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Relevance-CAM: Your Model Already Knows Where to Look

Relevance-CAM: Your Model Already Knows Where to Look

引用

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

作者： Lee, Jeong Ryong Kim, Sewon Park, Inyong Eo, Taejoon Hwang, Dosik Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781665445092

With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model's inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of computer vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://***/mongeoroo/Relevance-CAM

关键词： Deep learning Location awareness Analytical models computer vision Visualization Computational modeling Transfer learning

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PairDETR : Joint Detection and Association of Human Bodies and Faces

PairDETR : Joint Detection and Association of Human Bodies a...

引用

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

作者： Ali, Ammar Gaikov, Georgii Rybalchenko, Denis Chigorin, Alexander Laptev, Ivan Zagoruyko, Sergey MTS AI ITMO Moscow Russia MTS AI Moscow Russia VisionLabs Hyderabad Telangana India MBZUAI Abu Dhabi U Arab Emirates MTS AI Skoltech Moscow Russia

ISBN: (纸本)9798350353013;9798350353006

Image and video analysis requires not only accurate object detection but also the understanding of relationships among detected objects. Common solutions to relation modeling typically resort to stand-alone object detectors followed by non-differentiable post-processing techniques. Recently introduced detection transformers (DETR) perform end-to-end object detection based on a bipartite matching loss. Such methods, however, lack the ability to jointly detect objects and resolve object associations. In this paper, we build on the DETR approach and extend it to the joint detection of objects and their relationships by introducing an approximated bipartite matching. While our method can generalize to an arbitrary number of objects, we here focus on the modeling of object pairs and their relations. In particular, we apply our method PairDETR to the problem of detecting human bodies and faces, and associating them for the same person. Our approach not only eliminates the need for hand-designed post-processing but also achieves excellent results for body-face associations. We evaluate PairDETR on the challenging CrowdHuman and CityPersons datasets and demonstrate a large improvement over the state of the art. Our training code and pre-trained models are available at https://***/mts-ai/pairdetr

关键词： Association CityPersons computer vision CrowdHuman DETR end-to-end Object detection Transformers

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ACRE: Abstract Causal REasoning Beyond Covariation

ACRE: Abstract Causal REasoning Beyond Covariation

引用

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

作者： Zhang, Chi Jia, Baoxiong Edmonds, Mark Zhu, Song-Chun Zhu, Yixin UCLA Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA

ISBN: (纸本)9781665445092

Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Causal REasoning (ACRE) dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario: direct, indirect, screening-off, and backward-blocking, intentionally going beyond the simple strategy of inducing causal relationships by covariation. By analyzing visual reasoning architectures on this testbed, we notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning. These deficiencies call for future research in models with a more comprehensive capability of causal induction.

关键词： Visualization Pediatrics Systematics Benchmark testing Particle measurements Cognition pattern recognition

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Continual Learning for Visual Search with Backward Consistent Feature Embedding

Continual Learning for Visual Search with Backward Consisten...

引用

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

作者： Wan, Timmy S. T. Chen, Jun-Cheng Wu, Tzer-Yi Chen, Chu-Song Natl Taiwan Univ Taipei Taiwan Acad Sinica Taipei Taiwan Ucfunnel Co Ltd Taipei Taiwan

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In visual search, the gallery set could be incrementally growing and added to the database in practice. However, existing methods rely on the model trained on the entire dataset, ignoring the continual updating of the model. Besides, as the model updates, the new model must re-extract features for the entire gallery set to maintain compatible feature space, imposing a high computational cost for a large gallery set. To address the issues of long-term visual search, we introduce a continual learning (CL) approach that can handle the incrementally growing gallery set with backward embedding consistency. We enforce the losses of inter-session data coherence, neighbor-session model coherence, and intra-session discrimination to conduct a continual learner. In addition to the disjoint setup, our CL solution also tackles the situation of increasingly adding new classes for the blurry boundary without assuming all categories known in the beginning and during model update. To our knowledge, this is the first CL method both tackling the issue of backward-consistent feature embedding and allowing novel classes to occur in the new sessions. Extensive experiments on various benchmarks show the efficacy of our approach under a wide range of setups(1).

关键词： Representation learning Visualization computer vision Computational modeling Coherence Benchmark testing Data models

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Unsupervised Human Pose Estimation through Transforming Shape Templates

Unsupervised Human Pose Estimation through Transforming Shap...

引用

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

作者： Schmidtke, Luca Vlontzos, Athanasios Ellershaw, Simon Lukens, Anna Arichi, Tomoki Kainz, Bernhard Imperial Coll London London England Kings Coll London London England Evelina London Childrens Hosp London England

ISBN: (纸本)9781665445092

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to generalize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants.

关键词： Pediatrics computer vision Three-dimensional displays Shape Tracking Surveillance Pose estimation

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Domain Prompt Learning with Quaternion Networks

Domain Prompt Learning with Quaternion Networks

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

作者： Cao, Qinglong Xu, Zhengqin Chen, Yuntian Ma, Chao Yang, Xiaokang Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Eastern Inst Technol Ningbo Inst Digital Twin Ningbo Peoples R China

ISBN: (纸本)9798350353006

Prompt learning has emerged as a potent and resource-efficient technique in large vision-Language Models (VLMs). However, its application in adapting VLMs to specialized domains like remote sensing and medical imaging, termed domain prompt learning, remains relatively unexplored. Although large-scale domain-specific foundation models offer a potential solution, their focus on a singular vision level presents challenges in prompting both vision and language modalities. To address this limitation, we propose leveraging domain-specific knowledge from these foundation models to transfer the robust recognition abilities of VLMs from generalized to specialized domains, employing quaternion networks. Our method entails utilizing domain-specific vision features from domain-specific foundation models to guide the transformation of generalized contextual embeddings from the language branch into a specialized space within quaternion networks. Furthermore, we introduce a hierarchical approach that derives vision prompt features by analyzing intermodal relationships between hierarchical language prompt features and domain-specific vision features. Through this mechanism, quaternion networks can effectively explore intermodal relationships in specific domains, facilitating domain-specific vision-language contrastive learning. Extensive experiments conducted on domain-specific datasets demonstrate that our proposed method achieves new state-of-the-art results in prompt learning. Codes are available at https://***/caoql98/DPLQ.

关键词： domain prompt learning quaternion networks vision-language models

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IDA-3D: Instance-Depth-Aware 3D Object Detection from Stereo vision for Autonomous Driving

IDA-3D: Instance-Depth-Aware 3D Object Detection from Stereo...

引用

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

作者： Peng, Wanli Pan, Hao Liu, He Sun, Yi Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781728171685

3D object detection is an important scene understanding task in autonomous driving and virtual reality. Approaches based on LiDAR technology have high performance, but Li-DAR is expensive. Considering more general scenes, where there is no LiDAR data in the 3D datasets, we propose a 3D object detection approach from stereo vision which does not rely on LiDAR data either as input or as supervision in training, but solely takes RGB images with corresponding annotated 3D bounding boxes as training data. As depth estimation of object is the key factor affecting the performance of 3D object detection, we introduce an Instance-Depth-Aware (IDA) module which accurately predicts the depth of the 3D bounding box's center by instance-depth awareness, disparity adaptation and matching cost reweighting. Moreover, our model is an end-to-end learning framework which does not require multiple stages or postprocessing algorithm. We provide detailed experiments on KITTI benchmark and achieve impressive improvements compared with the existing image-based methods. Our code is available at https://***/swords123/IDA-3D.

关键词： Object detection

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A mobile vision system for robust multi-person tracking

A mobile vision system for robust multi-person tracking

引用

ieee conference on computer vision and pattern recognition

作者： Ess, Andreas Leibe, Bastian Schindler, Konrad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781424422425

We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.

关键词： Feedback

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Voxel Field Fusion for 3D Object Detection

Voxel Field Fusion for 3D Object Detection

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

作者： Li, Yanwei Qi, Xiaojuan Chen, Yukang Wang, Liwei Li, Zeming Sun, Jian Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China MEGVII Technol Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we present a conceptually simple yet effective framework for cross-modality 3D object detection, named voxel field fusion. The proposed approach aims to maintain cross-modality consistency by representing and fusing augmented image features as a ray in the voxel field. To this end, the learnable sampler is first designed to sample vital features from the image plane that are projected to the voxel grid in a point-to-ray manner, which maintains the consistency in feature representation with spatial context. In addition, ray-wise fusion is conducted to fuse features with the supplemental context in the constructed voxel field. We further develop mixed augmentor to align feature-variant transformations, which bridges the modality gap in data augmentation. The proposed framework is demonstrated to achieve consistent gains in various benchmarks and outperforms previous fusion-based methods on KITTI and nuScenes datasets. Code is made available at https://***/dvlab-research/VFF.(1)

关键词： Bridges computer vision Three-dimensional displays Codes Fuses Computational modeling Object detection

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One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

One Thing One Click: A Self-Training Approach for Weakly Sup...

引用

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

作者： Liu, Zhengzhe Qi, Xiaojuan Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Point cloud semantic segmentation often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose "One Thing One Click," meaning that the annotator only needs to label one point per object. To leverage these extremely sparse labels in network training, we design a novel self-training approach, in which we iteratively conduct the training and label propagation, facilitated by a graph propagation module. Also, we adopt a relation network to generate the per-category prototype and explicitly model the similarity among graph nodes to generate pseudo labels to guide the iterative training. Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic segmentation by a large margin, and our results are also comparable to those of the fully supervised counterparts.

关键词： Training computer vision Three-dimensional displays Annotations Semantics Training data Prototypes

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