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Found 19 papers, 10 papers with code
Debiased Novel Category Discovering and Localization
In recent years, object detection in deep learning has experienced rapid development.
A Survey for Foundation Models in Autonomous Driving
The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD).
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment strategies.
Open-Set Image Tagging with Multi-Grained Text Supervision
Specifically, for predefined commonly used tag categories, RAM++ showcases 10. 2 mAP and 15. 4 mAP enhancements over CLIP on OpenImages and ImageNet.
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning
Instead of "opening set", i. e., modeling OOD distribution, Prototype Fission "closes set" and makes it hard for OOD samples to fit in sub-class latent space.
CLIP Brings Better Features to Visual Aesthetics Learners
Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss.
MAM Faster R-CNN: Improved Faster R-CNN based on Malformed Attention Module for object detection on X-ray security inspection
First, in order to expand the receptive field of the feature map and effectively extract the regional features of the target object with shape distortion in the feature map, we propose the Malformed Attention Module (MAM).
Recognize Anything: A Strong Image Tagging Model
We are releasing the RAM at \url{https://recognize-anything. github. io/} to foster the advancements of large models in computer vision.
Box-Level Active Detection
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection.
Knowledge Distillation from Single to Multi Labels: an Empirical Study
Knowledge distillation (KD) has been extensively studied in single-label image classification.
Tag2Text: Guiding Vision-Language Model via Image Tagging
This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features.
Mosaic Representation Learning for Self-supervised Visual Pre-training
To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations.
IDEA: Increasing Text Diversity via Online Multi-Label Recognition for Vision-Language Pre-training
However, the image-text pairs co-occurrent on the Internet typically lack explicit alignment information, which is suboptimal for VLP.
Mixed Sample Augmentation for Online Distillation
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks.
Personalized Image Aesthetics Assessment with Rich Attributes
To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31, 220 images with annotations by 438 subjects.
Self-Distillation from the Last Mini-Batch for Consistency Regularization
Afterwards, the former half mini-batch distills on-the-fly soft targets generated in the previous iteration.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning
This paper investigates the feasibility of federated representation learning under the constraints of communication cost and privacy protection.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals
It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning.
