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Found 10 papers, 4 papers with code
The Devil is in the Details: A Deep Dive into the Rabbit Hole of Data Filtering
DataComp is a new benchmark dedicated to evaluating different methods for data filtering.
Learning Dynamic Query Combinations for Transformer-based Object Detection and Segmentation
Transformer-based detection and segmentation methods use a list of learned detection queries to retrieve information from the transformer network and learn to predict the location and category of one specific object from each query.
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data.
Exploring the Role of Audio in Video Captioning
Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Boosted Dynamic Neural Networks
To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data.
Dropout Prediction Uncertainty Estimation Using Neuron Activation Strength
Dropout has been commonly used to quantify prediction uncertainty, i. e, the variations of model predictions on a given input example.
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization
Post-training quantization methods use a set of calibration data to compute quantization ranges for network parameters and activations.
High-Resolution Deep Image Matting
Image matting is a key technique for image and video editing and composition.
FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation
In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinical environment.
Any-Precision Deep Neural Networks
When all layers are set to low-bits, we show that the model achieved accuracy comparable to dedicated models trained at the same precision.
