Search Results for author:
Found 33 papers, 6 papers with code
Large-Scale Riemannian Meta-Optimization via Subspace Adaptation
However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimization settings, as the learned optimizer can only adapt gradients of a fixed size and thus cannot be shared across different Riemannian parameters.
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage
The advancement of large language models (LLMs) prompts the development of multi-modal agents, which are used as a controller to call external tools, providing a feasible way to solve practical tasks.
Consistency of Compositional Generalization across Multiple Levels
Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts.
World knowledge-enhanced Reasoning Using Instruction-guided Interactor in Autonomous Driving
The Multi-modal Large Language Models (MLLMs) with extensive world knowledge have revitalized autonomous driving, particularly in reasoning tasks within perceivable regions.
Residual Hyperbolic Graph Convolution Networks
In this paper, we propose residual hyperbolic graph convolutional networks (R-HGCNs) to address the over-smoothing problem.
FIRE: A Dataset for Feedback Integration and Refinement Evaluation of Multimodal Models
Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction.
Temporally Consistent Stereo Matching
Based on this coherent state, we introduce a dual-space refinement module to iteratively refine the initialized result in both disparity and disparity gradient spaces, improving estimations in ill-posed regions.
Fine-Grained Annotation for Face Anti-Spoofing
In this paper, we propose a fine-grained annotation method for face anti-spoofing.
Neural 3D Scene Reconstruction from Multiple 2D Images without 3D Supervision
We improve the reconstruction quality of complex geometry scene regions with sparse depth obtained by using the geometric constraints.
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding
Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length.
Exploring Data Geometry for Continual Learning
Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account.
Parameterized Cost Volume for Stereo Matching
In this paper, we propose a parameterized cost volume to encode the entire disparity space using multi-Gaussian distribution.
Sparse Point Guided 3D Lane Detection
The high-resolution local information brought by sparse points refines 3D lanes in the BEV space hierarchically from low resolution to high resolution.
Video Anomaly Detection via Sequentially Learning Multiple Pretext Tasks
In this way, our method is able to reduce the difficulties of learning and avoid converging to sub-optimal solutions.
Exploring the Effect of Primitives for Compositional Generalization in Vision-and-Language
Compositional generalization is critical to simulate the compositional capability of humans, and has received much attention in the vision-and-language (V&L) community.
Maintaining Reasoning Consistency in Compositional Visual Question Answering
Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions.
Global-Aware Registration of Less-Overlap RGB-D Scans
We propose a novel method of registering less-overlap RGB-D scans.
A Decomposition Model for Stereo Matching
In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases.
A Hyperbolic-to-Hyperbolic Graph Convolutional Network
Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation.
Curvature Generation in Curved Spaces for Few-Shot Learning
Few-shot learning describes the challenging problem of recognizing samples from unseen classes given very few labeled examples.
Video Captioning Using Weak Annotation
Through traversing the dependency trees, the sentences are generated to train the captioning model.
Content-Aware Inter-Scale Cost Aggregation for Stereo Matching
In this paper, we present a content-aware inter-scale cost aggregation method that adaptively aggregates and upsamples the cost volume from coarse-scale to fine-scale by learning dynamic filter weights according to the content of the left and right views on the two scales.
Deep 3D Portrait from a Single Image
We evaluate the accuracy of our method both in 3D and with pose manipulation tasks on 2D images.
Relational Reasoning using Prior Knowledge for Visual Captioning
Extensive experiments on the MS-COCO image captioning benchmark and the MSVD video captioning benchmark validate the superiority of our method on leveraging prior commonsense knowledge to enhance relational reasoning for visual captioning.
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.
Ranked #3 on
3D Face Reconstruction
on Florence
(RMSE Cooperative metric)
Stitching Videos from a Fisheye Lens Camera and a Wide-Angle Lens Camera for Telepresence Robots
In this paper, we propose to stitch videos from the FF-camera with a wide-angle lens and the DF-camera with a fisheye lens for telepresence robots.
Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
The cost aggregation sub-architecture is realized by a two-stream network: one for the generation of cost aggregation proposals, the other for the selection of the proposals.
Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds
To this end, several new layers are introduced in our network, including a nonlinear kernel aggregation layer, an SPD matrix transformation layer, and a vectorization layer.
Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection
We use the anisotropic diffusion to enhance the edges and boundary locations of a face image, and the kernel matrix model to extract face image features which we call the diffusion-kernel (D-K) features.
A Hybrid Data Association Framework for Robust Online Multi-Object Tracking
In this paper, we present a hybrid data association framework with a min-cost multi-commodity network flow for robust online multi-object tracking.
Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration
The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization.
Ranked #6 on
Point Cloud Registration
on FP-O-H
Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking
The temporal dynamic makes a sufficient complement to the spatial structure of varying appearances in the feature space, which significantly improves the affinity measurement between trajectories and detections.
Discriminatively Trained And-Or Tree Models for Object Detection
This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection.
