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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...

引用

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

作者： Basu, Abhipsa Addepalli, Sravanti Babu, R. Venkatesh Indian Inst Sci Vis & AI Lab Bangalore India

ISBN: (纸本)9798350301298

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) 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.

关键词： language reasoning vision

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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

引用

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

来源：评论

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Omni Aggregation Networks for Lightweight Image Super-Resolution

Omni Aggregation Networks for Lightweight Image Super-Resolu...

引用

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

作者： Wang, Hang Chen, Xuanhong Ni, Bingbing Liu, Yutian Liu, Jinfan Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks, this work proposes two enhanced components under a new Omni-SR architecture. First, an Omni Self-Attention (OSA) block is proposed based on dense interaction principle, which can simultaneously model pixel-interaction from both spatial and channel dimensions, mining the potential correlations across omni-axis (i.e., spatial and channel). Coupling with mainstream window partitioning strategies, OSA can achieve superior performance with compelling computational budgets. Second, a multi-scale interaction scheme is proposed to mitigate sub-optimal ERF (i.e., premature saturation) in shallow models, which facilitates local propagation and meso-/global-scale interactions, rendering an omni-scale aggregation building block. Extensive experiments demonstrate that Omni-SR achieves record-high performance on lightweight super-resolution benchmarks (e.g., 26.95dB@Urban100 x4 with only 792K parameters). Our code is available at https://***/Francis0625/Omni-SR.

关键词： Low-level vision

来源：评论

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Neuralizer: General Neuroimage Analysis without Re-Training

Neuralizer: General Neuroimage Analysis without Re-Training

引用

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

作者： Czolbe, Steffen Dalca, Adrian V. Univ Copenhagen Copenhagen Denmark MGH Copenhagen Denmark MIT Cambridge MA USA Harvard Med Sch MGH Boston MA USA

ISBN: (纸本)9798350301298

Neuroimage processing tasks like segmentation, reconstruction, and registration are central to the study of neuroscience. Robust deep learning strategies and architectures used to solve these tasks are often similar. Yet, when presented with a new task or a dataset with different visual characteristics, practitioners most often need to train a new model, or fine-tune an existing one. This is a time-consuming process that poses a substantial barrier for the thousands of neuroscientists and clinical researchers who often lack the resources or machine-learning expertise to train deep learning models. In practice, this leads to a lack of adoption of deep learning, and neuroscience tools being dominated by classical frameworks. We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning. Tasks do not have to be known a priori, and generalization happens in a single forward pass during inference. The model can solve processing tasks across multiple image modalities, acquisition methods, and datasets, and generalize to tasks and modalities it has not been trained on. Our experiments on coronal slices show that when few annotated subjects are available, our multi-task network outperforms task-specific baselines without training on the task.

关键词： cell microscopy Medical and biological vision

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Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection

Instance Relation Graph Guided Source-Free Domain Adaptive O...

引用

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

作者： Vibashan, V. S. Oza, Poojan Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21205 USA

ISBN: (纸本)9798350301298

Unsupervised Domain Adaptation (UDA) is an effective approach to tackle the issue of domain shift. Specifically, UDA methods try to align the source and target representations to improve generalization on the target domain. Further, UDA methods work under the assumption that the source data is accessible during the adaptation process. However, in real-world scenarios, the labelled source data is often restricted due to privacy regulations, data transmission constraints, or proprietary data concerns. The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data. In this paper, we explore the SFDA setting for the task of adaptive object detection. To this end, we propose a novel training strategy for adapting a source-trained object detector to the target domain without source data. More precisely, we design a novel contrastive loss to enhance the target representations by exploiting the objects relations for a given target domain input. These object instance relations are modelled using an Instance Relation Graph (IRG) network, which are then used to guide the contrastive representation learning. In addition, we utilize a student-teacher to effectively distill knowledge from source-trained model to target domain. Extensive experiments on multiple object detection benchmark datasets show that the proposed approach is able to efficiently adapt source-trained object detectors to the target domain, outperforming state-of-the-art domain adaptive detection methods. Code and models are provided in https://***/irg-sfda-web/.

关键词： detection recognition: Categorization retrieval

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Learning Partial Correlation based Deep Visual Representation for Image Classification

Learning Partial Correlation based Deep Visual Representatio...

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

作者： Rahmanl, Saimunur Koniuszt, Piotr Wang, Lei Zhou, Luping Moghadam, Peyman Sun, Changming CSIRO Data61 Clayton Vic Australia Univ Wollongong Wollongong NSW Australia Australian Natl Univ Canberra ACT Australia Univ Sydney Sydney NSW Australia Queensland Univ Technol Kelvin Grove Qld Australia

ISBN: (纸本)9798350301298

Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the "confounding" effect. For this case, "partial correlation" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts.

关键词： detection recognition: Categorization retrieval

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Collaborative Static and Dynamic vision-Language Streams for Spatio-Temporal Video Grounding

Collaborative Static and Dynamic Vision-Language Streams for...

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

作者： Lin, Zihang Tan, Chaolei Hu, Jian-Fang Jin, Zhi Ye, Tiancai Zheng, Wei-Shi Sun Yat Sen Univ Guangzhou Peoples R China Tencent Shenzhen Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Spatio-Temporal Video Grounding (STVG) aims to localize the target object spatially and temporally according to the given language query. It is a challenging task in which the model should well understand dynamic visual cues (e.g., motions) and static visual cues (e.g., object appearances) in the language description, which requires effective joint modeling of spatio-temporal visual-linguistic dependencies. In this work, we propose a novel framework in which a static vision-language stream and a dynamic vision-language stream are developed to collaboratively reason the target tube. The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances. The dynamic stream models visual-linguistic dependencies across multiple consecutive frames to capture dynamic cues like motions. We further design a novel cross-stream collaborative block between the two streams, which enables the static and dynamic streams to transfer useful and complementary information from each other to achieve collaborative reasoning. Experimental results show the effectiveness of the collaboration of the two streams and our overall framework achieves new state-of-the-art performance on both HCSTVG and VidSTG datasets.

关键词： language reasoning vision

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Prototype-based Embedding Network for Scene Graph Generation

Prototype-based Embedding Network for Scene Graph Generation

引用

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

作者： Zheng, Chaofan Lyu, Xinyu Gao, Lianli Dai, Bo Song, Jingkuan Univ Elect Sci & Technol China Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9798350301298

Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous possible subject-object combinations, there is a large intra-class variation within each predicate category, e.g., "man-eating-pizza, giraffe-eating-leaf", and the severe inter-class similarity between different classes, e.g., "man-holding-plate, man-eating-pizza", in model's latent space. The above challenges prevent current SGG methods from acquiring robust features for reliable relation prediction. In this paper, we claim that the predicate's category-inherent semantics can serve as class-wise prototypes in the semantic space for relieving the challenges. To the end, we propose the Prototype-based Embedding Network (PE-Net), which models entities/predicates with prototype-aligned compact and distinctive representations and thereby establishes matching between entity pairs and predicates in a common embedding space for relation recognition. Moreover, Prototype-guided Learning (PL) is introduced to help PE-Net efficiently learn such entity-predicate matching, and Prototype Regularization (PR) is devised to relieve the ambiguous entity-predicate matching caused by the predicate's semantic overlap. Extensive experiments demonstrate that our method gains superior relation recognition capability on SGG, achieving new state-of-the-art performances on both Visual Genome and Open Images datasets. The codes are available at https://***/VL- Group/PENET.

关键词： and reasoning language vision

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Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement

Learning Semantic-Aware Knowledge Guidance for Low-Light Ima...

引用

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

作者： Wu, Yuhui Pan, Chen Wang, Guoqing Yang, Yang Wei, Jiwei Li, Chongyi Shen, Heng Tao Univ Elect Sci & Technol China Ctr Future Media Chengdu Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9798350301298

Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement

关键词： Low-level vision

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InternImage: Exploring Large-Scale vision Foundation Models with Deformable Convolutions

InternImage: Exploring Large-Scale Vision Foundation Models ...

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

作者： Wang, Wenhai Dai, Jifeng Chen, Zhe Huang, Zhenhang Li, Zhiqi Zhu, Xizhou Hu, Xiaowei Lu, Tong Lu, Lewei Li, Hongsheng Wang, Xiaogang Qiao, Yu Shanghai AI Lab Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China Nanjing Univ Nanjing Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early state. This work presents a new large-scale CNN-based foundation model, termed InternImage, which can obtain the gain from increasing parameters and training data like ViTs. Different from the recent CNNs that focus on large dense kernels, InternImage takes deformable convolution as the core operator, so that our model not only has the large effective receptive field required for downstream tasks such as detection and segmentation, but also has the adaptive spatial aggregation conditioned by input and task information. As a result, the proposed InternImage reduces the strict inductive bias of traditional CNNs and makes it possible to learn stronger and more robust patterns with large-scale parameters from massive data like ViTs. The effectiveness of our model is proven on challenging benchmarks including ImageNet, COCO, and ADE20K. It is worth mentioning that InternImage-H achieved a new record 65.4 mAP on COCO test-dev and 62.9 mIoU on ADE20K, outperforming current leading CNNs and ViTs.

关键词： detection recognition: Categorization retrieval

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