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Found 14 papers, 3 papers with code
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input
We observe the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue.
Persistent Test-time Adaptation in Episodic Testing Scenarios
Current test-time adaptation (TTA) approaches aim to adapt to environments that change continuously.
EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension
Instead of relying on large amounts of data and/or scaling up network parameters, we introduce a highly effective retrieval-augmented image captioning method that prompts LLMs with object names retrieved from External Visual--name memory (EVCap).
Partition-and-Debias: Agnostic Biases Mitigation via A Mixture of Biases-Specific Experts
However, most of these methods implicitly assume that a given image contains only one type of known or unknown bias, failing to consider the complexities of real-world biases.
Revisiting Latent Space of GAN Inversion for Real Image Editing
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem.
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data
Label-noise or curated unlabeled data is used to compensate for the assumption of clean labeled data in training the conditional generative adversarial network; however, satisfying such an extended assumption is occasionally laborious or impractical.
Balancing Reconstruction and Editing Quality of GAN Inversion for Real Image Editing with StyleGAN Prior Latent Space
The exploration of the latent space in StyleGANs and GAN inversion exemplify impressive real-world image editing, yet the trade-off between reconstruction quality and editing quality remains an open problem.
A-CAP: Anticipation Captioning with Commonsense Knowledge
Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time.
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning
Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.
OSSGAN: Open-Set Semi-Supervised Image Generation
We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set.
NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External Knowledge
We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset.
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression
We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression.
Two-Stream FCNs to Balance Content and Style for Style Transfer
The semantic content feature and the style representation feature are then concatenated adaptively and fed into the decoder to generate style-transferred (stylized) images.
Visual-Relation Conscious Image Generation from Structured-Text
We also use individual relation separately to predict from the initial bounding-boxes relation-units for all the relations in the input text.
