Search Results for author:
Found 22 papers, 15 papers with code
RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework
In this paper, we revisit these issues and propose a novel multi-modal RGB-Event attribute recognition task by drawing inspiration from the advantages of event cameras in low-light, high-speed, and low-power consumption.
UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface
Generalist models have achieved remarkable success in both language and vision-language tasks, showcasing the potential of unified modeling.
TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters
By treating model parameters as tokens, we replace all the linear projections in Transformers with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values.
SNN-PAR: Energy Efficient Pedestrian Attribute Recognition via Spiking Neural Networks
To address this issue, in this paper, we propose a Spiking Neural Network (SNN) based framework for energy-efficient attribute recognition.
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework
To address this issue, this paper proposes a new large-scale, cross-domain pedestrian attribute recognition dataset to fill the data gap, termed MSP60K.
GiT: Towards Generalist Vision Transformer through Universal Language Interface
Due to its simple design, this paradigm holds promise for narrowing the architectural gap between vision and language.
Ranked #2 on
Video Captioning
on MSVD-CTN
(using extra training data)
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
Many recent endeavors of GIL focus on extracting the invariant subgraph from the input graph for prediction as a regularization strategy to improve the generalization performance of graph learning.
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems.
Ranked #8 on
3D Object Detection
on nuScenes
DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets
However, due to the sparse characteristics of point clouds, it is non-trivial to apply a standard transformer on sparse points.
Ranked #1 on
3D Object Detection
on waymo cyclist
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Ranked #1 on
3D Object Detection
on SUN-RGBD
Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies
The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide.
RBGNet: Ray-based Grouping for 3D Object Detection
In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays uniformly emitted from cluster centers.
Ranked #15 on
3D Object Detection
on SUN-RGBD val
Full-attention based Neural Architecture Search using Context Auto-regression
Thus, it is appropriate to consider using NAS methods to discover a better self-attention architecture automatically.
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Distributionally robust optimization (DRO) is a widely-used approach to learn models that are robust against distribution shift.
Drug-Target Interaction Prediction with Graph Attention networks
For better learning and interpreting the DTI topological structure and the similarity, it is desirable to have methods specifically for predicting interactions from the graph structure.
Collaborative Visual Navigation
As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques.
Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder
Our model is based on Variational Auto-Encoder (VAE), and its backbone is fulfilled by a Recurrent Neural Network to capture latent temporal structures of time series for both generative model and inference model.
Explicit Shape Encoding for Real-Time Instance Segmentation
In this paper, we propose a novel top-down instance segmentation framework based on explicit shape encoding, named \textbf{ESE-Seg}.
Ranked #3 on
Semantic Contour Prediction
on Sbd val
Visual Rhythm Prediction with Feature-Aligning Network
In this paper, we propose a data-driven visual rhythm prediction method, which overcomes the previous works' deficiency that predictions are made primarily by human-crafted hard rules.
Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems
Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration.
OMNIRank: Risk Quantification for P2P Platforms with Deep Learning
P2P lending presents as an innovative and flexible alternative for conventional lending institutions like banks, where lenders and borrowers directly make transactions and benefit each other without complicated verifications.
