Exploring Social Media Image Categorization Using Large Models with Different Adaptation Methods: A Case Study on Cultural Nature's Contributions to People

• URL: http://arxiv.org/abs/2410.00275v3
• Date: Tue, 20 May 2025 11:03:37 GMT
• Title: Exploring Social Media Image Categorization Using Large Models with Different Adaptation Methods: A Case Study on Cultural Nature's Contributions to People
• Authors: Rohaifa Khaldi, Domingo Alcaraz-Segura, Ignacio Sánchez-Herrera, Javier Martinez-Lopez, Carlos Javier Navarro, Siham Tabik,
• Abstract summary: Social media images provide valuable insights for modeling, mapping, and understanding human interactions with natural and cultural heritage.<n> categorizing these images into semantically meaningful groups remains highly complex due to the vast diversity and heterogeneity of their visual content.<n>We introduce FLIPS a dataset of Flickr images that capture the interaction between human and nature.<n>We evaluate various solutions based on different types and combinations of large models using various adaptation methods.
• Score: 1.7736307382785161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social media images provide valuable insights for modeling, mapping, and understanding human interactions with natural and cultural heritage. However, categorizing these images into semantically meaningful groups remains highly complex due to the vast diversity and heterogeneity of their visual content as they contain an open-world human and nature elements. This challenge becomes greater when categories involve abstract concepts and lack consistent visual patterns. Related studies involve human supervision in the categorization process and the lack of public benchmark datasets make comparisons between these works unfeasible. On the other hand, the continuous advances in large models, including Large Language Models (LLMs), Large Visual Models (LVMs), and Large Visual Language Models (LVLMs), provide a large space of unexplored solutions. In this work 1) we introduce FLIPS a dataset of Flickr images that capture the interaction between human and nature, and 2) evaluate various solutions based on different types and combinations of large models using various adaptation methods. We assess and report their performance in terms of cost, productivity, scalability, and result quality to address the challenges of social media image categorization.

Related papers

• Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks [0.0]
  Large Language Models (LLMs) have significantly advanced Natural Language Processing (NLP) This study evaluates the continual fine-tuning of various open-source LLMs on key NLU tasks. Our results indicate that models such as Phi-3.5-mini exhibit minimal forgetting while maintaining strong learning capabilities.
  arXiv  Detail & Related papers  (2025-04-01T23:06:55Z)
• Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models [65.82564074712836]
  We introduce DIFfusionHOI, a new HOI detector shedding light on text-to-image diffusion models. We first devise an inversion-based strategy to learn the expression of relation patterns between humans and objects in embedding space. These learned relation embeddings then serve as textual prompts, to steer diffusion models generate images that depict specific interactions.
  arXiv  Detail & Related papers  (2024-10-26T12:00:33Z)
• Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance [78.48606021719206]
  Mini-InternVL is a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. We develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:58:20Z)
• Rethinking VLMs and LLMs for Image Classification [6.550471260627169]
  Large Language Models (LLMs) are increasingly being merged with Visual Language Models (VLMs) to enable new capabilities. We show that, for object and scene recognition, VLMs that do not leverage LLMs can achieve better performance than VLMs that do. We propose a pragmatic solution: a lightweight fix involving a relatively small LLM that efficiently routes visual tasks to the most suitable model for the task.
  arXiv  Detail & Related papers  (2024-10-03T23:40:21Z)
• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
  In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch. Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process. By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
  arXiv  Detail & Related papers  (2024-07-28T06:10:47Z)
• Concept-skill Transferability-based Data Selection for Large Vision-Language Models [56.0725292404808]
  We introduce COINCIDE, an effective and scalable data selection technique for training vision-language models. We cluster the training data using internal activations from a small model, which identifies concept-skill compositions needed by a target LVLM. Experiments demonstrate that COINCIDE achieves superior performance and data selection efficiency against 8 strong baselines.
  arXiv  Detail & Related papers  (2024-06-16T16:15:20Z)
• RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness [94.03511733306296]
  We introduce RLAIF-V, a framework that aligns MLLMs in a fully open-source paradigm for super GPT-4V trustworthiness. RLAIF-V maximally exploits the open-source feedback from two perspectives, including high-quality feedback data and online feedback learning algorithm. Experiments show that RLAIF-V substantially enhances the trustworthiness of models without sacrificing performance on other tasks.
  arXiv  Detail & Related papers  (2024-05-27T14:37:01Z)
• Corpus Considerations for Annotator Modeling and Scaling [9.263562546969695]
  We show that the commonly used user token model consistently outperforms more complex models. Our findings shed light on the relationship between corpus statistics and annotator modeling performance.
  arXiv  Detail & Related papers  (2024-04-02T22:27:24Z)
• Heterogeneous Contrastive Learning for Foundation Models and Beyond [73.74745053250619]
  In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. This survey critically evaluates the current landscape of heterogeneous contrastive learning for foundation models.
  arXiv  Detail & Related papers  (2024-03-30T02:55:49Z)
• Are We on the Right Way for Evaluating Large Vision-Language Models? [92.5761176224556]
  Large vision-language models (LVLMs) have recently achieved rapid progress, sparking numerous studies to evaluate their multi-modal capabilities. We identify two primary issues: Visual content is unnecessary for many samples and intentional data leakage exists. We present MMStar, an elite vision-indispensable multi-modal benchmark comprising 1,500 samples meticulously selected by humans.
  arXiv  Detail & Related papers  (2024-03-29T17:59:34Z)
• T-HITL Effectively Addresses Problematic Associations in Image Generation and Maintains Overall Visual Quality [52.5529784801908]
  We focus on addressing the generation of problematic associations between demographic groups and semantic concepts. We propose a new methodology with twice-human-in-the-loop (T-HITL) that promises improvements in both reducing problematic associations and also maintaining visual quality.
  arXiv  Detail & Related papers  (2024-02-27T00:29:33Z)
• Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models [57.95366341738857]
  In-depth analyses show that instruction-tuned LVLMs exhibit modality gap, showing discrepancy when given textual and visual inputs that correspond to the same concept. We propose a multiple attribute-centric evaluation benchmark, Finer, to evaluate LVLMs' fine-grained visual comprehension ability and provide significantly improved explainability.
  arXiv  Detail & Related papers  (2024-02-26T05:43:51Z)
• GAOKAO-MM: A Chinese Human-Level Benchmark for Multimodal Models Evaluation [55.2480439325792]
  Large Vision-Language Models (LVLMs) have demonstrated great abilities in image perception and language understanding. We propose GAOKAO-MM, a multimodal benchmark based on the Chinese College Entrance Examination (GAOKAO) We evaluate 10 LVLMs and find that the accuracies of all of them are lower than 50%, with GPT-4-Vison (48.1%), Qwen-VL-Plus (41.2%) and Gemini-Pro-Vision (35.1%) ranking in the top three positions.
  arXiv  Detail & Related papers  (2024-02-24T06:57:15Z)
• UniAR: A Unified model for predicting human Attention and Responses on visual content [12.281060227170792]
  We propose UniAR -- a unified model of human attention and preference behavior across diverse visual content. We train UniAR on diverse public datasets spanning natural images, webpages, and graphic designs, and achieve SOTA performance on multiple benchmarks. Potential applications include providing instant feedback on the effectiveness of UIs/visual content, and enabling designers and content-creation models to optimize their creation for human-centric improvements.
  arXiv  Detail & Related papers  (2023-12-15T19:57:07Z)
• Exploring the Robustness of Human Parsers Towards Common Corruptions [99.89886010550836]
  We construct three corruption robustness benchmarks, termed LIP-C, ATR-C, and Pascal-Person-Part-C, to assist us in evaluating the risk tolerance of human parsing models. Inspired by the data augmentation strategy, we propose a novel heterogeneous augmentation-enhanced mechanism to bolster robustness under commonly corrupted conditions.
  arXiv  Detail & Related papers  (2023-09-02T13:32:14Z)
• Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
  Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time. The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
  arXiv  Detail & Related papers  (2023-07-25T17:59:18Z)
• Human Image Generation: A Comprehensive Survey [44.204029557298476]
  In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. The advantages and characteristics of different methods are summarized in terms of model architectures. Due to the wide application potentials, the typical downstream usages of synthesized human images are covered.
  arXiv  Detail & Related papers  (2022-12-17T15:19:45Z)
• Explicit and implicit models in infrared and visible image fusion [5.842112272932475]
  This paper discusses the limitations of deep learning models in image fusion and the corresponding optimization strategies. Ten models for comparison experiments on 21 test sets were screened. The qualitative and quantitative results show that the implicit models have more comprehensive ability to learn image features.
  arXiv  Detail & Related papers  (2022-06-20T06:05:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.