Search Results for author:
Found 17 papers, 7 papers with code
A Concept-based Interpretable Model for the Diagnosis of Choroid Neoplasias using Multimodal Data
Diagnosing rare diseases presents a common challenge in clinical practice, necessitating the expertise of specialists for accurate identification.
VideoPoet: A Large Language Model for Zero-Shot Video Generation
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals.
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
PolyMaX: General Dense Prediction with Mask Transformer
Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction.
Ranked #2 on
Surface Normals Estimation
on NYU Depth v2
SANPO: A Scene Understanding, Accessibility, Navigation, Pathfinding, Obstacle Avoidance Dataset
All synthetic sessions and a subset of real sessions have temporally consistent dense panoptic segmentation labels.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed.
MMGA: Multimodal Learning with Graph Alignment
In this paper, we propose MMGA (Multimodal learning with Graph Alignment), a novel multimodal pre-training framework to incorporate information from graph (social network), image and text modalities on social media to enhance user representation learning.
On Label Granularity and Object Localization
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels.
DropMessage: Unifying Random Dropping for Graph Neural Networks
First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models.
Coverage Control Algorithm for DSNs Based on Improved Gravitational Search
—In directional sensor networks (DSNs), coverage control is an important way to ensure efficient communication and reliable data transmission.
FCCDN: Feature Constraint Network for VHR Image Change Detection
Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning.
When Does Contrastive Visual Representation Learning Work?
Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.
An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery
Semantic segmentation is an essential part of deep learning.
A Fast and Precise Method for Large-Scale Land-Use Mapping Based on Deep Learning
The land-use map is an important data that can reflect the use and transformation of human land, and can provide valuable reference for land-use planning.
DNN Dataflow Choice Is Overrated
Many DNN accelerators have been proposed and built using different microarchitectures and program mappings.
Distributed, Parallel, and Cluster Computing
Programming Heterogeneous Systems from an Image Processing DSL
We address this problem by extending the image processing language, Halide, so users can specify which portions of their applications should become hardware accelerators, and then we provide a compiler that uses this code to automatically create the accelerator along with the "glue" code needed for the user's application to access this hardware.
Software Engineering
A Systematic Approach to Blocking Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations.
