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Progressive Semantic-Guided vision Transformer for Zero-Shot Learning

Progressive Semantic-Guided Vision Transformer for Zero-Shot...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Shiming Hou, Wenjin Khan, Salman Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Huazhong Univ Sci & Technol Wuhan Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9798350353006

Zero-shot learning (ZSL) recognizes the unseen classes by conducting visual-semantic interactions to transfer semantic knowledge from seen classes to unseen ones, supported by semantic information (e.g., attributes). However, existing ZSL methods simply extract visual features using a pre-trained network backbone (i.e., CNN or ViT), which fail to learn matched visual-semantic correspondences for representing semantic-related visual features as lacking of the guidance of semantic information, resulting in undesirable visual-semantic interactions. To tackle this issue, we propose a progressive semantic-guided vision transformer for zero-shot learning (dubbed ZSLViT). ZSLViT mainly considers two properties in the whole network: i) discover the semantic-related visual representations explicitly, and ii) discard the semantic-unrelated visual information. Specifically, we first introduce semantic-embedded token learning to improve the visual-semantic correspondences via semantic enhancement and discover the semantic-related visual tokens explicitly with semantic-guided token attention. Then, we fuse low semantic-visual correspondence visual tokens to discard the semantic-unrelated visual information for visual enhancement. These two operations are integrated into various encoders to progressively learn semantic-related visual representations for accurate visual-semantic interactions in ZSL. The extensive experiments show that our ZSLViT achieves significant performance gains on three popular benchmark datasets, i.e., CUB, SUN, and AWA2.

关键词： zero-shot learning

来源：评论

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Towards Explaining Image-Based Distribution Shifts

Towards Explaining Image-Based Distribution Shifts

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kulinski, Sean Inouye, David I. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding such distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work has focused on either natively handling distribution shift (e.g., Domain Generalization) or merely detecting a shift while assuming any detected shift can be understood and handled appropriately by a human operator. For the latter, we hope to aid in these manual mitigation tasks by explaining the distribution shift to an operator. To this end, we suggest two methods: providing a set of interpretable mappings from the original distribution to the shifted one or providing a set of distributional counterfactual examples. We provide preliminary experiments on these two methods, and discuss important concepts and challenges for moving towards a better understanding of image-based distribution shifts.

关键词： computer vision conferences pattern recognition Task analysis

来源：评论

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Are Labels Needed for Incremental Instance Learning?

Are Labels Needed for Incremental Instance Learning?

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Kilickaya, Mert Vanschoren, Joaquin Eindhoven University of Technology Netherlands

ISBN: (纸本)9798350302493

In this paper, we learn to classify visual object instances, incrementally and via self-supervision (self-incremental). Our learner observes a single instance at a time, which is then discarded from the dataset. Incremental instance learning is challenging, since longer learning sessions exacerbate forgetfulness, and labeling instances is cumbersome. We overcome these challenges via three contributions: i). We propose VINIL, a self-incremental learner that can learn object instances sequentially, ii). We equip VINIL with self-supervision to by-pass the need for instance la-belling, iii). We compare VINIL to label-supervised variants on two large-scale benchmarks [6], [32], and show that VINIL significantly improves accuracy while reducing forgetfulness. © 2023 ieee.

关键词： computer vision

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Rethinking Supervised Depth Estimation for 360° Panoramic Imagery

Rethinking Supervised Depth Estimation for 360° Panoramic I...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： He, Lu Jian, Bing Wen, Yangming Zhu, Haichao Liu, Kelin Feng, Weiwei Liu, Shan Tencent Amer Palo Alto CA 94306 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a single 360 degrees panorama image is a difficult task. It is an ill-posed problem to estimate depth maps from an RGB panorama image due to the intrinsic scale ambiguity issue. To mitigate the scale inconsistency issue in the ground truth depth map, we propose a simple yet effective method to normalize the depth data based on estimated camera height. In addition, we design a multiple head planar-guided depth network, to provide more geometric constraints for depth estimation. Experimental results show that our relative depth estimation task is more accurate than the absolute depth estimation task, and our proposed model produces state-of-the-art performance on both Matterport3D and Stanford2D3D datasets.

关键词： Measurement computer vision Head conferences Estimation Data models pattern recognition

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Key Point-Based Driver Activity recognition

Key Point-Based Driver Activity Recognition

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vats, Arpita Anastasiu, David C. Santa Clara Univ Santa Clara CA 95053 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a key point-based activity recognition framework, built upon pre-trained human pose estimation and facial feature detection models. Our method extracts complex static and movement-based features from key frames in videos, which are used to predict a sequence of key-frame activities. Finally, a merge procedure is employed to identify robust activity segments while ignoring outlier frame activity predictions. We analyze the different components of our framework via a wide array of experiments and draw conclusions with regards to the utility of the model and ways it can be improved. Results show our model is competitive, taking the 11th place out of 27 teams submitting to Track 3 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Pose estimation Activity recognition Predictive models Feature extraction

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Weak-to-Strong 3D Object Detection with X-Ray Distillation

Weak-to-Strong 3D Object Detection with X-Ray Distillation

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Gambashidze, Alexander Dadukin, Aleksandr Golyadkin, Maxim Razzhivina, Maria Makarov, Ilya Artificial Intelligence Res Inst Barcelona Spain HSE Univ Moscow Russia ISP RAS Moscow Russia

ISBN: (纸本)9798350353006

This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs, potentially limiting their applicability to new and evolving architectures. To our knowledge, we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection, marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework, X-Ray Distillation with Object-Complete Frames, suitable for both supervised and semi-supervised settings, that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames, creating Object-Complete frames that represent objects from multiple viewpoints, thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference, we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher, which processes simple and informative Object-Complete frames, effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets, even with default hyperparameters. Code for Object-Complete frames is available here: https://***/sakharok13/X-Ray-TeacherPatching-Tools.

关键词： 3D detection autonomous driving computer vision

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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversari...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wang, Sibo Zhang, Jie Yuan, Zheng Shan, Shiguang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350353006

Large-scale pre-trained vision-language models like CLIP have demonstrated impressive performance across various tasks, and exhibit remarkable zero-shot generalization capability, while they are also vulnerable to imperceptible adversarial examples. Existing works typically employ adversarial training (fine-tuning) as a defense method against adversarial examples. However, direct application to the CLIP model may result in overfitting, compromising the model's capacity for generalization. In this paper, we propose Pre-trained Model Guided Adversarial Fine-Tuning (PMG-AFT) method, which leverages supervision from the original pre-trained model by carefully designing an auxiliary branch, to enhance the model's zero-shot adversarial robustness. Specifically, PMG-AFT minimizes the distance between the features of adversarial examples in the target model and those in the pre-trained model, aiming to preserve the generalization features already captured by the pre-trained model. Extensive Experiments on 15 zero-shot datasets demonstrate that PMG-AFT significantly outper-forms the state-of-the-art method, improving the top-1 robust accuracy by an average of 4.99%. Furthermore, our approach consistently improves clean accuracy by an aver-age of 8.72%. Our code is available at here.(1)

关键词： Adversarial Robustness Large-scale vision-language models Zero-Shot

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RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos

RGBD Objects in the Wild: Scaling Real-World 3D Object Learn...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Xia, Hongchi Fu, Yang Liu, Sifei Wang, Xiaolong Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif San Diego San Diego CA USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9798350353006

We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing, the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos, which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video;(ii) multiple objects in one video;and (iii) an object with a static hand in one video. The dataset is annotated with object masks, real-world scale camera poses, and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis, camera pose estimation, object 6d pose estimation, and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://***/.

关键词： computer vision

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ABAW: Valence-Arousal Estimation, Expression recognition, Action Unit Detection & Multi-Task Learning Challenges

ABAW: Valence-Arousal Estimation, Expression Recognition, Ac...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computer vision and pattern recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieee CVPR 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.

关键词： Measurement computer vision Databases conferences Estimation Benchmark testing Multitasking

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SPIN: Simultaneous Perception, Interaction and Navigation

SPIN: Simultaneous Perception, Interaction and Navigation

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Uppal, Shagun Agarwal, Ananye Xiong, Haoyu Shaw, Kenneth Pathak, Deepak Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

While there has been remarkable progress recently in the fields of manipulation and locomotion, mobile manipulation remains a long-standing challenge. Compared to locomotion or static manipulation, a mobile system must make a diverse range of long-horizon tasks feasible in unstructured and dynamic environments. While the applications are broad and interesting, there are a plethora of challenges in developing these systems such as coordination between the base and arm, reliance on onboard perception for perceiving and interacting with the environment, and most importantly, simultaneously integrating all these parts together. Prior works approach the problem using disentangled modular skills for mobility and manipulation that are trivially tied together. This causes several limitations such as compounding errors, delays in decision-making, and no whole-body coordination. In this work, we present a reactive mobile manipulation framework that uses an active visual system to consciously perceive and react to its environment. Similar to how humans leverage whole-body and hand-eye coordination, we develop a mobile manipulator that exploits its ability to move and see, more specifically - to move in order to see and to see in order to move. This allows it to not only move around and interact with its environment but also, choose "when" to perceive "what" using an active visual system. We observe that such an agent learns to navigate around complex cluttered scenarios while displaying agile whole-body coordination using only ego-vision without needing to create environment maps. Videos are available at https://***

关键词： Active vision End-to-end learning Mobile Manipulation Whole-body coordination

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