With the explosion of human-machine interaction, emotion recognition has reignited attention. Previous works focus on improving visual feature fusion and reasoning from multiple image levels. Although it is non-trivial to deduce a person’s emotion by integrating multi-level feature (head, body and context), the emotion recognition results of each level is usually different from one another, which creates inconsistency in the prevailing feature alignment method and decrease recognition performance. In this work, we propose a multi-level image feature refinement method for emotion recognition (CocoER) to mitigate the impact caused by conflicting results from multi-level recognition. First, we leverage cross-level attention to improve visual feature consistency between hierarchically cropped head, body and context windows. Then, vocabulary informed alignment is incorporated into the recognition framework to produce pseudo label and guide hierarchical visual feature refinement. To effectively fuse multi-level feature, we elaborate on a competition process of eliminating irrelevant image level predictions and a coordination process to enhance the feature across all levels. Extensive experiments are executed on two popular datasets, and our method achieves state-of-the-art performance with multi-level interpretation results. Code is available at: https://github.com/bisno/CocoER.

Reasoning-focused large language models (LLMs) are rapidly evolving across various domains, yet their capabilities in handling complex legal problems remains underexplored.In this paper, we introduce Unilaw-R1, a large language model tailored for legal reasoning.With a lightweight 7-billion parameter scale, Unilaw-R1 significantly reduces deployment cost while effectively tackling three core challenges in the legal domain: insufficient legal knowledge, unreliable reasoning logic, and weak business generalization.To address these issues, we first construct Unilaw-R1-Data, a high-quality dataset containing ∼17K distilled and screened chain-of-thought (CoT) samples.Based on this, we adopt a two-stage training strategy combining Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), which significantly boosts the model's performance on complex legal reasoning tasks and supports interpretable decision-making in legal AI applications.To assess legal reasoning ability, we also introduce Unilaw-R1-Eval, a dedicated benchmark designed to evaluate models across single-and multi-choice legal tasks.Unilaw-R1 demonstrates strong results on authoritative benchmarks, outperforming all models of similar scale and achieving performance on par with the much larger DeepSeek-R1-Distill-Qwen-32B (54.9%).Following domainspecific training, it also showed significant gains on LawBench and LexEval, exceeding Qwen-2.5-7B-Instruct(46.6%) by an average margin of 6.6%.Code is available at: https://github.com/Hanscal/Unilaw-R1.

Analogical reasoning is fundamental to human cognition and plays a crucial role across various fields. However, previous studies have primarily focused on single-modal analogical reasoning, often ignoring the benefits of incorporating structural knowledge. Research in cognitive psychology has shown that information from multimodal sources brings more powerful cognitive transfer than single-modality sources. Motivated by the cognitive analogical theory, we address the challenge of multimodal analogical reasoning over multimodal knowledge graphs. Our approach introduces a novel triplet interaction mechanism, where both internal and external interactions between entities and relations are leveraged to enhance the accuracy of multimodal analogical reasoning. Experimental results show that our model outperforms Transformer-based multimodal pre-trained baselines. This improvement underscores the essential role of triplet interactions in integrating multimodal data, leading to more accurate analogical reasoning processes.

Generating emotion-specific talking head videos from audio input is an important and complex challenge for human-machine interaction. However, emotion is highly abstract concept with ambiguous boundaries, and it necessitates disentangled expression parameters to generate emotionally expressive talking head videos. In this work, we present EmoHead to synthesize talking head videos via semantic expression parameters. To predict expression parameter for arbitrary audio input, we apply an audio-expression module that can be specified by an emotion tag. This module aims to enhance correlation from audio input across various emotions. Furthermore, we leverage pre-trained hyperplane to refine facial movements by probing along the vertical direction. Finally, the refined expression parameters regularize neural radiance fields and facilitate the emotion-consistent generation of talking head videos. Experimental results demonstrate that semantic expression parameters lead to better reconstruction quality and controllability.