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Found 31 papers, 11 papers with code
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss
This paper proposes a hierarchical loss for monocular depth estimation, which measures the differences between the prediction and ground truth in hierarchical embedding spaces of depth maps.
AD-H: Autonomous Driving with Hierarchical Agents
However, prevalent approaches often directly translate high-level instructions into low-level vehicle control signals, which deviates from the inherent language generation paradigm of MLLMs and fails to fully harness their emergent powers.
BERP: A Blind Estimator of Room Acoustic and Physical Parameters for Single-Channel Noisy Speech Signals
On the other hand, in this paper, we propose a novel universal blind estimation framework called the blind estimator of room acoustical and physical parameters (BERP), by introducing a new stochastic room impulse response (RIR) model, namely, the sparse stochastic impulse response (SSIR) model, and endowing the BERP with a unified encoder and multiple separate predictors to estimate RPPs and SSIR parameters in parallel.
Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception
Multimodal Large Language Model (MLLMs) leverages Large Language Models as a cognitive framework for diverse visual-language tasks.
Faster Projected GAN: Towards Faster Few-Shot Image Generation
In order to solve the problems of long training time, large consumption of computing resources and huge parameter amount of GAN network in image generation, this paper proposes an improved GAN network model, which is named Faster Projected GAN, based on Projected GAN.
PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety
In this paper, we explore these concerns through the innovative lens of agent psychology, revealing that the dark psychological states of agents constitute a significant threat to safety.
Isomer: Isomerous Transformer for Zero-shot Video Object Segmentation
Recent leading zero-shot video object segmentation (ZVOS) works devote to integrating appearance and motion information by elaborately designing feature fusion modules and identically applying them in multiple feature stages.
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Our method sets the new state of the art for depth-aware panoptic segmentation on both Cityscapes-DVPS and SemKITTI-DVPS datasets.
BEV-IO: Enhancing Bird's-Eye-View 3D Detection with Instance Occupancy
At the core of our method is the newly-designed instance occupancy prediction (IOP) module, which aims to infer point-level occupancy status for each instance in the frustum space.
Time Reversal Enabled Fiber-Optic Time Synchronization
It measures the clock difference between two locations without involving a data layer, which can reduce the complexity of the system.
ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data
Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking.
Blind estimation of room acoustic parameters from speech signals based on extended model of room impulse response
The root-mean-square errors between the estimated and ground-truth results were used to comparatively evaluate the proposed method with the previous method.
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection
We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label.
Comparison between Time Shifting Deviation and Cross-correlation Methods
It stands in clear contrast to the result of cross-correlation method, whose localization error is 70 m and the standard deviation is 208. 4 m. Compared with cross-correlation method, TSDEV has the same resistance to white noise, but has fewer boundary conditions and better suppression on linear drift or common noise, which leads to more precise TDE results.
Towards Toxic and Narcotic Medication Detection with Rotated Object Detector
Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry.
Video Annotation for Visual Tracking via Selection and Refinement
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?
The integration of SAG loss and two-stream network enables more consistent scale inference and more accurate relative depth estimation.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays.
Appearance-free Tripartite Matching for Multiple Object Tracking
We focus on the general MOT problem regardless of the appearance and propose an appearance-free tripartite matching to avoid the irregular velocity problem of the bipartite matching.
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation
Monocular depth estimation is an ill-posed problem, and as such critically relies on scene priors and semantics.
Ranked #2 on
Depth Estimation
on Cityscapes test
When Relation Networks meet GANs: Relation GANs with Triplet Loss
Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets.
FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint Comparison
We designed a fast similarity search engine for large molecular libraries: FPScreen.
DeepLens: Shallow Depth Of Field From A Single Image
To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module.
Learning regression and verification networks for long-term visual tracking
Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent.
Structured Siamese Network for Real-Time Visual Tracking
In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking.
Learning to Detect Salient Objects With Image-Level Supervision
In the second stage, FIN is fine-tuned with its predicted saliency maps as ground truth.
Visual Tracking via Shallow and Deep Collaborative Model
In the discriminative model, we exploit the advances of deep learning architectures to learn generic features which are robust to both background clutters and foreground appearance variations.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN.
STCT: Sequentially Training Convolutional Networks for Visual Tracking
To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features.
Visual Tracking With Fully Convolutional Networks
Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet.
Deep Networks for Saliency Detection via Local Estimation and Global Search
In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions.
