We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal is to (1) discover and model the different perception experiences in a dataset of images and captions, where each experience is defined by an objective factual and a subjective affective aspect, and (2) associate images to their relevant perception experiences.
arXiv:2609.36776v1 Announce Type: new Abstract: Emotional Video Captioning aims to generate factually accurate and emotionally empathetic descriptions. While recent methods have recognized the import...
The paper investigates whether Vision‑Language Models (VLMs) can understand visual persuasiveness by evaluating image‑message pairs that humans consistently judge as persuasive. It introduces Visual Persuasive Factors (VPFs), a taxonomy from cognitive psychology, to quantify visual cues influencing persuasive judgments. Empirical analysis shows VLMs tend to over‑predict persuasiveness, partially reproducing human patterns but often generating false positives, and that VPF‑guided interventions can improve performance only when properly framed.
arXiv:2607. 16214v1 Announce Type: cross Abstract: Image descriptions represented with language models (LMs) predict human brain responses to naturalistic images in high-level visual regions, but the factors driving this predictivity remain unclear.
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
The paper introduces MObyGaze, a dataset of 20 films annotated by experts for multimodal objectification, covering 6072 segments across 43 hours of video. It defines objectification through a structured thesaurus of 5 sub‑constructs and 11 concepts spanning visual, speech, and audio modalities. The authors formulate learning tasks, explore label diversity strategies, and benchmark vision, text, and audio models to demonstrate the task’s feasibility.
arXiv:2607. 18237v1 Announce Type: cross Abstract: Human visual similarity judgments are context-dependent.
The paper introduces DAN, a training‑free inference‑time framework that improves affective reasoning in multimodal large language models. It combines a Hierarchical Emotional Reasoning Chain (HERC) to better capture fine‑grained visual cues and a Contrastive Discriminative Visual Pruning (CDVP) module to isolate discriminative tokens for semantically similar emotions. Experiments show significant gains, notably a +10.47% improvement on the WebEmo25 benchmark with Qwen3‑VL‑8B‑Instruct.
MemeTAG introduces a dual‑objective framework for classifying harmful memes by combining keyword generation from a pretrained Vision‑Language Model with an Aggregated Tag Inference Network (ATIN) that condenses these keywords into a rich semantic embedding. The embedding is used as a target for an auxiliary reconstruction loss, encouraging deep alignment between visual and textual features. This approach, along with a three‑stage training strategy, achieves new state‑of‑the‑art results on the HarMeme, Hateful Memes Challenge, and PrideMM datasets.
In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (\textbf{S}hared-task on \textbf{H}allucinations and \textbf{R}elated \textbf{O}bservable \textbf{O}vergeneration...
The paper explores how AI can develop its own aesthetic categorization of art across text, audio, image, and video without explicit labels. Using a self‑supervised framework, the authors embed these modalities into a shared 256‑dimensional space and iteratively cluster the data to uncover aesthetic structure. They compare the AI’s cluster assignments with human affective labels, highlighting divergences and discussing implications for cross‑modal similarity, media organization, and automated labeling.
The paper introduces a self‑supervised framework that maps text, audio, image, and video into a shared 256‑dimensional embedding space and uses iterative clustering to uncover aesthetic structure. It examines how AI’s cluster assignments diverge from human affective labels on a weakly supervised multimodal dataset. The study highlights implications for cross‑modal similarity, media organization for Retrieval‑Augmented Generation, and automated data labeling.
The paper introduces Auto-Comp, a fully automated, concept-driven pipeline that generates photorealistic compositional benchmarks for vision‑language models. Auto‑Comp creates paired Minimal and Contextual samples for each concept, enabling isolation of core binding abilities from visio‑linguistic complexity. Evaluations across 25 models reveal consistent failures in attribute and relational binding, with context helping relational tasks but hindering attribute tasks due to visual clutter.