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Found 14 papers, 5 papers with code
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase.
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity.
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Action Recognition
on Something-Something V2
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage.
Absolute Zero-Shot Learning
Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i. e., training a classifier with zero real data.
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
This heavy-backbone design paradigm is mostly due to the historical legacy when transferring image recognition models to object detection rather than an end-to-end optimized design for object detection.
AnANet: Modeling Association and Alignment for Cross-modal Correlation Classification
Thus researchers study the definition of cross-modal correlation category and construct various classification systems and predictive models.
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function.
Order Matters: Shuffling Sequence Generation for Video Prediction
Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community.
Segmentation Rectification for Video Cutout via One-Class Structured Learning
Comprehensive experiments on both RGB and RGB-D data demonstrate that our simple and effective method significantly outperforms the segmentation propagation methods adopted in the state-of-the-art video cutout systems, and the results also suggest the potential usefulness of our method in image cutout system.
Superpixelizing Binary MRF for Image Labeling Problems
Superpixels have become prevalent in computer vision.
Rigid and Non-rigid Shape Evolutions for Shape Alignment and Recovery in Images
Our idea is to use general model of shape deformation in minimizing active contour energies.
A Compact Linear Programming Relaxation for Binary Sub-modular MRF
We propose a novel compact linear programming (LP) relaxation for binary sub-modular MRF in the context of object segmentation.
Matching-Constrained Active Contours
To this effect, we obtain a mathematical model of interior points to boundary contour such that matching of interior feature points gives contour alignment, and we formulate the matching score as a constraint to active contour model such that the feature matching of maximum score that gives the contour alignment provides the initial feasible solution to the constrained optimization model of segmentation.
