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Found 14 papers, 4 papers with code
Foundation Model is Efficient Multimodal Multitask Model Selector
This paper investigates an under-explored but important problem: given a collection of pre-trained neural networks, predicting their performance on each multi-modal task without fine-tuning them, such as image recognition, referring, captioning, visual question answering, and text question answering.
Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification
We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages.
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
A simple pixel selection strategy followed with the construction of multi-level contrastive units is introduced to optimize the model for both domain adaptation and active supervised learning.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Exemplar Normalization for Learning Deep Representation
This work investigates a novel dynamic learning-to-normalize (L2N) problem by proposing Exemplar Normalization (EN), which is able to learn different normalization methods for different convolutional layers and image samples of a deep network.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
Switchable Normalization for Learning-to-Normalize Deep Representation
Analyses of SN are also presented to answer the following three questions: (a) Is it useful to allow each normalization layer to select its own normalizer?
Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?
Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.
Towards Understanding Regularization in Batch Normalization
Batch Normalization (BN) improves both convergence and generalization in training neural networks.
Differentiable Learning-to-Normalize via Switchable Normalization
We hope SN will help ease the usage and understand the normalization techniques in deep learning.
Progressively Diffused Networks for Semantic Image Segmentation
This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application.
Geometric Scene Parsing with Hierarchical LSTM
This paper addresses the problem of geometric scene parsing, i. e. simultaneously labeling geometric surfaces (e. g. sky, ground and vertical plane) and determining the interaction relations (e. g. layering, supporting, siding and affinity) between main regions.
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy
In each layer, we construct a dictionary of filters by combining the filters from the lower layer, and iteratively optimize the image representation with a joint discriminative-generative formulation, i. e. minimization of empirical classification error plus regularization of analysis image generation over training images.
Deep Boosting: Layered Feature Mining for General Image Classification
Constructing effective representations is a critical but challenging problem in multimedia understanding.
