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Found 14 papers, 7 papers with code
Open-World Dynamic Prompt and Continual Visual Representation Learning
Our DPaRL learns to generate dynamic prompts for inference, as opposed to relying on a static prompt pool in previous PCL methods.
B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory
Recent hybrid architectures have combined eidetic and fading memory, but with limitations that do not allow the designer or the learning process to seamlessly modulate the two, nor to extend the eidetic memory span.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training
Based on the observation, we present a method, called Ensemble Logit Difference Inhibition (ELODI), to train a classification system that achieves paragon performance in both error rate and NFR, at the inference cost of a single model.
Towards Universal Backward-Compatible Representation Learning
The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features.
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval
In contrast, hot-refresh model upgrades deploy the new model immediately and then gradually improve the retrieval accuracy by backfilling the gallery on-the-fly.
Non-Inherent Feature Compatible Learning
In this work, we introduce an approach for feature compatible learning without inheriting old classifier and training data, i. e., Non-Inherent Feature Compatible Learning.
Towards Backward-Compatible Representation Learning
Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods.
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association
Person re-identification is an important task that requires learning discriminative visual features for distinguishing different person identities.
Ranked #24 on
Text based Person Retrieval
on CUHK-PEDES
End-to-End Deep Kronecker-Product Matching for Person Re-identification
Person re-identification aims to robustly measure similarities between person images.
Deep Group-shuffling Random Walk for Person Re-identification
Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images.
Person Re-identification with Deep Similarity-Guided Graph Neural Network
However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs.
Ranked #1 on
Person Re-Identification
on CUHK03
Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals
Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation.
