Papers Dense Captioning
“Dense Captioning” 태그가 달린 논문 87편 · 필터 해제
PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection
Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image captions, but reliably…
Panoptic SegmentationDense CaptioningPhrase GroundingVisChronos: Revolutionizing Image Captioning Through Real-Life Events
This paper aims to bridge the semantic gap between visual content and natural language understanding by leveraging historical events in the real world as a source of knowledge for caption generation. We propose VisChrono…
Natural Language UnderstandingDense CaptioningImage CaptioningCapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning
Image and video captioning are fundamental tasks that bridge the visual and linguistic domains, playing a critical role in pre-training Large Vision-Language Models (LVLMs). Current state-of-the-art captioning models are…
Reinforcement LearningVideo CaptioningDense CaptioningCodecCap: High-Fidelity Codec-Inspired Residual Modeling for Dense Video Captioning
Existing video captioning methods struggle to balance visual fidelity and redundancy: holistic captions are compact but lose fine-grained evidence, whereas segment-wise captions improve coverage but introduce heavy redun…
Dense Video CaptioningDense CaptioningCOPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection
Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existing VLM-based VAD methods suffer from a fundamental mismatch between t…
Video Question AnsweringVideo Anomaly DetectionReinforcement LearningDense CaptioningCurvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding
Accurate 3D scene description is fundamental to robotic navigation and augmented reality, yet current dense captioning methods face significant limitations in processing sparse point cloud data. % Existing approaches tha…
Object LocalizationScene UnderstandingDense CaptioningOmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding
Video Temporal Grounding (VTG), the task of localizing video segments from text queries, struggles in open-world settings due to limited dataset scale and semantic diversity, causing performance gaps between common and r…
Reinforcement LearningDense CaptioningLoFi: Location-Aware Fine-Grained Representation Learning for Chest X-ray
Fine-grained representation learning is crucial for retrieval and phrase grounding in chest X-rays, where clinically relevant findings are often spatially confined. However, the lack of region-level supervision in contra…
Representation LearningDense CaptioningPhrase GroundingPenguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders
Vision Language Model (VLM) development has largely relied on scaling model size, which hinders deployment on compute-constrained mobile and edge devices such as smartphones and robots. In this work, we explore the perfo…
Mathematical ReasoningContrastive LearningDense CaptioningTimeChat-Captioner: Scripting Multi-Scene Videos with Time-Aware and Structural Audio-Visual Captions
This paper proposes Omni Dense Captioning, a novel task designed to generate continuous, fine-grained, and structured audio-visual narratives with explicit timestamps. To ensure dense semantic coverage, we introduce a si…
Dense CaptioningVisual ReasoningOpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation
Recent advances in diffusion models have significantly elevated the visual fidelity of Virtual Try-On (VTON) systems, yet reliable evaluation remains a persistent bottleneck. Traditional metrics struggle to quantify fine…
Dense CaptioningVirtual Try-onDIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection
Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucination risks due to weak visual foundation…
Multimodal ReasoningFake News DetectionDense CaptioningCLIMP: Contrastive Language-Image Mamba Pretraining
Contrastive Language-Image Pre-training (CLIP) relies on Vision Transformers whose attention mechanism is susceptible to spurious correlations, and scales quadratically with resolution. To address these limitations, We p…
Cross-Modal RetrievalDense CaptioningDenseScan: Advancing 3D Scene Understanding with 2D Dense Annotation
3D understanding is a key capability for real-world AI assistance. High-quality data plays an important role in driving the development of the 3D understanding community. Current 3D scene understanding datasets often pro…
Question GenerationScene UnderstandingQuestion AnsweringDense CaptioningIF-VidCap: Can Video Caption Models Follow Instructions?
Although Multimodal Large Language Models (MLLMs) have demonstrated proficiency in video captioning, practical applications require captions that follow specific user instructions rather than generating exhaustive, uncon…
Video CaptioningDense CaptioningOne Patch to Caption Them All: A Unified Zero-Shot Captioning Framework
Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption an image, they proceed by textually deco…
Dense CaptioningHarnessing Object Grounding for Time-Sensitive Video Understanding
We propose to improve the time-sensitive video understanding (TSV) capability of video large language models (Video-LLMs) with grounded objects (GO). We hypothesize that TSV tasks can benefit from GO within frames, which…
Dense Captioning3D-MoRe: Unified Modal-Contextual Reasoning for Embodied Question Answering
With the growing need for diverse and scalable data in indoor scene tasks, such as question answering and dense captioning, we propose 3D-MoRe, a novel paradigm designed to generate large-scale 3D-language datasets by le…
Response GenerationQuestion AnsweringData AugmentationDense CaptioningSTSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
We introduce STSBench, a scenario-based framework to benchmark the holistic understanding of vision-language models (VLMs) for autonomous driving. The framework automatically mines pre-defined traffic scenarios from any …
Autonomous DrivingAutonomous VehiclesDense CaptioningMultiple-choice+2Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs
Remarkable progress in 2D Vision-Language Models (VLMs) has spurred interest in extending them to 3D settings for tasks like 3D Question Answering, Dense Captioning, and Visual Grounding. Unlike 2D VLMs that typically pr…
cross-modal alignmentDense CaptioningQuestion AnsweringScene Understanding+1