Papers Video Captioning
“Video Captioning” 태그가 달린 논문 547편 · 필터 해제
Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering
Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from th…
Question AnsweringVideo CaptioningCan We Trust Video Hallucination Detectors? VidHalLoc for Evaluating the Evaluators
Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model hallucinations, and heterogeneous mechanisms make detector reliabili…
Video Question AnsweringVideo CaptioningTAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding
Traffic Anomaly Understanding (TAU) requires models and systems to detect, reason about, and explain anomalous events in transportation videos. To address this challenge, we propose TAU-Agent, an agentic retrieval-augmen…
Answer GenerationVideo CaptioningPL-NBA: A Possession-level Universal Basketball Video Dataset Supporting Multiple Visual Understanding Tasks
Visual understanding in sports has emerged as a hot topic in computer vision in recent years. Most existing basketball video datasets adopt single action or activity as sample, which can neither preserve the temporal con…
Temporal Action LocalizationAction AnticipationVideo CaptioningSterilizable Scene Graph Generation for Operating Rooms
Scene graph generation from surgical video enables a holistic and structured understanding of surgical scenes by modeling objects and their semantic relationships. Despite recent advances, state-of-the-art approaches rel…
Scene Graph GenerationScene UnderstandingObject DetectionVideo CaptioningAdaptive Two-Stage Visual Token Pruning for Efficient Inference in Video-Language Models
Vision-language models excel at image and video understanding but suffer from high inference latency due to the need to process thousands of tokens per image, limiting their deployment on resource-constrained edge device…
Video CaptioningRefCaptioner: Multi-Reference Image-Grounded Video Captioning
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-reference image-grounded video captionin…
Video ReconstructionVideo CaptioningProCap: Prominence-guided Object Rectification for Faithful and Comprehensive Video Captioning
Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing training-free alternatives instead ground captions in detected object…
Video CaptioningPercepCap: Video Captioner with Structured Spatio-Temporal Perception
Video captioning requires fine-grained spatio-temporal understanding of videos, including spatial perception of where objects are located and temporal perception of when events occur. Existing MLLMs usually generate capt…
Reinforcement LearningVideo CaptioningAVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects co-evolve. Existing large multimodal mod…
Reinforcement LearningSpeech RecognitionVideo CaptioningEmpowering Long-form Omni-modal Understanding with Robust Audio Perception
Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with…
Video CaptioningVEGAS: Human-Aligned Video Caption Evaluation via Gaze
Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation via GAze Score), a training-free metric…
Video CaptioningVideo RetrievalGen4U: Unifying Video Generation and Understanding via Diffusion
Prior work suggests that diffusion representations capture low-level geometry but struggle with high-level semantics. We demonstrate that state-of-the-art video diffusion models overcome this limitation. By systematicall…
Camera Pose EstimationVideo ClassificationDepth EstimationVideo CaptioningTemporal and Cross-Modal Alignment for Enhanced Audiovisual Video Captioning
While Multimodal Large Language Models (MLLMs) have advanced video understanding, achieving precise temporal and cross-modal alignment in audiovisual video captioning remains a formidable challenge. Most existing approac…
Relational ReasoningVideo CaptioningRigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation
Automatic evaluation of image and video captioning is essential for benchmarking multimodal systems, although standard evaluation metrics show limited alignment with human judgments. Recent approaches using large languag…
Video CaptioningCineCap: Structured Reasoning with Spatio-Temporal Anchors for Cinematographic Video Captioning
Cinematographic captioning aims to describe how a video is filmed using professional film-language concepts such as camera movement, shot size, depth of field, composition, and shooting angle. This capability is importan…
Reinforcement LearningVideo CaptioningVideo GenerationCapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales
Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can objectively and comprehensively evaluate t…
Video 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 CaptioningTowards Accurate Emotion-Attributed Video Captioning via Fine-grained Emotion-Cause Pair Extraction
Emotional Video Captioning (EVC) is a challenging task that aims to generate factually accurate and emotionally rich descriptions for videos. Existing EVC methods leverage holistic visual features to mine global emotiona…
Emotion-Cause Pair ExtractionVideo CaptioningOmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning
While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability to strictly adhere to complex, multi-faceted user instructions remain…
Instruction FollowingVideo Captioning