Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on image classification tasks, it lacks object…
Depth Estimationimage-classificationImage ClassificationObject Localization+3Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey
The recent enthusiasm for open-world vision systems show the high interest of the community to perform perception tasks outside of the closed-vocabulary benchmark setups which have been so popular until now. Being able t…
ObjectObject LocalizationUnsupervised Object LocalizationDiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class-agnostic since the model is unsupervise…
ObjectObject LocalizationPseudo LabelWeakly-Supervised Object LocalizationCoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection
Open-vocabulary 3D Object Detection (OV-3DDet) aims to detect objects from an arbitrary list of categories within a 3D scene, which remains seldom explored in the literature. There are primarily two fundamental problems …
3D Object Detectioncross-modal alignmentObjectobject-detection+3Memory-efficient particle filter recurrent neural network for object localization
This study proposes a novel memory-efficient recurrent neural network (RNN) architecture specified to solve the object localization problem. This problem is to recover the object states along with its movement in a noisy…
Object LocalizationLearning to Terminate in Object Navigation
This paper tackles the critical challenge of object navigation in autonomous navigation systems, particularly focusing on the problem of target approach and episode termination in environments with long optimal episode l…
Autonomous NavigationDeep Reinforcement LearningObjectObject Localization+2Context-Aware Entity Grounding with Open-Vocabulary 3D Scene Graphs
We present an Open-Vocabulary 3D Scene Graph (OVSG), a formal framework for grounding a variety of entities, such as object instances, agents, and regions, with free-form text-based queries. Unlike conventional semantic-…
FormNavigateObjectObject Localization+1CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free
The emergence of CLIP has opened the way for open-world image perception. The zero-shot classification capabilities of the model are impressive but are harder to use for dense tasks such as image segmentation. Several me…
Image SegmentationObject LocalizationOpen Vocabulary Semantic SegmentationOpen-Vocabulary Semantic Segmentation+6Background Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve pixel…
ObjectObject LocalizationSemantic SegmentationWeakly-Supervised Object Localization+2DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality
The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in ca…
Collaborative InferenceFeature CompressionObjectObject LocalizationSEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics
Accurately determining salient regions of an image is challenging when labeled data is scarce. DINO-based self-supervised approaches have recently leveraged meaningful image semantics captured by patch-wise features for …
Objectobject-detectionObject DetectionObject Localization+1Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization. We propose a method tha…
ObjectObject LocalizationRepresentation LearningFDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization
This work addresses the task of class-incremental weakly supervised object localization (CI-WSOL). The goal is to incrementally learn object localization for novel classes using only image-level annotations while retaini…
class-incremental learningIncremental LearningKnowledge DistillationObject+2ALWOD: Active Learning for Weakly-Supervised Object Detection
Object detection (OD), a crucial vision task, remains challenged by the lack of large training datasets with precise object localization labels. In this work, we propose ALWOD, a new framework that addresses this problem…
Active LearningObjectobject-detectionObject Detection+3Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
Convolutional networks and vision transformers have different forms of pairwise interactions, pooling across layers and pooling at the end of the network. Does the latter really need to be different? As a by-product of p…
Image ClassificationImage RetrievalMetric LearningObject Discovery+3HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving
Autonomous driving systems generally employ separate models for different tasks resulting in intricate designs. For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple au…
Autonomous DrivingObject LocalizationUnsupervised Object Localization with Representer Point Selection
We propose a novel unsupervised object localization method that allows us to explain the predictions of the model by utilizing self-supervised pre-trained models without additional finetuning. Existing unsupervised and s…
ObjectObject LocalizationUnsupervised Object LocalizationFour Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding
3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language. With a wide range of applications ranging from autonomous indoor robotics to AR/VR, the task …
3D Instance Segmentation3D visual groundingInstance SegmentationObject+3Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs
Accurately localizing objects in three dimensions (3D) is crucial for various computer vision applications, such as robotics, autonomous driving, and augmented reality. This task finds another important application in sp…
Autonomous DrivingCamera CalibrationObject LocalizationSports AnalyticsBroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications
Class activation mapping~(CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation~(WSSS) and object localization~(WSOL). It is the weighted …
Object LocalizationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic Segmentation