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Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective

Rethinking Image Super Resolution from Long-Tailed Distribut...

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

作者： Gou, Yuanbiao Hu, Peng Lv, Jiancheng Zhu, Hongyuan Peng, Xi Sichuan Univ Coll Comp Sci Chengdu Peoples R China ASTAR Inst Infocomm Res I2R Singapore Singapore

ISBN: (纸本)9798350301298

Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer from a machine learning perspective, i.e., the twin fitting problem caused by the long-tailed pixel distribution in natural images. With this explanation, we reformulate image super resolution (SR) as a long-tailed distribution learning problem and solve it by bridging the gaps of the problem between in low- and high-level vision tasks. As a result, we design a long-tailed distribution learning solution, that rebalances the gradients from the pixels in the low- and high-frequency region, by introducing a static and a learnable structure prior. The learned SR model achieves better balance on the fitting of the low- and high-frequency region so that the overall performance is improved. In the experiments, we evaluate the solution on four CNN- and one Transformer-based SR models w.r.t. six datasets and three tasks, and experimental results demonstrate its superiority.

关键词： Low-level vision

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Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification

Bridging the Gap between Model Explanations in Partially Ann...

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

作者： Kim, Youngwook Kim, Jae Myung Jeong, Jieun Schmid, Cordelia Akata, Zeynep Lee, Jungwoo Seoul Natl Univ Seoul South Korea Univ Tubingen Tubingen Germany HodooAI Lab Ho Chi Minh City Vietnam PSL Res Univ CNRS Ecole Normale Super Inria Paris France MPI Intelligent Syst Stuttgart Germany

ISBN: (纸本)9798350301298

Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https:// ***/youngwk/BridgeGapExplanationPAMC.

关键词： detection recognition: Categorization retrieval

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Uncertainty-aware vision-based Metric Cross-view Geolocalization

Uncertainty-aware Vision-based Metric Cross-view Geolocaliza...

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

作者： Fervers, Florian Bullinger, Sebastian Bodensteiner, Christoph Arens, Michael Stiefelhagen, Rainer Fraunhofer IOSB Ettlingen Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9798350301298

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r.t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m.

关键词： Autonomous driving

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Language in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification

Language in a Bottle: Language Model Guided Concept Bottlene...

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

作者： Yang, Yue Panagopoulou, Artemis Zhou, Shenghao Jin, Daniel Callison-Burch, Chris Yatskar, Mark Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9798350301298

Concept Bottleneck Models (CBM) are inherently interpretable models that factor model decisions into human-readable concepts. They allow people to easily understand why a model is failing, a critical feature for high-stakes applications. CBMs require manually specified concepts and often under-perform their black box counterparts, preventing their broad adoption. We address these shortcomings and are first to show how to construct high-performance CBMs without manual specification of similar accuracy to black box models. Our approach, Language Guided Bottlenecks (LaBo), leverages a language model, GPT-3, to define a large space of possible bottlenecks. Given a problem domain, LaBo uses GPT-3 to produce factual sentences about categories to form candidate concepts. LaBo efficiently searches possible bottlenecks through a novel submodular utility that promotes the selection of discriminative and diverse information. Ultimately, GPT-3's sentential concepts can be aligned to images using CLIP, to form a bottleneck layer. Experiments demonstrate that LaBo is a highly effective prior for concepts important to visual recognition. In the evaluation with 11 diverse datasets, LaBo bottlenecks excel at few-shot classification: they are 11.7% more accurate than black box linear probes at 1 shot and comparable with more data. Overall, LaBo demonstrates that inherently interpretable models can be widely applied at similar, or better, performance than black box approaches.(1)

关键词： Explainable computer vision

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Watch or Listen: Robust Audio-Visual Speech recognition with Visual Corruption Modeling and Reliability Scoring

Watch or Listen: Robust Audio-Visual Speech Recognition with...

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

作者： Hong, Joanna Kim, Minsu Choi, Jeongsoo Ro, Yong Man Korea Adv Inst Sci & Technol Image & Video Syst Lab Daejeon South Korea

ISBN: (纸本)9798350301298

This paper deals with Audio-Visual Speech recognition (AVSR) under multimodal input corruption situations where audio inputs and visual inputs are both corrupted, which is not well addressed in previous research directions. Previous studies have focused on how to complement the corrupted audio inputs with the clean visual inputs with the assumption of the availability of clean visual inputs. However, in real life, clean visual inputs are not always accessible and can even be corrupted by occluded lip regions or noises. Thus, we firstly analyze that the previous AVSR models are not indeed robust to the corruption of multimodal input streams, the audio and the visual inputs, compared to uni-modal models. Then, we design multimodal input corruption modeling to develop robust AVSR models. Lastly, we propose a novel AVSR framework, namely Audio-Visual Reliability Scoring module (AV-RelScore), that is robust to the corrupted multimodal inputs. The AV-RelScore can determine which input modal stream is reliable or not for the prediction and also can exploit the more reliable streams in prediction. The effectiveness of the proposed method is evaluated with comprehensive experiments on popular benchmark databases, LRS2 and LRS3. We also show that the reliability scores obtained by AV-RelScore well reflect the degree of corruption and make the proposed model focus on the reliable multimodal representations.

关键词： Multi-modal learning

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Overlooked Factors in Concept-based Explanations: Dataset Choice, Concept Learnability, and Human Capability

Overlooked Factors in Concept-based Explanations: Dataset Ch...

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

作者： Ramaswamy, Vikram V. Kim, Sunnie S. Y. Fong, Ruth Russakovsky, Olga Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350301298

Concept-based interpretability methods aim to explain a deep neural network model's components and predictions using a pre-defined set of semantic concepts. These methods evaluate a trained model on a new, "probe" dataset and correlate the model's outputs with concepts labeled in that dataset. Despite their popularity, they suffer from limitations that are not well-understood and articulated in the literature. In this work, we identify and analyze three commonly overlooked factors in concept-based explanations. First, we find that the choice of the probe dataset has a profound impact on the generated explanations. Our analysis reveals that different probe datasets lead to very different explanations, suggesting that the generated explanations are not generalizable outside the probe dataset. Second, we find that concepts in the probe dataset are often harder to learn than the target classes they are used to explain, calling into question the correctness of the explanations. We argue that only easily learnable concepts should be used in concept-based explanations. Finally, while existing methods use hundreds or even thousands of concepts, our human studies reveal a much stricter upper bound of 32 concepts or less, beyond which the explanations are much less practically useful. We discuss the implications of our findings and provide suggestions for future development of concept-based interpretability methods. Code for our analysis and user interface can be found at https://***/princetonvisualai/OverlookedFactors

关键词： Explainable computer vision

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Rethinking the Learning Paradigm for Dynamic Facial Expression recognition

Rethinking the Learning Paradigm for Dynamic Facial Expressi...

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

作者： Wang, Hanyang Li, Bo Wu, Shuang Shen, Siyuan Liu, Feng Ding, Shouhong Zhou, Aimin East China Normal Univ Shanghai Inst AI Educ Shanghai Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Tencent Youtu Lab Beijing Peoples R China East China Normal Univ Shanghai Int Sch Chief Technol Officer Shanghai Peoples R China

ISBN: (纸本)9798350301298

Dynamic Facial Expression recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format. Previous research has considered non-target frames as noisy frames, but we propose that it should be treated as a weakly supervised problem. We also identify the imbalance of short- and long-term temporal relationships in DFER. Therefore, we introduce the Multi-3D Dynamic Facial Expression Learning (M3DFEL) framework, which utilizes Multi-Instance Learning (MIL) to handle inexact labels. M3DFEL generates 3D-instances to model the strong short-term temporal relationship and utilizes 3DCNNs for feature extraction. The Dynamic Long-term Instance Aggregation Module (DLIAM) is then utilized to learn the long-term temporal relationships and dynamically aggregate the instances. Our experiments on DFEW and FERV39K datasets show that M3DFEL outperforms existing state-of-the-art approaches with a vanilla R3D18 backbone. The source code is available at https://***/faceeyes/M3DFEL.

关键词： body gesture Humans: Face movement pose

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Improving Commonsense in vision-Language Models via Knowledge Graph Riddles

Improving Commonsense in Vision-Language Models via Knowledg...

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

作者： Ye, Shuquan Xie, Yujia Chen, Dongdong Xu, Yichong Yuan, Lu Zhu, Chenguang Liao, Jing Microsoft Redmond WA USA City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

This paper focuses on analyzing and improving the commonsense ability of recent popular vision-language (VL) models. Despite the great success, we observe that existing VL-models still lack commonsense knowledge/reasoning ability (e.g., "Lemons are sour"), which is a vital component towards artificial general intelligence. Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective. Rather than collecting a new VL training dataset, we propose a more scalable strategy, i.e., "Data Augmentation with kNowledge graph linearization for CommonsensE capability" (DANCE). It can be viewed as one type of data augmentation technique, which can inject commonsense knowledge into existing VL datasets on the fly during training. More specifically, we leverage the commonsense knowledge graph (e.g., ConceptNet) and create variants of text description in VL datasets via bidirectional sub-graph sequentialization. For better commonsense evaluation, we further propose the first retrieval-based commonsense diagnostic benchmark. By conducting extensive experiments on some representative VL-models, we demonstrate that our DANCE technique is able to significantly improve the commonsense ability while maintaining the performance on vanilla retrieval tasks. The code and data are available at https:// ***/ pleaseconnectwifi/ DANCE.

关键词： Multi-modal learning

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

作者： Doveh, Sivan Arbelle, Assaf Harary, Sivan Schwartz, Eli Herzig, Roei Giryes, Raja Feris, Rogerio Panda, Rameswar Ullman, Shimon Karlinsky, Leonid IBM Res Yorktown Hts NY 10598 USA Weizmann Inst Sci Rehovot Israel Tel Aviv Univ Tel Aviv Israel MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350301298

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

关键词： Multi-modal learning

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Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

Weakly supervised segmentation with point annotations for hi...

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

作者： Zhang, Hongrun Burrows, Liam Meng, Yanda Sculthorpe, Declan Mukherjee, Abhik Coupland, Sarah E. Chen, Ke Zheng, Yalin Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England Univ Liverpool Dept Math Sci Liverpool Merseyside England Univ Liverpool Ctr Math Imaging Techn Liverpool Merseyside England Univ Nottingham Biodiscovery Inst Sch Med Nottingham England Univ Liverpool Inst Syst Mol & Integrat Biol Liverpool Merseyside England

ISBN: (纸本)9798350301298

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled 'novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models. Code is available at: https://***/hrzhang1123/CVM_WS_Segmentation.

关键词： cell microscopy Medical and biological vision

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