Because of the compositionality of natural language, syntactic structure which contains the information about the relationship between words is a key factor for semantic understanding.
Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT).
Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations, exemplified by systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc.
By adding linear image domain error analysis, the noise is reduced after under-sampled and DFT processing, and the anti-interference ability of the algorithm is enhanced.
In this study, we propose a novel framework, CorrTalk, which effectively establishes the temporal correlation between hierarchical speech features and facial activities of different intensities across distinct regions.
Finally, the experimental results on real-world fetal brain MRI stacks demonstrate the state-of-the-art performance of our method.
To reduce the data-dependent redundancy, we devise a dynamic shuffle module to generate data-dependent permutation matrices for shuffling.
Therefore the pruning strategy can gradually prune the network and automatically determine the appropriate pruning rates for each layer.
Although PTMs shed new light on artificial general intelligence, they are constructed with general tasks in mind, and thus, their efficacy for specific tasks can be further improved.
Self-attention mechanism is applied within windows for capturing temporal important information locally in a fine-grained way.
Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses.
Ranked #1 on Speech Emotion Recognition on LSSED
The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances.
Ranked #4 on 3D Instance Segmentation on ScanNet(v2)
In this work, we propose a novel Context Sensing Attention Network (CSA-Net), which improves both the frame feature extraction and temporal aggregation steps.
Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.
Ranked #1 on Emotion Recognition in Conversation on CPED
In this paper, we devise a new training method, low-rank projection with energy transfer (LRPET), that trains low-rank compressed networks from scratch and achieves competitive performance.
Comprehensive experiments show that WE outperforms the other reactivation methods and plug-in training methods with typical convolutional neural networks, especially lightweight networks.
With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions.
Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI).
Ranked #3 on Speech Emotion Recognition on LSSED
However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning.
Visual tracking is challenging due to image variations caused by various factors, such as object deformation, scale change, illumination change and occlusion.
In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space.
With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution.
However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA.
However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the flexibility of models to infer various scales of details for high resolution (HR) output.
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Ranked #7 on Image Dehazing on RS-Haze