. While MixMAE can be applied to various hierarchical Transformers, this paper explores using Swin Transformer with a large window size and scales up to huge model size (to reach 600M parameters). Empirical results demonstrate that MixMAE can learn high-quality visual representations efficiently. Notably, MixMAE with Swin-B/W14 achieves 85.1% top-1 accuracy on ImageNet-1K by pretraining for 600 epochs. Besides, its transfer performances on the other 6 datasets show that MixMAE has better FLOPs / performance tradeoff than previous popular MIM methods.

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