Image Captioning Evaluation

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16 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Class-Grounded Batik Captioning under Scarce Text Annotation: Label Retention and Source-Specific Error Propagation
Oct 22, 2026Novanto Yudistira, Muhammad Dzaka Aufa Fadhillah, Irawati Nurmala SariImage Captioning Image Captioning Evaluation
Universitas Brawijaya, Malang, Indonesia
VisExMEM: Visual Evidence-Based Explainable Multimodal Evaluation Metric
Oct 12, 2026Subhadeep Roy, Christoph Leiter, Steffen EgerVLM Evaluation Image Captioning Evaluation
Natural Language Learning & Generation Lab, University of Technology Nuremberg
DEPICT: Scoring Text-to-Image Alignment by Answer Agreement
Oct 2, 2026Vasco Ramos, Sandra Godinho Silva, Joao Magalhaes +2Text-Image Alignment T2I Generation Evaluation
Sword Health · NOVALINCS, NOVA University of Lisbon
CapProbe: Evaluating Detailed Image Captions via Full-Scene Dense Question Answering
Aug 11, 2026Mouxiao Huang, Qiangyu Yan, Borui Jiang +1VLM Evaluation Image Captioning
Huawei Technologies
CAPEval: A Decoupled Caption Evaluation across Understanding and Generation
Aug 3, 2026Zhipeng Liu, Haochen Wang, Zhaoxiang ZhangImage Captioning Image Captioning Evaluation
University of Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences
A Reconstruction-Based Framework for Caption Evaluation Beyond Reference Captions
Jul 25, 2026Zhijiang Tang, Jiaxin Qi, Kaihua Tang +2Image Captioning Image Captioning Evaluation
2Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, China · 1Computer Network Information Center, Chinese Academy of Sciences, China · 3Tongji University, China +1
Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation
Jun 29, 2026Shuitsu Koyama, Kazuki Matsuda, Yuiga Wada +3Image Captioning Image Captioning Evaluation
Keio University
Do Image-Text Metrics Respect Semantic Invariances?
May 23, 2026Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu +9VLM Evaluation Multimodal Robustness
Oracle AI
BEiTScore: Reference-free Image Captioning Evaluation with an Efficient Cross-Encoder Model
May 20, 2026Gonçalo Gomes, Bruno Martins, Chrysoula ZervaVLM Evaluation Image Captioning
Instituto Superior Técnico, University of Lisbon · INESC-ID · Instituto de Telecomunicações
MSD-Score: Multi-Scale Distributional Scoring for Reference-Free Image Caption Evaluation
May 7, 2026Shichao Kan, Xuyang Zhang, Haojie Zhang +7Cross-Modal Alignment Vision-Language Grounding
School of Computer Science and Engineering, Central South University, Changsha, 410083, China · School of Computer Science and Technology and the State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, 100044, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101408, China +1
One Single Hub Text Breaks CLIP: Identifying Vulnerabilities in Cross-Modal Encoders via Hubness
Apr 30, 2026Hiroyuki Deguchi, Katsuki Chousa, Yusuke SakaiCross-Modal Learning Multimodal Robustness
NTT, Inc. · Nara Institute of Science and Technology
ITIScore: An Image-to-Text-to-Image Rating Framework for the Image Captioning Ability of MLLMs
Apr 4, 2026Zitong Xu, Huiyu Duan, Shengyao Qin +6Image Captioning Evaluation Image Captioning
Shanghai Jiao Tong University · University of Electronic and Science Technology of China · Beijing University of Technology +1
Captioning Daily Activity Images in Early Childhood Education: Benchmark and Algorithm
Apr 2, 2026Sixing Li, Zhibin Gu, Ziqi Zhang +4Image Captioning Evaluation Image Captioning
School of Robotics, Beijing Union University, Beijing 100101, China · College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050010, China · Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing 100190, China +4
HalDec-Bench: Benchmarking Hallucination Detector in Image Captioning
Mar 16, 2026Kuniaki Saito, Risa Shinoda, Shohei Tanaka +3Image Captioning Evaluation Hallucination Detection in VLMs
OMRON SINICX · The University of Tokyo · The University of Osaka
VIVECaption: A Split Approach to Caption Quality Improvement
Mar 8, 2026Varun Ananth, Baqiao Liu, Haoran CaiImage Captioning Evaluation Image Captioning
Adobe Inc.
Redemption Score: A Multi-Modal Evaluation Framework for Image Captioning via Distributional, Perceptual, and Linguistic Signal Triangulation
May 22, 2025Ashim Dahal, Ankit Ghimire, Saydul Akbar Murad +1Image Captioning Image Captioning Evaluation
University of Southern Mississippi, Hattiesburg, MS, USA · School of Business & Technology, Emporia State University, Emporia, KS, USA

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Class-Grounded Batik Captioning under Scarce Text Annotation: Label Retention and Source-Specific Error Propagation

Novanto Yudistira, Muhammad Dzaka Aufa Fadhillah, Irawati Nurmala Sari

Universitas Brawijaya, Malang, Indonesia

Image Captioning Image Captioning Evaluation

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Class labels offer compact supervision for describing cultural-heritage images when expert text annotations are scarce. We investigate a batik-captioning cascade that integrates a LoRA-adapted DINOv2 classifier with Qwen2.5-VL-7B, adapted using LoRA and loaded in 4-bit NF4. A

3\times2

evaluation combines three captioner conditions with two inference prompts to examine adaptation and predicted-class injection. Across 150 caption-evaluation images, all three injected conditions achieve a ground-truth class-label mention rate of 0.9067. The classifier's separate test yields 96.10% pooled accuracy, with 98.33% on 300 public-source images and 75.76% on 33 local images. Supported-class macro-F1 and balanced accuracy on the local subset are 0.8493 and 0.8302, respectively. The classifier and captioner use separate partitions, and their training--evaluation overlap remains unquantified. Differences in label semantics and training support also affect interpretation of the source-specific accuracy gap. Reference-based caption metrics and image--text compatibility scores produce different model rankings. Qualitative examples show both retention of correctly predicted identities and propagation of classification errors into fluent descriptions. These findings characterize how class conditioning preserves label information and transfers upstream errors into batik captions under scarce text annotation.