Search Results for author:
Found 22 papers, 13 papers with code
SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving
End-to-End paradigms use a unified framework to implement multi-tasks in an autonomous driving system.
Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine Transformations
Under the minimal $4$-point configuration, the first and the last similarity transformations in SKS are computed by two anchor points on target and source planes, respectively.
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Video compression is widely used in digital television, surveillance systems, and virtual reality.
KNVQA: A Benchmark for evaluation knowledge-based VQA
Within the multimodal field, large vision-language models (LVLMs) have made significant progress due to their strong perception and reasoning capabilities in the visual and language systems.
Multiscale Superpixel Structured Difference Graph Convolutional Network for VL Representation
It parses the entire image as a fine-to-coarse hierarchical structure of constituent visual patterns, and captures multiscale features by progressively merging adjacent superpixels as graph nodes.
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based Rollout
Here, we propose a goal-conditioned HRL framework named Guided Cooperation via Model-based Rollout (GCMR), aiming to bridge inter-layer information synchronization and cooperation by exploiting forward dynamics.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning
In this work, we propose an efficient computation framework for multimodal alignment by introducing a novel visual semantic module to further improve the performance of the VL tasks.
LittleMu: Deploying an Online Virtual Teaching Assistant via Heterogeneous Sources Integration and Chain of Teach Prompts
However, few MOOC platforms are providing human or virtual teaching assistants to support learning for massive online students due to the complexity of real-world online education scenarios and the lack of training data.
LOIS: Looking Out of Instance Semantics for Visual Question Answering
Visual question answering (VQA) has been intensively studied as a multimodal task that requires effort in bridging vision and language to infer answers correctly.
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance.
PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model.
OnePose: One-Shot Object Pose Estimation without CAD Models
We propose a new method named OnePose for object pose estimation.
An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture Search
However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects.
High-fidelity 3D Model Compression based on Key Spheres
Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction.
Provably Secure Generative Linguistic Steganography
Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext).
SN-Graph: a Minimalist 3D Object Representation for Classification
Using deep learning techniques to process 3D objects has achieved many successes.
You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking
In this work, we propose a novel system for integrated 3D object detection and tracking, which uses a dynamic object occupancy map and previous object states as spatial-temporal memory to assist object detection in future frames.
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification
Voxel-based 3D object classification has been thoroughly studied in recent years.
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images
To handle this problem, in this paper, learning transferrable representations from unlabeled PolSAR data through convolutional architectures is explored for the first time.
Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation
In this paper, we propose a novel system named Disp R-CNN for 3D object detection from stereo images.
3D Object Detection From Stereo Images
Disparity Estimation
+2
InSphereNet: a Concise Representation and Classification Method for 3D Object
Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed method constructs a class of more representative features named infilling spheres from signed distance field (SDF).
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification
By DAS, the weights parameters and architecture parameters (corresponds to the hyperparameters but not the topologies) can be optimized by stochastic gradient descent method during the training.
