Search Results for author:
Found 25 papers, 14 papers with code
Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts
Wireless signal-based human sensing technologies, such as WiFi, millimeter-wave (mmWave) radar, and Radio Frequency Identification (RFID), enable the detection and interpretation of human presence, posture, and activities, thereby providing critical support for applications in public security, healthcare, and smart environments.
HoGS: Unified Near and Far Object Reconstruction via Homogeneous Gaussian Splatting
Novel view synthesis has demonstrated impressive progress recently, with 3D Gaussian splatting (3DGS) offering efficient training time and photorealistic real-time rendering.
IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Agents
These findings establish Iterative Refinement as an effective new strategy for LLM-driven ML automation and position IMPROVE as an accessible solution for building high-quality computer vision models without requiring ML expertise.
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs
Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited context windows.
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation
We also introduce a relation regularization method to further disentangle image transformation features from irrelevant contents in exemplar images.
Ascend HiFloat8 Format for Deep Learning
This preliminary white paper proposes a novel 8-bit floating-point data format HiFloat8 (abbreviated as HiF8) for deep learning.
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Current large-scale diffusion models represent a giant leap forward in conditional image synthesis, capable of interpreting diverse cues like text, human poses, and edges.
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain.
Ranked #21 on
Domain Generalization
on PACS
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
To study what the LLM can do with this XML-based textual description of images, we test the LLM on three broad computer vision tasks: (i) visual reasoning and question answering, (ii) image classification under distribution shift, few-shot learning, and (iii) generating new images using visual prompting.
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks.
Ranked #1 on
Classifier calibration
on CIFAR-100
Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation
Finally, we test this simple technique we identify (worst-case data augmentation with squared l2 norm alignment regularization) and show that the benefits of this method outrun those of the specially designed methods.
The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization
Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e. g., generalization across distributions) is valued.
On the Integration of Self-Attention and Convolution
In this paper, we show that there exists a strong underlying relation between them, in the sense that the bulk of computations of these two paradigms are in fact done with the same operation.
Toward Learning Human-aligned Cross-domain Robust Models by Countering Misaligned Features
Machine learning has demonstrated remarkable prediction accuracy over i. i. d data, but the accuracy often drops when tested with data from another distribution.
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition
Inspired by this phenomenon, we propose a Dynamic Transformer to automatically configure a proper number of tokens for each input image.
Ranked #28 on
Image Classification
on CIFAR-100
(using extra training data)
On the Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Learning Robust Models by Countering Spurious Correlations
In this paper, we formally study the generalization error bound for this setup with the knowledge of how the spurious features are associated with the label.
Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations
Data augmentation is one of the most popular techniques for improving the robustness of neural networks.
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Moreover, the image-level category labels do not enforce consistent object detection across different transformations of the same images.
Ranked #1 on
Weakly Supervised Object Detection
on MSCOCO
Self-Challenging Improves Cross-Domain Generalization
We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data.
Ranked #35 on
Domain Generalization
on PACS
Multiple Anchor Learning for Visual Object Detection
In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector.
Ranked #119 on
Object Detection
on COCO test-dev
High Frequency Component Helps Explain the Generalization of Convolutional Neural Networks
We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN).
Improving Object Detection with Inverted Attention
Our approach (1) operates along both the spatial and channels dimensions of the feature maps; (2) requires no extra training on hard samples, no extra network parameters for attention estimation, and no testing overheads.
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently.
