Search Results for author:
Found 21 papers, 14 papers with code
MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
Problems include: (1) How to systematically structure and evaluate the complicated multimodal tasks; (2) How to design evaluation metrics that work well across question and answer types; and (3) How to give model insights beyond a simple performance ranking.
Two-stage Denoising Diffusion Model for Source Localization in Graph Inverse Problems
However, the inherent intricacy and uncertainty in information dissemination pose significant challenges, and the ill-posed nature of the source localization problem further exacerbates these challenges.
InceptionNeXt: When Inception Meets ConvNeXt
Inspired by the long-range modeling ability of ViTs, large-kernel convolutions are widely studied and adopted recently to enlarge the receptive field and improve model performance, like the remarkable work ConvNeXt which employs 7x7 depthwise convolution.
Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering
Introducing prior auxiliary information from the knowledge graph (KG) to assist the user-item graph can improve the comprehensive performance of the recommender system.
MetaFormer Baselines for Vision
By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an accuracy of 85. 5% at 224x224 resolution, under normal supervised training without external data or distillation.
Ranked #2 on
Domain Generalization
on ImageNet-C
(using extra training data)
ODformer: Spatial-Temporal Transformers for Long Sequence Origin-Destination Matrix Forecasting Against Cross Application Scenario
Origin-Destination (OD) matrices record directional flow data between pairs of OD regions.
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
The LIDC-IDRI database is the most popular benchmark for lung cancer prediction.
Inception Transformer
Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information.
Mugs: A Multi-Granular Self-Supervised Learning Framework
It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability.
Ranked #8 on
Self-Supervised Image Classification
on ImageNet
Contrastive Learning
Self-Supervised Image Classification
+3
LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation
Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans.
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches.
MetaFormer Is Actually What You Need for Vision
Based on this observation, we hypothesize that the general architecture of the Transformers, instead of the specific token mixer module, is more essential to the model's performance.
Ranked #9 on
Semantic Segmentation
on DensePASS
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation
Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.
LV-BERT: Exploiting Layer Variety for BERT
In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order.
Refiner: Refining Self-attention for Vision Transformers
Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks compared with CNNs.
Ranked #169 on
Image Classification
on ImageNet
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study.
Ranked #386 on
Image Classification
on ImageNet
ConvBERT: Improving BERT with Span-based Dynamic Convolution
The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning.
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning
Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set.
Ranked #29 on
Reading Comprehension
on ReClor
Heterogeneous Graph Learning for Visual Commonsense Reasoning
Our HGL consists of a primal vision-to-answer heterogeneous graph (VAHG) module and a dual question-to-answer heterogeneous graph (QAHG) module to interactively refine reasoning paths for semantic agreement.
Knowledge-Embedded Routing Network for Scene Graph Generation
More specifically, we show that the statistical correlations between objects appearing in images and their relationships, can be explicitly represented by a structured knowledge graph, and a routing mechanism is learned to propagate messages through the graph to explore their interactions.
Ranked #7 on
Scene Graph Generation
on Visual Genome
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects.
