Search Results for author:
Found 29 papers, 12 papers with code
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
Derived from the theoretical framework of GIL, we introduce two novel objective functions: the invariant prototype matching loss to ensure samples are matched to the correct class prototypes, and the prototype separation loss to increase the distinction between prototypes of different classes in the hyperspherical space.
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods.
Ranked #10 on
Node Classification
on Actor
Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control
This technique leverages semantic features to control the representation of LLM's intermediate hidden states, enabling the model to meet specific requirements such as increased honesty or heightened safety awareness.
SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs
Knowledge graphs (KGs) play a pivotal role in knowledge-intensive tasks across specialized domains, where the acquisition of precise and dependable knowledge is crucial.
From Yes-Men to Truth-Tellers: Addressing Sycophancy in Large Language Models with Pinpoint Tuning
Recent works propose to employ supervised fine-tuning (SFT) to mitigate the sycophancy issue, while it typically leads to the degeneration of LLMs' general capability.
Interpreting and Improving Large Language Models in Arithmetic Calculation
In this work, we delve into uncovering a specific mechanism by which LLMs execute calculations.
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
To bridge the gap, in this work, we present a Unified Benchmark for unsupervised Graph-level OOD and anomaly Detection (our method), a comprehensive evaluation framework that unifies GLAD and GLOD under the concept of generalized graph-level OOD detection.
Graph rewiring and preprocessing for graph neural networks based on effective resistance
Graph neural networks (GNNs) are powerful models for processing graph data and have demonstrated state-of-the-art performance on many downstream tasks.
Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models
In this work, we propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs.
Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs).
Enhanced Motion-Text Alignment for Image-to-Video Transfer Learning
This limitation hinders the potential of language supervision emphasized in CLIP and restricts the learning of temporal features as the text encoder has demonstrated limited proficiency in motion understanding.
Increasing Electric Vehicles Utilization in Transit Fleets using Learning, Predictions, Optimization, and Automation
These predictions are then used by a Predictive Block Assignment module to maximize the BEB fleet utilization.
A Block-Based Adaptive Decoupling Framework for Graph Neural Networks
Graph neural networks (GNNs) with feature propagation have demonstrated their power in handling unstructured data.
ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer
The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper.
Meta Convolutional Neural Networks for Single Domain Generalization
Taking meta features as reference, we propose compositional operations to eliminate irrelevant features of local convolutional features by an addressing process and then to reformulate the convolutional feature maps as a composition of related meta features.
Ranked #5 on
Single-Source Domain Generalization
on Digits-five
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering algorithms.
Unleash the Potential of Adaptation Models via Dynamic Domain Labels
In this paper, we propose an embarrassing simple yet highly effective adversarial domain adaptation (ADA) method for effectively training models for alignment.
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
The inheritance part is learned with a similarity loss to transfer the existing learned knowledge from the teacher model to the student model, while the exploration part is encouraged to learn representations different from the inherited ones with a dis-similarity loss.
Partial Person Re-Identification With Part-Part Correspondence Learning
Driven by the success of deep learning, the last decade has seen rapid advances in person re-identification (re-ID).
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Specifically, we introduce Gait recognition as an auxiliary task to drive the Image ReID model to learn cloth-agnostic representations by leveraging personal unique and cloth-independent gait information, we name this framework as GI-ReID.
Ranked #5 on
Person Re-Identification
on VC-Clothes
Cloth-Changing Person Re-Identification
Computational Efficiency
+1
Dense Interaction Learning for Video-based Person Re-identification
The CNN encoder is responsible for efficiently extracting discriminative spatial features while the DI decoder is designed to densely model spatial-temporal inherent interaction across frames.
Ranked #1 on
Person Re-Identification
on DukeMTMC-reID
3D Local Convolutional Neural Networks for Gait Recognition
Second, different body parts possess different scales, and even the same part in different frames can appear at different locations and scales.
Ranked #4 on
Gait Recognition
on OUMVLP
Video Object Segmentation With Dynamic Memory Networks and Adaptive Object Alignment
In this paper, we propose a novel solution for object-matching based semi-supervised video object segmentation, where the target object masks in the first frame are provided.
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
The topology of the adjacency graph is a key factor for modeling the correlations of the input skeletons.
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
We investigate the problem of predicting driver behavior in parking lots, an environment which is less structured than typical road networks and features complex, interactive maneuvers in a compact space.
Transform-Invariant Convolutional Neural Networks for Image Classification and Search
In this paper, we propose randomly transforming (rotation, scale, and translation) feature maps of CNNs during the training stage.
Continuous Dropout
On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout.
Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks.
Quantization Networks
The proposed quantization function can be learned in a lossless and end-to-end manner and works for any weights and activations of neural networks in a simple and uniform way.
