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Found 11 papers, 7 papers with code
Parrot Captions Teach CLIP to Spot Text
Despite CLIP being the foundation model in numerous vision-language applications, the CLIP suffers from a severe text spotting bias.
Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF
The explicit neural radiance field (NeRF) has gained considerable interest for its efficient training and fast inference capabilities, making it a promising direction such as virtual reality and gaming.
Towards Language-guided Interactive 3D Generation: LLMs as Layout Interpreter with Generative Feedback
Generating and editing a 3D scene guided by natural language poses a challenge, primarily due to the complexity of specifying the positional relations and volumetric changes within the 3D space.
SEPT: Towards Scalable and Efficient Visual Pre-Training
To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains.
On the instrumental variable estimation with many weak and invalid instruments
We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity.
Priors in Deep Image Restoration and Enhancement: A Survey
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation.
Exploring the Interactive Guidance for Unified and Effective Image Matting
Although avoiding the extensive labors of trimap annotation, existing methods still suffer from two limitations: (1) For the single image with multiple objects, it is essential to provide extra interaction information to help determining the matting target; (2) For transparent objects, the accurate regression of alpha matte from RGB image is much more difficult compared with the opaque ones.
MINI: Mining Implicit Novel Instances for Few-Shot Object Detection
The offline mining mechanism leverages a self-supervised discriminative model to collaboratively mine implicit novel instances with a trained FSOD network.
Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning
Then we force the model to pull the feature of the distracting video and the feature of the original video closer, so that the model is explicitly restricted to resist the background influence, focusing more on the motion changes.
Self-supervised Temporal Discriminative Learning for Video Representation Learning
Without labelled data for network pretraining, temporal triplet is generated for each anchor video by using segment of the same or different time interval so as to enhance the capacity for temporal feature representation.
Self-supervised learning using consistency regularization of spatio-temporal data augmentation for action recognition
Self-supervised learning has shown great potentials in improving the deep learning model in an unsupervised manner by constructing surrogate supervision signals directly from the unlabeled data.
