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AffectDelta: Beyond Emotion Labels for Image Editing

AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source image’s affective state and encodes the signed difference to guide a diffusion backbone that applies context‑dependent semantic and appearance changes. The authors created a large AffectPair‑249K dataset of source‑target pairs and show that AffectDelta outperforms six baselines in both affective alignment and content preservation, with ablation studies supporting their design choices.

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arXiv Computer Vision
Sep 3

AffectDelta: Beyond Emotion Labels for Image Editing

AffectDelta is a new image editing framework that moves beyond single emotion labels by modeling edits as transitions between eight‑dimensional emotion distributions. It uses a frozen Emotion Distribution Predictor to estimate the source state and a signed difference vector to encode the desired change, which is then translated into context‑dependent semantic and appearance modifications via a transition encoder and a diffusion backbone. The authors introduce AffectPair‑249K, a dataset of 248,841 source‑target pairs covering both cross‑category and within‑category transitions, and show that AffectDelta outperforms six baselines in affective alignment and content preservation.

By Xingzu Zhan, Lin Gu, Ruogu Fang
diffusionsafety
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arXiv Computer Vision
Oct 12

What We Talk About When We Talk About Emotion Editing

The paper introduces ROI‑ReFT, a method for emotional image editing that leverages neural representations from fMRI encoding models to guide visual changes while preserving content. By combining source visual responses with predicted affective changes, the approach achieves high target‑emotion accuracy (91.75%) and joint success (83.25%) on a curated set of 400 editing requests. The authors plan to release their code and dataset publicly.

By Xingzu Zhan, Lin Gu, Ruogu Fang
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arXiv Computation and Language
Aug 27

Controllable Affective Generation via Latent Vector Steering

The paper introduces EmoVec, a lightweight framework that enables controllable affective generation in large language models by steering latent vectors. EmoVec identifies emotion-specific directions from paired neutral and emotion-conditioned responses using contrastive activation addition, then refines these directions through task-specific debiasing and principal subspace removal. During inference, the refined vectors are injected into the final residual stream with static or scenario-adaptive scaling, allowing continuous control over emotional intensity without updating model weights, and experiments across three LLMs and eight emotions demonstrate improved emotional salience while preserving semantic content, fluency, and coherence.

By Xixian Yong, Siyuan Chang, Yingying Zhang, Xian Wu, Xiao Zhou
llmsbenchmarkssafety
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arXiv AI
Sep 25

Interpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow

The paper introduces a steering‑vector‑based causal attribution framework to study how large vision‑language models (LVLMs) translate visual input into emotional narratives. By creating a specialized dataset, the authors uncover a functional decoupling in the LVLM’s three‑stage Adapt‑Aggregate‑Execute mechanism: visual emotional cues are first aggregated in middle layers via sentiment‑specific attention heads, then translated into narrative generation in deeper layers through emotion‑general pathways. Using these insights, they regulate emotional information routing to strengthen attention flow and amplify semantic activation, achieving significant performance gains on the MER‑UniBench and reducing emotional hallucinations through inference‑time intervention.

By Chengsheng Zhang, Chenghao Sun, Zhining Xie, Xinmei Tian
llmsagentsmultimodalsafety
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