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Found 10 papers, 4 papers with code
Morph: A Motion-free Physics Optimization Framework for Human Motion Generation
Human motion generation plays a vital role in applications such as digital humans and humanoid robot control.
M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
Third, M$^3$GPT learns to model the connections and synergies among various motion-relevant tasks.
Pre-Trained Vision-Language Models as Partial Annotators
Pre-trained vision-language models learn massive data to model unified representations of images and natural languages, which can be widely applied to downstream machine learning tasks.
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding
To the best of our knowledge, our method is the first INR-based codec comparable with Hyperprior in both decoding speed and quality while maintaining low complexity.
CLIP-based Synergistic Knowledge Transfer for Text-based Person Retrieval
Text-based Person Retrieval (TPR) aims to retrieve the target person images given a textual query.
Lifelong Person Re-Identification via Knowledge Refreshing and Consolidation
Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently.
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid.
Pose-guided Visible Part Matching for Occluded Person ReID
Occluded person re-identification is a challenging task as the appearance varies substantially with various obstacles, especially in the crowd scenario.
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification
In particular, we formulate a Deep Reinforcement Active Learning (DRAL) method to guide an agent (a model in a reinforcement learning process) in selecting training samples on-the-fly by a human user/annotator.
Stepwise Metric Promotion for Unsupervised Video Person Re-Identification
The intensive annotation cost and the rich but unlabeled data contained in videos motivate us to propose an unsupervised video-based person re-identification (re-ID) method.
Ranked #7 on
Person Re-Identification
on PRID2011
