arXiv subjects

Jian liu

Publications and source records attributed to Jian liu.

6 recordsLinked to original sources

arXiv2026

Emotion Understanding in Streaming Video with Trajectory-Aware Reliability

Video emotion understanding is commonly studied as an offline classification problem, where the complete video segment is available before prediction. Real-time interaction, however, requires emotion decisions from incomplete and evolving evidence. This paper studies streaming video emotion understanding as a reliability-aware decision process over evolving emotion beliefs. In this setting, a single confident prefix prediction can still be unreliable when the underlying belief trajectory is unstable or repeatedly switches across emotion classes. We propose TRACE, a trajectory-aware reliability framework that forms low-latency emotion beliefs from streaming audio prefixes, estimates reliability from confidence, entropy, stability, and class-switching patterns, and selectively invokes contextual belief reinterpretation with visual, textual, and neighboring-utterance evidence. TRACE keeps stable cases in the low-latency online pathway while allocating stronger multimodal reasoning to uncertain cases that remain ambiguous. Experiments on StreamMER, MELD, and MER2024 show that TRACE improves the accuracy-cost trade-off, retaining most full-context gains while reducing unnecessary contextual reasoning.

cs.MM↗

arXiv2025

OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models

Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However, the high computational cost of processing longer joint audio-video token sequences has become a key bottleneck. Existing token compression methods have not addressed the emerging need to jointly compress multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates model inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive results demonstrate the merits of OmniZip: it achieves a 3.42X inference speedup and a 1.4X memory reduction over other top-performing counterparts, while maintaining the performance of OmniLLMs without training.

cs.CV↗

arXiv2025

STORM: Benchmarking Visual Rating of MLLMs with a Comprehensive Ordinal Regression Dataset

Visual rating is an essential capability of artificial intelligence (AI) for multi-dimensional quantification of visual content, primarily applied in ordinal regression (OR) tasks such as image quality assessment, facial age estimation, and medical image grading. However, current multi-modal large language models (MLLMs) under-perform in such visual rating ability while also suffering the lack of relevant datasets and benchmarks. In this work, we collect and present STORM, a data collection and benchmark for Stimulating Trustworthy Ordinal Regression Ability of MLLMs for universal visual rating. STORM encompasses 14 ordinal regression datasets across five common visual rating domains, comprising 655K image-level pairs and the corresponding carefully curated VQAs. Importantly, we also propose a coarse-to-fine processing pipeline that dynamically considers label candidates and provides interpretable thoughts, providing MLLMs with a general and trustworthy ordinal thinking paradigm. This benchmark aims to evaluate the all-in-one and zero-shot performance of MLLMs in scenarios requiring understanding of the essential common ordinal relationships of rating labels. Extensive experiments demonstrate the effectiveness of our framework and shed light on better fine-tuning strategies. The STORM dataset, benchmark, and pre-trained models are available on the following webpage to support further research in this area. Datasets and codes are released on the project page: https://storm-bench.github.io/.

cs.CV↗

arXiv2025

OrderChain: Towards General Instruct-Tuning for Stimulating the Ordinal Understanding Ability of MLLM

Despite the remarkable progress of multimodal large language models (MLLMs), they continue to face challenges in achieving competitive performance on ordinal regression (OR; a.k.a. ordinal classification). To address this issue, this paper presents OrderChain, a novel and general prompting paradigm that improves the ordinal understanding ability of MLLMs by specificity and commonality modeling. Specifically, our OrderChain consists of a set of task-aware prompts to facilitate the specificity modeling of diverse OR tasks and a new range optimization Chain-of-Thought (RO-CoT), which learns a commonality way of thinking about OR tasks by uniformly decomposing them into multiple small-range optimization subtasks. Further, we propose a category recursive division (CRD) method to generate instruction candidate category prompts to support RO-CoT automatic optimization. Comprehensive experiments show that LLaVA model with our OrderChain improves baseline LLaVA significantly on diverse OR datasets, e.g., from 47.5\% to 93.2\% accuracy on the Adience dataset for age estimation, and from 30.0\% to 85.7\% accuracy on the Diabetic Retinopathy dataset. Notably, LLaVA with our OrderChain also remarkably outperforms state-of-the-art methods by 27% on accuracy and 0.24 on MAE on the Adience dataset. To our best knowledge, our OrderChain is the first work that augments MLLMs for OR tasks, and the effectiveness is witnessed across a spectrum of OR datasets. Project Page: https://order-chain.github.io/.

cs.CV↗

arXiv2025

Verification of Electromagnetic Fully-kinetic Symplectic Particle-in-cell Method in Microinstabilities Simulation of

We present a symplectic electromagnetic fully-kinetic particle-in-cell simulation of microinstabilities in plasma, using parameters from the Cyclone Base Case [Dimits, et al., Physics of Plasmas 7, 969 (2000)]. The results show that the growth rates of unstable modes, including ion temperature gradient (ITG), trapped electron mode (TEM), and kinetic ballooning mode (KBM), are consistent with those obtained from previous gyrokinetic models. Additionally, the \b{eta}-stabilization of the ITG is reproduced. The analysis also reveals that the impact of the ion-electron mass ratio and the numerical speed of light on the growth rate of the most unstable modes is minimal. This suggests that the fully kinetic method offers a potential for reduced computational cost when investigating the physics of drift wave instabilities at relevant space-time scales.

physics.plasm-ph↗

arXiv2023

Text as Image: Learning Transferable Adapter for Multi-Label Classification

Pre-trained vision-language models have notably accelerated progress of open-world concept recognition. Their impressive zero-shot ability has recently been transferred to multi-label image classification via prompt tuning, enabling to discover novel labels in an open-vocabulary manner. However, this paradigm suffers from non-trivial training costs, and becomes computationally prohibitive for a large number of candidate labels. To address this issue, we note that vision-language pre-training aligns images and texts in a unified embedding space, making it potential for an adapter network to identify labels in visual modality while be trained in text modality. To enhance such cross-modal transfer ability, a simple yet effective method termed random perturbation is proposed, which enables the adapter to search for potential visual embeddings by perturbing text embeddings with noise during training, resulting in better performance in visual modality. Furthermore, we introduce an effective approach to employ large language models for multi-label instruction-following text generation. In this way, a fully automated pipeline for visual label recognition is developed without relying on any manual data. Extensive experiments on public benchmarks show the superiority of our method in various multi-label classification tasks.

cs.CV↗