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Found 23 papers, 7 papers with code
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #26 on
Code Generation
on MBPP
Effective Long-Context Scaling of Foundation Models
We also examine the impact of various design choices in the pretraining process, including the data mix and the training curriculum of sequence lengths -- our ablation experiments suggest that having abundant long texts in the pretrain dataset is not the key to achieving strong performance, and we empirically verify that long context continual pretraining is more efficient and similarly effective compared to pretraining from scratch with long sequences.
EDFace-Celeb-1M: Benchmarking Face Hallucination with a Million-scale Dataset
It is thus unclear how these algorithms perform on public face hallucination datasets.
Ranked #1 on
Image Super-Resolution
on WLFW
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.
The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab.
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli.
Referring Expression Generation and Comprehension via Attributes
In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension.
Verification Code Recognition Based on Active and Deep Learning
However, the advantages of convolutional neural networks depend on the data used by the training classifier, particularly the size of the training set.
Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision
We show that the use of contrastive attention and alignment module allows the model to learn rich identification and localization information using only a small amount of location annotations, resulting in state-of-the-art performance in NIH chest X-ray dataset.
Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia
Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality.
Coarse to Fine: Video Retrieval before Moment Localization
The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.
CLIP2TV: Align, Match and Distill for Video-Text Retrieval
Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.
Ranked #11 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
A Sparse Polynomial Chaos Expansion-Based Method for Probabilistic Transient Stability Assessment and Enhancement
This paper proposes an adaptive sparse polynomial chaos expansion(PCE)-based method to quantify the impacts of uncertainties on critical clearing time (CCT) that is an important index in transient stability analysis.
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks.
CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding
Whether heuristic or learned, these methods ignore instance-level visual attributes of objects, and as a result may produce visually less coherent scenes.
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data.
Text-guided 3D Human Generation from 2D Collections
To address this \texttt{T3H} task, we propose Compositional Cross-modal Human (CCH).
Ranked #1 on
Text-to-3D-Human Generation
on SHHQ
Perfect Alignment May be Poisonous to Graph Contrastive Learning
Graph Contrastive Learning (GCL) aims to learn node representations by aligning positive pairs and separating negative ones.
Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method
Thus, we propose GraphRank, a simple yet efficient graph contrastive learning method that addresses the problem of false negative samples by redefining the concept of negative samples to a certain extent, thereby avoiding the issue of false negative samples.
Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model
This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation.
Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoning
This paper introduces Scene-LLM, a 3D-visual-language model that enhances embodied agents' abilities in interactive 3D indoor environments by integrating the reasoning strengths of Large Language Models (LLMs).
A Survey of Neural Network Robustness Assessment in Image Recognition
We investigate the perturbation metrics and range representations used to measure the degree of perturbations on images, as well as the robustness metrics specifically for the robustness conditions of classification models.
