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Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection

Normalizing Flow based Feature Synthesis for Outlier-Aware O...

引用

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

作者： Kumar, Nishant Segvic, Sinisa Eslami, Abouzar Gumhold, Stefan Tech Univ Dresden Dresden Germany Univ Zagreb FER Zagreb Croatia Carl Zeiss Meditec AG Jena Germany

ISBN: (纸本)9798350301298

Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to providing overconfident predictions for outlier objects. Recent outlier-aware object detection approaches estimate the density of instance-wide features with class-conditional Gaussians and train on synthesized outlier features from their low-likelihood regions. However, this strategy does not guarantee that the synthesized outlier features will have a low likelihood according to the other class-conditional Gaussians. We propose a novel outlier-aware object detection framework that distinguishes outliers from inlier objects by learning the joint data distribution of all inlier classes with an invertible normalizing flow. The appropriate sampling of the flow model ensures that the synthesized outliers have a lower likelihood than inliers of all object classes, thereby modeling a better decision boundary between inlier and outlier objects. Our approach significantly outperforms the state-of-the-art for outlier-aware object detection on both image and video datasets.

关键词： detection recognition: Categorization retrieval

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Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

Divide and Conquer: Answering Questions with Object Factoriz...

引用

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

作者： Chen, Shi Zhao, Qi Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9798350301298

Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://***/szzexpoi/POEM.

关键词： and reasoning language vision

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Robust Single Image Reflection Removal Against Adversarial Attacks

Robust Single Image Reflection Removal Against Adversarial A...

引用

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

作者： Song, Zhenbo Zhang, Zhenyuan Zhang, Kaihao Luo, Wenhan Fan, Zhaoxin Ren, Wenqi Lu, Jianfeng Nanjing Univ Sci & Technol Nanjing Peoples R China Australian Natl Univ Canberra ACT Australia Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the problem of robust deep single-image reflection removal (SIRR) against adversarial attacks. Current deep learning based SIRR methods have shown significant performance degradation due to unnoticeable distortions and perturbations on input images. For a comprehensive robustness study, we first conduct diverse adversarial attacks specifically for the SIRR problem, i.e. towards different attacking targets and regions. Then we propose a robust SIRR model, which integrates the cross-scale attention module, the multi-scale fusion module, and the adversarial image discriminator. By exploiting the multi-scale mechanism, the model narrows the gap between features from clean and adversarial images. The image discriminator adaptively distinguishes clean or noisy inputs, and thus further gains reliable robustness. Extensive experiments on Nature, SIR2, and Real datasets demonstrate that our model remarkably improves the robustness of SIRR across disparate scenes.

关键词： Low-level vision

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Switchable Representation Learning Framework with Self-compatibility

Switchable Representation Learning Framework with Self-compa...

引用

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

作者： Wu, Shengsen Bai, Yan Lou, Yihang Linghu, Xiongkun He, Jianzhong Duan, Ling-Yu Peng Cheng Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Huawei Technol Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resource constraints, which requires features extracted by these models to be aligned in the metric space. The method to achieve feature alignments is called "compatible learning". Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models. We propose a Switchable representation learning Framework with Self-Compatibility (SFSC). SFSC generates a series of compatible sub-models with different capacities through one training process. The optimization of sub-models faces gradients conflict, and we mitigate this problem from the perspective of the magnitude and direction. We adjust the priorities of sub-models dynamically through uncertainty estimation to co-optimize sub-models properly. Besides, the gradients with conflicting directions are projected to avoid mutual interference. SFSC achieves state-of-the-art performance on the evaluated datasets.

关键词： detection recognition: Categorization retrieval

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Deep Frequency Filtering for Domain Generalization

Deep Frequency Filtering for Domain Generalization

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

作者： Lin, Shiqi Zhang, Zhizheng Huang, Zhipeng Lu, Yan Lan, Cuiling Chu, Peng You, Quanzeng Wang, Jiang Liu, Zicheng Parulkar, Amey Navkal, Viraj Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China Microsoft Albuquerque NM USA Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of learned features. In this paper, we propose Deep Frequency Filtering (DFF) for learning domain-generalizable features, which is the first endeavour to explicitly modulate the frequency components of different transfer difficulties across domains in the latent space during training. To achieve this, we perform Fast Fourier Transform (FFT) for the feature maps at different layers, then adopt a light-weight module to learn attention masks from the frequency representations after FFT to enhance transferable components while suppressing the components not conducive to generalization. Further, we empirically compare the effectiveness of adopting different types of attention designs for implementing DFF. Extensive experiments demonstrate the effectiveness of our proposed DFF and show that applying our DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.

关键词： detection recognition: Categorization retrieval

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GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

GPT as Psychologist? Preliminary Evaluations for GPT-4V on V...

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

作者： Lu, Hao Niu, Xuesong Wang, Jiyao Wang, Yin Hu, Qingyong Tang, Jiaqi Zhang, Yuting Yuan, Kaishen Huang, Bin Yu, Zitong He, Dengbo Deng, Shuiguang Chen, Hao Chen, Yingcong Shan, Shiguang Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Zhejiang Univ Beijing Inst Gen Artificial Intelligence Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Great Bay Univ Portsmouth NH USA Beihang Univ Hangzhou Res Inst Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350365474

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in language understanding, it is critical to evaluate the performance of downstream tasks for better human-centric applications. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks. The results show that GPT-4V has high accuracy in facial action unit recognition and micro-expression detection while its general facial expression recognition performance is not accurate. We also highlight the challenges of achieving fine-grained micro-expression recognition and the potential for further study and demonstrate the versatility and potential of GPT-4V for handling advanced tasks in emotion recognition and related fields by integrating with taskrelated agents for more complex tasks, such as heart rate estimation through signal processing. In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing. Our interesting examples are at https://***/Envision-Research/GPT4Affectivity.

关键词： Emotion recognition

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PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable ...

引用

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

作者： Nauta, Meike Schloetterer, Joerg van Keulen, Maurice Seifert, Christin Univ Twente Enschede Netherlands Univ Duisburg Essen Essen Germany

ISBN: (纸本)9798350301298

Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-bydesign, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://***/M- Nauta/PIPNet.

关键词： Explainable computer vision

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High-fidelity Event-Radiance Recovery via Transient Event Frequency

High-fidelity Event-Radiance Recovery via Transient Event Fr...

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

作者： Han, Jin Asano, Yuta Shi, Boxin Zheng, Yinqiang Sato, Imari Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan Natl Inst Informat Tokyo Japan Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Peking Univ Sch Comp Sci Natl Engn Res Ctr Visual Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

关键词： Physics-based vision and shape-from-X

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Texts as Images in Prompt Tuning for Multi-Label Image recognition

Texts as Images in Prompt Tuning for Multi-Label Image Recog...

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

作者： Guo, Zixian Dong, Bowen Ji, Zhilong Bai, Jinfeng Guo, Yiwen Zuo, Wangmeng Harbin Inst Technol Harbin Peoples R China Tomorrow Adv Life Beijing Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. The code is released at https://***/guozix/TaI-DPT.

关键词： continual low-shot meta or long-tail learning Transfer

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Is BERT Blind? Exploring the Effect of vision-and-Language Pretraining on Visual Language Understanding

Is BERT Blind? Exploring the Effect of Vision-and-Language P...

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

作者： Alper, Morris Fiman, Michael Averbuch-Elor, Hadar Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9798350301298

Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modelling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being under-performed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts.

关键词： and reasoning language vision

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