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Found 24 papers, 15 papers with code
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
A Multi-Scale Spatial-Temporal Network for Wireless Video Transmission
Deep joint source-channel coding (DeepJSCC) has shown promise in wireless transmission of text, speech, and images within the realm of semantic communication.
Optical Semantic Communication through Multimode Fiber: From Symbol Transmission to Sentiment Analysis
By encoding semantically similar symbols to adjacent frequencies, the system's noise tolerance is effectively improved, facilitating accurate sentiment analysis.
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation
Transformer-based deep models for single image super-resolution (SISR) have greatly improved the performance of lightweight SISR tasks in recent years.
Diff-Shadow: Global-guided Diffusion Model for Shadow Removal
Specifically, we propose a parallel UNets architecture: 1) the local branch performs the patch-based noise estimation in the diffusion process, and 2) the global branch recovers the low-resolution shadow-free images.
E5-V: Universal Embeddings with Multimodal Large Language Models
We propose a single modality training approach for E5-V, where the model is trained exclusively on text pairs.
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models.
Improving Bracket Image Restoration and Enhancement with Flow-guided Alignment and Enhanced Feature Aggregation
In this paper, we address the Bracket Image Restoration and Enhancement (BracketIRE) task using a novel framework, which requires restoring a high-quality high dynamic range (HDR) image from a sequence of noisy, blurred, and low dynamic range (LDR) multi-exposure RAW inputs.
Improving Domain Adaptation through Extended-Text Reading Comprehension
To enhance the domain-specific capabilities of large language models, continued pre-training on a domain-specific corpus is a prevalent method.
MEFLUT: Unsupervised 1D Lookup Tables for Multi-exposure Image Fusion
In this paper, we introduce a new approach for high-quality multi-exposure image fusion (MEF).
FrFT based estimation of linear and nonlinear impairments using Vision Transformer
To comprehensively assess optical fiber communication system conditions, it is essential to implement joint estimation of the following four critical impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD).
Scaling Sentence Embeddings with Large Language Models
We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.
Ranked #1 on
Semantic Textual Similarity
on STS12
SAM-IQA: Can Segment Anything Boost Image Quality Assessment?
Our experiments confirm the powerful feature extraction capabilities of Segment Anything and highlight the value of combining spatial-domain and frequency-domain features in IQA tasks.
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
Realistic Noise Synthesis with Diffusion Models
Deep denoising models require extensive real-world training data, which is challenging to acquire.
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution
To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance.
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs
To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning.
Pruning Pre-trained Language Models Without Fine-Tuning
These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Exploiting Global and Local Hierarchies for Hierarchical Text Classification
Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.
Multi Label Text Classification
Multi-Label Text Classification
+1
PromptBERT: Improving BERT Sentence Embeddings with Prompts
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation.
Improving Non-autoregressive Generation with Mixup Training
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
In this paper, we present an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet.
Ranked #5 on
Face Alignment
on WFW (Extra Data)
LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification
In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.
