| id | YoP | Paper Name | Description |
|---|---|---|---|
| 2023 | *** | ||
| 48 | 2023 | GLIGEN: Open-Set Grounded Text-to-Image Generation | *** |
| 47 | 2023 | Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation | *** |
| 46 | 2023 | CLIPAG: Towards Generator-Free Text-to-Image Generation | *** |
| 45 | 2023 | Text-to-image Diffusion Models in Generative AI: A Survey | *** |
| 44 | 2023 | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | *** |
| 43 | 2023 | Imagic: Text-Based Real Image Editing With Diffusion Models | *** |
| 42 | 2023 | Adding Conditional Control to Text-to-Image Diffusion Models | *** |
| 41 | 2023 | ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model With Knowledge-Enhanced Mixture-of-Denoising-Experts | *** |
| 40 | 2023 | Multi-Concept Customization of Text-to-Image Diffusion | *** |
| 39 | 2023 | GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis | *** |
| 38 | 2023 | SINE: SINgle Image Editing with Text-to-Image Diffusion Models | *** |
| 37 | 2023 | Scaling Up GANs for Text-to-Image Synthesis | *** |
| 36 | 2023 | Versatile Diffusion: Text, Images and Variations All in One Diffusion Model | *** |
| 35 | 2023 | ReCo: Region-Controlled Text-to-Image Generation | *** |
| 34 | 2023 | Multiscale Feature Extraction and Fusion of Image and Text in VQA | *** |
| 33 | 2023 | BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification | *** |
| 32 | 2023 | MemeCap: A Dataset for Captioning and Interpreting Memes | *** |
| 31 | 2023 | Stochastic evolution of bad memes | This paper hypothesizes that even a bad meme with low/unattractive content can have significant circulation and adoption in a network iff it has a social conformation factor. This phenomenon occurs due to the pluralistic ignorance effect of SNA. The authors have created a mathematical model of meme categorization + propagation evolution. |
| 30 | 2023 | Mapping Meme to Words | This paper introduces a framework, 'ISSUES' which uses 3 techniques, * Textual inversion: it maps the image into the pseudo-text/token space. * Extracts latent representation of textual and image data using CLIP. * Multimodal Fusion It uses dataset (1) HMC and (2) HarMeme |
| 29 | 2023 | Contextualizing Internet Memes Across Social Platforms. | This paper creates a method to map a meme to meme-KG, 'IMKG' and hypothesizes that the learned mapping function can unveil implicit-contextual-knowledge of the meme. For evaluation, the authors have used two social media platforms - (1) Reddit and (2) Dischord. |
| 28 | 2023 | FLYPE : Multitask Prompt Tuning for Multimodal Human Understanding of Social Media | FLYPE has proposed a composite loss function for cross-task, shared prompts and targets unseen scenario to solve. |
| 27 | 2023 | "Somewhere Along Your Pedigree, a Bitch Got Over the Wall!” – A Data-Driven Approach to a Typology of Implicitly Offensive Language | This paper defines a NEW TOPOLOGY and corresponding definitions for implicit hate content. Also, highlights the issues with old approaches. |
| 26 | 2023 | PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models | PromptMTopic identifies and extracts topics involved in a set of memes.Used datasets, (1) TOTALDEFMEME (2)FHM (3)MEMOTION |
| 25 | 2023 | Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: a Case Study on Hateful Memes | It merges two distinct lines of research work, XAI and Cuasal Analysis. The authors have hypothesized that the task of hateful meme detection can be formulated in terms of Average Treatment Effect (ATE of XAI) and summarised gradient-based attention attribution score (from Causal Analysis). |
| 24 | 2023 | Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model | It uses 0-shot prompting in LLaVA for the task of hateful meme detection and correction. |
| 23 | 2023 | Leveraging World Knowledge in Implicit Hate Speech Detection | Uses Entity-Linking technique to incorporate world-contextual knowledge of entities to improve the detection of EXPLICIT and IMPLICIT memes. |
| 22 | 2023 | LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding | *** |
| 21 | 2023 | Chain of Explanation: New Prompting Method to Generate Quality Natural Language Explanation for Implicit Hate Speech | Using predefined prompts, Explanations for implicit hate content are generated in natural language. |
| 20 | 2023 | Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech | It tests the limitations and feasibility of ChatGPT for NLE of an implicit meme. |
| 19 | 2023 | An In-depth Analysis of Implicit and Subtle Hate Speech Messages | It compares different benchmarks for Hate-Speech detection and highlights the facts that conventional models are not suitable for detecting a implicit/subtle hate content. |
| 18 | 2022 | ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection | It is a toxic Data Creation Framework. Using LLM, it uses description-based prompting + adversarial-classifier-in-loop method to generate toxic data using a LLM. |
| 17 | 2022 | Generalizable Implicit Hate Speech Detection using Contrastive Learning | It uses contrastive learning to train a model for CROSS_TASK evaluation for IMPLICIT hate content detection. |
| 16 | 2021 | Latent Hatred: A Benchmark for Understanding Implicit Hate Speech | It defines a theoretically justified Taxonomy and a fine-grained annotated corpus for IMPLICIT hate speech. |
| 15 | 2023 | Tad: A Domain-Aware Framework Learning to Adapt Target Shifts of Hate Speech | It incorporates Cross-Domain + Target-Shift modelling of hate sppech content. |
| 15 | 2023 | Social Meme-ing: Measuring Linguistic Variation in Memes | It considers each meme as a node and each template as a semantically binding function w.r.t. the meme-text. Later it extracts all possible clusters, where each cluster contains memes with similar templates. Authors have studied these clusters to know how socio-factors have influenced the evolution of meme-text demographically. |
| 14 | 2023 | COGVLM: VISUAL EXPERT FOR LARGE LANGUAGE MODELS | A visual-language model. It trains and attaches a visual expert in attention + MLP layers of a language model which is responsible for extracting important similarity between text and an image. It is able to do deep-fusion. |
| 13 | 2023 | COVLM: COMPOSING VISUAL ENTITIES AND RELATIONSHIPS IN LARGE LANGUAGE MODELS VIA COMMUNICATIVE DECODING | A helper model that can guide an LLM to express the relationship between entities in the image and the visual text. |
| 12 | 2023 | GLaMM : Pixel Grounding Large Multimodal Model | First kind of LMM that can generate a textual response at multiple shades of granularity w.r.t. an input image. |
| 11 | 2023 | A Template Is All You Meme | It builds a KB based on 54k images. It consists of different templates with corresponding information and example images. |
| 10 | 2023 | Detecting and Correcting Hate Speech in Multimodal Memes with Large Visual Language Model | It uses a 0-shot prompt with LlaVa for Detecting and Correcting Hate meme. |
| 9 | 2023 | You Know What I Meme Enhancing People's Understanding and Awareness of Hateful Memes Using Crowdsourced Explanations | *** |
| 8 | 2023 | What Do You MEME Generating Explanations for Visual Semantic Role Labelling in Memes | Authors have represented an HVV dataset of 3K instances and an NLE novel task + a model 'LUMEN' with a comparative study. |
| 7 | 2023 | Pro-Cap Leveraging a Frozen Vision-Language Model for Hateful Meme Detection | It uses 0-shot-based QA to probe an LM and generate captions for no-text memes. |
| 6 | 2023 | MEMEX Detecting Explanatory Evidence for Memes via Knowledge-Enriched Contextualization | Given a meme and a document, MEMEX can extract the background information. |
| 5 | 2023 | HateProof: Are Hateful Meme Detection Systems really Robust? | A case study over the vulnerability of the existing systems in hate detection. It provides a solution based on contrastive learning + adversarial training. |
| 4 | 2023 | Decoding the Underlying Meaning of Multimodal Hateful Memes. | A novel task + annotated-contextual information dataset + relevance study over hateful meme. |
| 3 | 2023 | Characterizing the Entities in Harmful Memes Who is the Hero, the Villain, the Victim | Understanding and Identifying Hero, Villain and Victim of a meme. |
| 2 | 2023 | Review of Vision-Language Models and their Performance on the Hateful Memes Challenge | A survey of different unimodal + multimodal systems w.r.t. meme classification. |
| 1 | 2022 | Prompting for Multimodal Hateful Meme Classification | A prompt-based model that takes (1) image text, (2) image caption, and (3) a predefined prompt to classify an input meme. |