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Found 22 papers, 5 papers with code
Open World Object Detection: A Survey
Exploring new knowledge is a fundamental human ability that can be mirrored in the development of deep neural networks, especially in the field of object detection.
MultiFuser: Multimodal Fusion Transformer for Enhanced Driver Action Recognition
Driver action recognition, aiming to accurately identify drivers' behaviours, is crucial for enhancing driver-vehicle interactions and ensuring driving safety.
Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles
Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization.
Gland Segmentation Via Dual Encoders and Boundary-Enhanced Attention
Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma.
Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring
Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems.
Zero-Space Cost Fault Tolerance for Transformer-based Language Models on ReRAM
Resistive Random Access Memory (ReRAM) has emerged as a promising platform for deep neural networks (DNNs) due to its support for parallel in-situ matrix-vector multiplication.
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP).
CGCE: A Chinese Generative Chat Evaluation Benchmark for General and Financial Domains
Generative chat models, such as ChatGPT and GPT-4, have revolutionized natural language generation (NLG) by incorporating instructions and human feedback to achieve significant performance improvements.
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible.
A Novel Dataset and a Deep Learning Method for Mitosis Nuclei Segmentation and Classification
In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper.
Material Prediction for Design Automation Using Graph Representation Learning
Successful material selection is critical in designing and manufacturing products for design automation.
A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
Particularly, we develop a hardware-friendly sparse attention operator and a length-aware hardware resource scheduling algorithm.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision
Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses.
Ranked #42 on
3D Human Pose Estimation
on MPI-INF-3DHP
RestainNet: a self-supervised digital re-stainer for stain normalization
In this paper, we formulated stain normalization as a digital re-staining process and proposed a self-supervised learning model, which is called RestainNet.
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization
Our HP can achieve higher sparsity ratio and is more flexible than other sparsity pattern.
Detecting Gender Bias in Transformer-based Models: A Case Study on BERT
In this paper, we propose a novel gender bias detection method by utilizing attention map for transformer-based models.
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit.
Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
Specifically, RT3 integrates two-level optimizations: First, it utilizes an efficient BP as the first-step compression for resource-constrained mobile devices; then, RT3 heuristically generates a shrunken search space based on the first level optimization and searches multiple pattern sets with diverse sparsity for PP via reinforcement learning to support lightweight software reconfiguration, which corresponds to available frequency levels of DVFS (i. e., hardware reconfiguration).
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation
We further accelerate the convergence of the SLR by using quadratic penalties.
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks.
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using sequence-aligned recurrent neural networks (RNNs) or convolution, and it achieved significant improvements for sequence to sequence tasks.
