Papers Open Vocabulary Object Detection
“Open Vocabulary Object Detection” 태그가 달린 논문 147편 · 필터 해제
Unveiling the Unknown: Open Vocabulary Object Detection with Scene Graphs
Open-vocabulary object detection seeks to identify novel object categories that were not part of the training data. Many knowledge distillation-based approaches have shown promising performance by transferring knowledge …
Open Vocabulary Object DetectionKnowledge DistillationGeneral and Efficient Visual Goal-Conditioned Reinforcement Learning using Object-Agnostic Masks
Goal-conditioned reinforcement learning (GCRL) allows agents to learn diverse objectives using a unified policy. The success of GCRL, however, is contingent on the choice of goal representation. In this work, we propose …
Open Vocabulary Object DetectionReinforcement LearningATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction
Vision-language models such as CLIP have recently propelled open-vocabulary dense prediction tasks by enabling recognition of a broad range of visual concepts. However, CLIP still struggles with fine-grained, region-leve…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object Detection+1Gen-n-Val: Agentic Image Data Generation and Validation
Recently, Large Language Models (LLMs) and Vision Large Language Models (VLLMs) have demonstrated impressive performance as agents across various tasks while data scarcity and label noise remain significant challenges in…
Image HarmonizationInstance SegmentationObjectobject-detection+6From Data to Modeling: Fully Open-vocabulary Scene Graph Generation
We present OvSGTR, a novel transformer-based framework for fully open-vocabulary scene graph generation that overcomes the limitations of traditional closed-set models. Conventional methods restrict both object and relat…
Graph GenerationKnowledge DistillationNovel ConceptsOpen-vocabulary object detection+3FG-CLIP: Fine-Grained Visual and Textual Alignment
Contrastive Language-Image Pre-training (CLIP) excels in multimodal tasks such as image-text retrieval and zero-shot classification but struggles with fine-grained understanding due to its focus on coarse-grained short c…
Image-text Retrievalobject-detectionObject DetectionOpen-vocabulary object detection+5VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-…
Language ModelingLanguage ModellingOpen Vocabulary Object DetectionReferring Expression Comprehension+5An Iterative Feedback Mechanism for Improving Natural Language Class Descriptions in Open-Vocabulary Object Detection
Recent advances in open-vocabulary object detection models will enable Automatic Target Recognition systems to be sustainable and repurposed by non-technical end-users for a variety of applications or missions. New, and …
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object DetectionSuperpowering Open-Vocabulary Object Detectors for X-ray Vision
Open-vocabulary object detection (OvOD) is set to revolutionize security screening by enabling systems to recognize any item in X-ray scans. However, developing effective OvOD models for X-ray imaging presents unique cha…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object DetectionFine-Grained Open-Vocabulary Object Detection with Fined-Grained Prompts: Task, Dataset and Benchmark
Open-vocabulary detectors are proposed to locate and recognize objects in novel classes. However, variations in vision-aware language vocabulary data used for open-vocabulary learning can lead to unfair and unreliable ev…
Objectobject-detectionObject DetectionOpen-vocabulary object detection+1LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation
Large foundation models trained on large-scale vision-language data can boost Open-Vocabulary Object Detection (OVD) via synthetic training data, yet the hand-crafted pipelines often introduce bias and overfit to specifi…
DecoderObjectobject-detectionObject Detection+4Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection
In pursuit of detecting unstinted objects that extend beyond predefined categories, prior arts of open-vocabulary object detection (OVD) typically resort to pretrained vision-language models (VLMs) for base-to-novel cate…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object Detection+1A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection
Open-vocabulary object detection (OVD) aims to detect objects beyond the training annotations, where detectors are usually aligned to a pre-trained vision-language model, eg, CLIP, to inherit its generalizable recognitio…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object DetectionDitHub: A Modular Framework for Incremental Open-Vocabulary Object Detection
Open-Vocabulary object detectors can recognize a wide range of categories using simple textual prompts. However, improving their ability to detect rare classes or specialize in certain domains remains a challenge. While …
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object DetectionSeg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes. To addres…
Domain GeneralizationObject DetectionOpen Vocabulary Object DetectionOpen Vocabulary Semantic Segmentation+5OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images
Remote sensing object detection has made significant progress, but most studies still focus on closed-set detection, limiting generalization across diverse datasets. Open-vocabulary object detection (OVD) provides a solu…
Objectobject-detectionObject DetectionOpen-vocabulary object detection+2Visual-RFT: Visual Reinforcement Fine-Tuning
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce. Recent open-source work like DeepS…
Few-Shot Object DetectionFine-Grained Image Classificationimage-classificationImage Classification+5MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering
Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training. Existing open-vocabulary detectors are limited by compl…
Objectobject-detectionObject DetectionOpen-vocabulary object detection+2Learning the RoPEs: Better 2D and 3D Position Encodings with STRING
We introduce STRING: Separable Translationally Invariant Position Encodings. STRING extends Rotary Position Encodings, a recently proposed and widely used algorithm in large language models, via a unifying theoretical fr…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object Detection+1Modulating CNN Features with Pre-Trained ViT Representations for Open-Vocabulary Object Detection
Owing to large-scale image-text contrastive training, pre-trained vision language model (VLM) like CLIP shows superior open-vocabulary recognition ability. Most existing open-vocabulary object detectors attempt to utiliz…
object-detectionObject DetectionOpen-vocabulary object detectionOpen Vocabulary Object Detection