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Found 30 papers, 18 papers with code
Compress Polyphone Pronunciation Prediction Model with Shared Labels
In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection.
SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation
The Segment Anything model (SAM) has shown a generalized ability to group image pixels into patches, but applying it to semantic-aware segmentation still faces major challenges.
A Survey on Failure Analysis and Fault Injection in AI Systems
Moreover, this survey contributes to the field by providing a framework for fault diagnosis, evaluating the state-of-the-art in FI, and identifying areas for improvement in FI techniques to enhance the resilience of AI systems.
ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving
Since the increasing popularity of large language model (LLM) backend systems, it is common and necessary to deploy stable serverless serving of LLM on multi-GPU clusters with autoscaling.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models.
CPR++: Object Localization via Single Coarse Point Supervision
CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point.
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
An obvious obstacle lies in the absence of a specific benchmark to evaluate the effectiveness of MLLMs on aesthetic perception.
Semantic-aware SAM for Point-Prompted Instance Segmentation
In this paper, we introduce a cost-effective category-specific segmenter using SAM.
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects).
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services
Besides, experiments with different training datasets, different optimization objectives and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.
Improving Span-based Aspect Sentiment Triplet Extraction with Abundant Syntax Knowledge
Aspect sentiment triplet extraction is a subtask of aspect based sentiment analysis, which has attracted considerable attention in recent years.
Aspect-Based Sentiment Analysis
Aspect Sentiment Triplet Extraction
+1
Point-to-Box Network for Accurate Object Detection via Single Point Supervision
However, the performance gap between point supervised object detection (PSOD) and bounding box supervised detection remains large.
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels.
Object Localization under Single Coarse Point Supervision
In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Value function is the central notion of Reinforcement Learning (RL).
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.
Noise against noise: stochastic label noise helps combat inherent label noise
The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates.
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption. The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN).
Ranked #45 on
Image Classification
on Clothing1M
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping.
Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network
In this paper, we design an integrated simulation-prediction system which estimates the Origin-Destination (OD) matrix of a road network using only flow rate information and predicts the behavior of the road network in different simulation scenarios.
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work.
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
Most recent efforts have been devoted to defending noisy labels by discarding noisy samples from the training set or assigning weights to training samples, where the weight associated with a noisy sample is expected to be small.
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates.
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels.
Ranked #39 on
Image Classification
on mini WebVision 1.0
Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder
In Variational Auto-Encoder (VAE), the default choice of reconstruction loss function between the decoded sample and the input is the squared $L_2$.
Understanding Convolution for Semantic Segmentation
This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.
Ranked #20 on
Semantic Segmentation
on PASCAL VOC 2012 test
