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Found 7 papers, 1 papers with code
Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification
Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision.
Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition.
Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for Vision-Language Tracking
However, current VL trackers have not fully exploited the power of VL learning, as they suffer from limitations such as heavily relying on off-the-shelf backbones for feature extraction, ineffective VL fusion designs, and the absence of VL-related loss functions.
Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methods
However, the recipes of visual pre-training for robot manipulation tasks are yet to be built.
Scene-Aware Label Graph Learning for Multi-Label Image Classification
Multi-label image classification refers to assigning a set of labels for an image.
Two-Stream Transformer for Multi-Label Image Classification
Specifically, in each layer of TSFormer, a cross-modal attention module is developed to aggregate visual features from spatial stream into semantic stream and update label semantics via a residual connection.
Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data
Deep convolution neural network has attracted many attentions in large-scale visual classification task, and achieves significant performance improvement compared to traditional visual analysis methods.
