Copertina

Training A Small Emotional Vision Language Model for Visual Art Comprehension

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Jing, Zheng, Liang, Wang, Meng, Guo, Dan
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

EmoSEM: Segment and Explain Emotion Stimuli in Visual Art
di: Zhang, Jing, et al.
Pubblicazione: (2025)

A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
di: Jing, Liqiang, et al.
Pubblicazione: (2025)

REVAL: A Comprehension Evaluation on Reliability and Values of Large Vision-Language Models
di: Zhang, Jie, et al.
Pubblicazione: (2025)

Small Language Model Meets with Reinforced Vision Vocabulary
di: Wei, Haoran, et al.
Pubblicazione: (2024)

Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
di: Li, Jia, et al.
Pubblicazione: (2022)

Evaluating Attribute Comprehension in Large Vision-Language Models
di: Zhang, Haiwen, et al.
Pubblicazione: (2024)

RelationVLM: Making Large Vision-Language Models Understand Visual Relations
di: Huang, Zhipeng, et al.
Pubblicazione: (2024)

IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models
di: Zhang, Yiming, et al.
Pubblicazione: (2025)

Efficient-VLN: A Training-Efficient Vision-Language Navigation Model
di: Zheng, Duo, et al.
Pubblicazione: (2025)

Enhancing Large Vision Language Models with Self-Training on Image Comprehension
di: Deng, Yihe, et al.
Pubblicazione: (2024)

Native Visual Understanding: Resolving Resolution Dilemmas in Vision-Language Models
di: Niu, Junbo, et al.
Pubblicazione: (2025)

On the Explainability of Vision-Language Models in Art History
di: Schneider, Stefanie
Pubblicazione: (2026)

FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling
di: Xu, Guixian, et al.
Pubblicazione: (2026)

Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
di: Zhang, Tianyuan, et al.
Pubblicazione: (2024)

VLRMBench: A Comprehensive and Challenging Benchmark for Vision-Language Reward Models
di: Ruan, Jiacheng, et al.
Pubblicazione: (2025)

Evaluating Vision-Language Models for Emotion Recognition
di: Bhattacharyya, Sree, et al.
Pubblicazione: (2025)

LLMind: Bio-inspired Training-free Adaptive Visual Representations for Vision-Language Models
di: Debnath, Soumyaratna, et al.
Pubblicazione: (2026)

Adaptive-VoCo: Complexity-Aware Visual Token Compression for Vision-Language Models
di: Guo, Xiaoyang, et al.
Pubblicazione: (2025)

VLA-IAP: Training-Free Visual Token Pruning via Interaction Alignment for Vision-Language-Action Models
di: Cheng, Jintao, et al.
Pubblicazione: (2026)

SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models
di: Yue, Tongtian, et al.
Pubblicazione: (2024)

Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models
di: Yang, Xiaoyu, et al.
Pubblicazione: (2023)

Contextual Emotion Recognition using Large Vision Language Models
di: Etesam, Yasaman, et al.
Pubblicazione: (2024)

Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning
di: Guo, Longteng, et al.
Pubblicazione: (2026)

InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models
di: Deng, Nianchen, et al.
Pubblicazione: (2025)

Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey
di: Liu, Chenyang, et al.
Pubblicazione: (2024)

Have Large Vision-Language Models Mastered Art History?
di: Strafforello, Ombretta, et al.
Pubblicazione: (2024)

Decomposing Complex Visual Comprehension into Atomic Visual Skills for Vision Language Models
di: Chae, Hyunsik, et al.
Pubblicazione: (2025)

Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models
di: Sun, Haoyi, et al.
Pubblicazione: (2026)

Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets
di: Zhang, Tianxiao, et al.
Pubblicazione: (2024)

Visual Affect Analysis: Predicting Emotions of Image Viewers with Vision-Language Models
di: Nowicki, Filip, et al.
Pubblicazione: (2026)

Revealing the Power of Post-Training for Small Language Models via Knowledge Distillation
di: Rang, Miao, et al.
Pubblicazione: (2025)

HERO: Rethinking Visual Token Early Dropping in High-Resolution Large Vision-Language Models
di: Li, Xu, et al.
Pubblicazione: (2025)

Focusing Where Vision Matters: Selective Training for Large Vision Language Models via Visual Information Gain
di: Lee, Seulbi, et al.
Pubblicazione: (2026)

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
di: Ying, Kaining, et al.
Pubblicazione: (2024)

Towards Training-free Anomaly Detection with Vision and Language Foundation Models
di: Zhang, Jinjin, et al.
Pubblicazione: (2025)

JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse
di: Li, Muyao, et al.
Pubblicazione: (2025)

IKOD: Mitigating Visual Attention Degradation in Large Vision-Language Models
di: Yang, Jiabing, et al.
Pubblicazione: (2025)

Visual-Advantage On-Policy Distillation for Vision-Language Models
di: Liu, Ruiqi, et al.
Pubblicazione: (2026)

DeepScan: A Training-Free Framework for Visually Grounded Reasoning in Large Vision-Language Models
di: Li, Yangfu, et al.
Pubblicazione: (2026)

Vision-Language Model IP Protection via Prompt-based Learning
di: Wang, Lianyu, et al.
Pubblicazione: (2025)