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Found 22 papers, 8 papers with code
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Parkinson's Disease (PD) is the second most common neurodegenerative disorder.
YOLOOC: YOLO-based Open-Class Incremental Object Detection with Novel Class Discovery
Because of its use in practice, open-world object detection (OWOD) has gotten a lot of attention recently.
Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer
We observe that adapter tuning demonstrates superiority over prompt-based methods, even without parameter expansion in each learning session.
Enhancing the General Agent Capabilities of Low-Parameter LLMs through Tuning and Multi-Branch Reasoning
As intelligent agents, LLMs need to have the capabilities of task planning, long-term memory, and the ability to leverage external tools to achieve satisfactory performance.
Saliency Regularization for Self-Training with Partial Annotations
In this paper, we propose saliency regularization (SR) for a novel self-training framework.
Decoupling MaxLogit for Out-of-Distribution Detection
We demonstrate the effectiveness of our logit-based OOD detection methods on CIFAR-10, CIFAR-100 and ImageNet and establish state-of-the-art performance.
A Pilot Study of Relating MYCN-Gene Amplification with Neuroblastoma-Patient CT Scans
Neuroblastoma is one of the most common cancers in infants, and the initial diagnosis of this disease is difficult.
Lightweight Human Pose Estimation Using Heatmap-Weighting Loss
Recent research on human pose estimation exploits complex structures to improve performance on benchmark datasets, ignoring the resource overhead and inference speed when the model is actually deployed.
Aligning Logits Generatively for Principled Black-Box Knowledge Distillation
Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server.
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation
After the autonomous partition of coarse and fine predicates, the model is first trained on the coarse predicates and then learns the fine predicates.
Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation
The real-world data distribution is essentially long-tailed, which poses great challenge to the deep model.
Ranked #14 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain.
Coarse-To-Fine Incremental Few-Shot Learning
Such images form a new training set (i. e., support set) so that the incremental model is hoped to recognize a basenji (i. e., query) as a basenji next time.
Entropy-based Optimization via A* Algorithm for Parking Space Recommendation
This paper addresses the path planning problems for recommending parking spaces, given the difficulties of identifying the most optimal route to vacant parking spaces and the shortest time to leave the parking space.
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Mass segmentation provides effective morphological features which are important for mass diagnosis.
NormFace: L2 Hypersphere Embedding for Face Verification
We show that both strategies, and small variants, consistently improve performance by between 0. 2% to 0. 4% on the LFW dataset based on two models.
Regularizing Face Verification Nets For Pain Intensity Regression
In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification.
Linear Disentangled Representation Learning for Facial Actions
Limited annotated data available for the recognition of facial expression and action units embarrasses the training of deep networks, which can learn disentangled invariant features.
Adversarial Deep Structural Networks for Mammographic Mass Segmentation
Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.
Pose-Selective Max Pooling for Measuring Similarity
In this paper, we deal with two challenges for measuring the similarity of the subject identities in practical video-based face recognition - the variation of the head pose in uncontrolled environments and the computational expense of processing videos.
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data.
Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition
In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data.
