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Found 10 papers, 4 papers with code
TDViT: Temporal Dilated Video Transformer for Dense Video Tasks
Deep video models, for example, 3D CNNs or video transformers, have achieved promising performance on sparse video tasks, i. e., predicting one result per video.
Efficient One-stage Video Object Detection by Exploiting Temporal Consistency
Based on the analysis, we present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD by exploiting the temporal consistency in video frames.
MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection
However, we argue that these memory structures are not efficient or sufficient because of two implied operations: (1) concatenating all features in memory for enhancement, leading to a heavy computational cost; (2) frame-wise memory updating, preventing the memory from capturing more temporal information.
Imbalance Robust Softmax for Deep Embeeding Learning
IR-Softmax can generalise to any softmax and its variants (which are discriminative for open-set problem) by directly setting the weights as their class centers, naturally solving the data imbalance problem.
ParaCNN: Visual Paragraph Generation via Adversarial Twin Contextual CNNs
Furthermore, to enable the ParaCNN to model paragraph comprehensively, we also propose an adversarial twin net training scheme.
Instance Cross Entropy for Deep Metric Learning
Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed.
Analysing Representations of Memory Impairment in a Clinical Notes Classification Model
Despite recent advances in the application of deep neural networks to various kinds of medical data, extracting information from unstructured textual sources remains a challenging task.
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network
Despite the breakthroughs in quality of image enhancement, an end-to-end solution for simultaneous recovery of the finer texture details and sharpness for degraded images with low resolution is still unsolved.
Deep Multi-Task Learning to Recognise Subtle Facial Expressions of Mental States
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
