Heterogeneous clusters with diverse devices mitigate computational and memory burdens in large language model (LLM) training, yet their inherent resource heterogeneity, characterized by divergent computation, memory, and bandwidth capabilities, renders manual parallelization strategy optimization both challenging and time-intensive. Automatic parallelization is critical for scaling complex workloads across heterogeneous architectures. However, previous methodologies face significant inefficiencies. First, insufficient pruning of the parameter initialization space results in impractically large search spaces. Second, the prevailing automatic parallel search strategies exhibit suboptimal performance in load balancing and resource constraint adaptation. Third, dynamic parallel strategy tuning incurs substantial overhead due to redundant latency calculations for operators with unchanged configurations, leading to unnecessary computational costs. Therefore, insufficient search space pruning, suboptimal load/resource adaptation, and redundant latency computation are identified as the major bottlenecks in our research. To address these challenges, we propose AutoHAAP (Automated Heterogeneity-Aware Asymmetric Partitioning), a novel framework incorporating three core innovations: (1) memory-aware initialization to drastically reduce viable search spaces; (2) a heterogeneity-aware load-balancing estimator that guides resource-efficient configuration search; and (3) state caching mechanisms eliminating redundant latency calculations. Evaluations across GPT3 and Llama3 models of varying scales on both homogeneous and heterogeneous clusters demonstrate that AutoHAAP achieves$\mathbf{0. 6 8}-\mathbf{9 8} \times$search efficiency gains,$\mathbf{6. 5 7 \%} \boldsymbol{-} \mathbf{1 0 6. 9 \%} \boldsymbol{\times}$throughput improvements in homogeneous environments, and$\mathbf{1 0. 1 \%} \boldsymbol{-} 22.28 \% \times$throughput enhancements in heterogeneous setups. These results validate AutoHAAP's effectiveness in distributed LLM training on diverse hardware.

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.

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.

Abstract This paper aims to detect road regions based on a two‐scaled deep neural network. The information from different scales is helpful to boost the performance of deep learning models, and it is also a widely used strategy in various computer vision applications. In the two‐scaled model, skip‐architecture and fully convolutional layers are used to fuse the low‐level details and high‐level semantic information. It enables to detect the road areas by multi‐scale feature maps from different reception fields. To avoid the redundancy of scale information and the loss of features caused by the pooling layer, the feature maps before the first pooling layer are adopted in our model. By the convolutional kernels, our model can balance the information of two scales automatically. The loss function is also improved, in which the intersection over union (IoU) term is taken into account to guide the model to learn more features on the whole road regions. Comprehensive experiments on three benchmark datasets demonstrate that this approach can reach state‐of‐the‐art performance.