Search Results for author:
Found 16 papers, 5 papers with code
Concise and Organized Perception Facilitates Large Language Models for Deductive Reasoning
By perceiving concise and organized proofs, the deductive reasoning abilities of LLMs can be better elicited, and the risk of acquiring errors caused by excessive reasoning stages is mitigated.
Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation
Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information.
Ranked #57 on
Semantic Segmentation
on NYU Depth v2
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods.
Self-Learning Symmetric Multi-view Probabilistic Clustering
Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution.
Towards Accurate Post-Training Quantization for Vision Transformer
We find the main reasons lie in (1) the existing calibration metric is inaccurate in measuring the quantization influence for extremely low-bit representation, and (2) the existing quantization paradigm is unfriendly to the power-law distribution of Softmax.
Compressing Models with Few Samples: Mimicking then Replacing
Hence, previous methods optimize the compressed model layer-by-layer and try to make every layer have the same outputs as the corresponding layer in the teacher model, which is cumbersome.
MPC: Multi-View Probabilistic Clustering
Despite the promising progress having been made, the two challenges of multi-view clustering (MVC) are still waiting for better solutions: i) Most existing methods are either not qualified or require additional steps for incomplete multi-view clustering and ii) noise or outliers might significantly degrade the overall clustering performance.
Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection
In this work, we develop a novel detection algorithm based on a well proven object detection framework in the computer vision field.
Multi-objective optimization and explanation for stroke risk assessment in Shanxi province
The experimental results showed that the QIDNN model with 7 interactive features achieve the state-of-art accuracy $83. 25\%$.
Analysis and classification of main risk factors causing stroke in Shanxi Province
Evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China.
Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps
"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision.
Ranked #1 on
Face Detection
on FDDB
(Accuracy metric)
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model
In this paper, we provide an explicit convex optimization example where training the BNNs with the traditionally adaptive optimization methods still faces the risk of non-convergence, and identify that constraining the range of gradients is critical for optimizing the deep binary model to avoid highly suboptimal solutions.
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Through-Estimator (STE) still dominates quantized networks.
Ranked #930 on
Image Classification
on ImageNet
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds.
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
Firstly, we employ weights with duplicated channels for the weight-intensive layers to reduce the model size.
Ranked #1 on
Face Detection
on WIDER Face
(GFLOPs metric)
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability).
Ranked #182 on
Image Classification
on CIFAR-10
(using extra training data)
