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Found 15 papers, 6 papers with code
UniFL: Improve Stable Diffusion via Unified Feedback Learning
Diffusion models have revolutionized the field of image generation, leading to the proliferation of high-quality models and diverse downstream applications.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
Generating Visual Scenes from Touch
An emerging line of work has sought to generate plausible imagery from touch.
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Specifically, we propose a Stage-wise Hybrid Matching strategy that combines the one-to-many assignment and one-to-one assignment strategies to improve the training efficiency of the first stage and thus provide high-quality pseudo labels for the training of the second stage.
On time-consistent equilibrium stopping under aggregation of diverse discount rates
When the sufficient condition of the attitude function is violated, we can illustrate by various examples that the characterization of the optimal equilibrium may differ significantly from some existing results for an individual agent.
A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition
In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN).
Touch and Go: Learning from Human-Collected Vision and Touch
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
Text-to-Table: A New Way of Information Extraction
We first employ a seq2seq model fine-tuned from a pre-trained language model to perform the task.
Modeling Voting for System Combination in Machine Translation
System combination is an important technique for combining the hypotheses of different machine translation systems to improve translation performance.
Neural Machine Translation with Explicit Phrase Alignment
The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints.
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge.
Improving the Transformer Translation Model with Document-Level Context
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
THUMT: An Open Source Toolkit for Neural Machine Translation
This paper introduces THUMT, an open-source toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University.
