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Found 16 papers, 1 papers with code
Action Detection via an Image Diffusion Process
Action detection aims to localize the starting and ending points of action instances in untrimmed videos, and predict the classes of those instances.
LLMs are Good Sign Language Translators
Sign Language Translation (SLT) is a challenging task that aims to translate sign videos into spoken language.
AI-Generated Content (AIGC) for Various Data Modalities: A Survey
Due to its wide range of applications and the demonstrated potential of recent works, AIGC developments have been attracting lots of attention recently, and AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape (as voxels, point clouds, meshes, and neural implicit fields), 3D scene, 3D human avatar (body and head), 3D motion, and audio -- each presenting different characteristics and challenges.
Distribution-Aligned Diffusion for Human Mesh Recovery
Inspired by their capability, we explore a diffusion-based approach for human mesh recovery, and propose a Human Mesh Diffusion (HMDiff) framework which frames mesh recovery as a reverse diffusion process.
Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, where both the inputs and masked ``ground truth" targets can potentially be unreliable in this case.
Progressive Channel-Shrinking Network
Currently, salience-based channel pruning makes continuous breakthroughs in network compression.
GradMDM: Adversarial Attack on Dynamic Networks
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input.
System-status-aware Adaptive Network for Online Streaming Video Understanding
Recent years have witnessed great progress in deep neural networks for real-time applications.
Unified Pose Sequence Modeling
We propose a Unified Pose Sequence Modeling approach to unify heterogeneous human behavior understanding tasks based on pose data, e. g., action recognition, 3D pose estimation and 3D early action prediction.
A Characteristic Function-Based Method for Bottom-Up Human Pose Estimation
Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap.
DiffPose: Toward More Reliable 3D Pose Estimation
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy.
Ranked #11 on
3D Human Pose Estimation
on MPI-INF-3DHP
Improving the Reliability for Confidence Estimation
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.
Heatmap Distribution Matching for Human Pose Estimation
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition
We design a novel Dynamic Spatio-Temporal Specialization (DSTS) module, which consists of specialized neurons that are only activated for a subset of samples that are highly similar.
ERA: Expert Retrieval and Assembly for Early Action Prediction
Early action prediction aims to successfully predict the class label of an action before it is completely performed.
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation
In this work, we propose a two-step post-processing procedure, Split and Expand, that directly improves the conversion of segmentation maps to instances.
