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Found 23 papers, 13 papers with code
PSDNet and DPDNet: Efficient channel expansion, Depthwise-Pointwise-Depthwise Inverted Bottleneck Block
To design more efficient lightweight concolutional neural netwok, Depthwise-Pointwise-Depthwise inverted bottleneck block (DPD block) is proposed and DPDNet is designed by stacking DPD block.
Reconstructing Images of Two Adjacent Objects through Scattering Medium Using Generative Adversarial Network
It is shown that based on the trained YGAN, we can reconstruct images of two adjacent objects from one speckle pattern with high fidelity.
Structure from Silence: Learning Scene Structure from Ambient Sound
From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene.
Sound Localization by Self-Supervised Time Delay Estimation
We adapt the contrastive random walk of Jabri et al. to learn a cycle-consistent representation from unlabeled stereo sounds, resulting in a model that performs on par with supervised methods on "in the wild" internet recordings.
A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutions
We demonstrate that using parallel branches as opposed to serial stacked convolutional layers will be a more powerful design for robotic visual grasping tasks.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
Mix and Localize: Localizing Sound Sources in Mixtures
This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal.
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Manipulated videos often contain subtle inconsistencies between their visual and audio signals.
Ranked #3 on
DeepFake Detection
on FakeAVCeleb
Sound Localization from Motion: Jointly Learning Sound Direction and Camera Rotation
In this paper, we use these cues to solve a problem we call Sound Localization from Motion (SLfM): jointly estimating camera rotation and localizing sound sources.
UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner
Moreover, UniSeg also beats other pre-trained models on two downstream datasets, providing the community with a high-quality pre-trained model for 3D medical image segmentation.
Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation
The reconstruction alignment (RA) module uses a variational auto-encoder (VAE) to reconstruct the input image and thus boosts the image representation ability of the network in a self-supervised way.
Conditional Generation of Audio from Video via Foley Analogies
Second, we propose a model for generating a soundtrack for a silent input video, given a user-supplied example that specifies what the video should "sound like".
Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation
In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution.
MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection
We first formulate the training and parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters.
SAMN: A Sample Attention Memory Network Combining SVM and NN in One Architecture
To address this, we propose a sample attention memory network (SAMN) that effectively combines SVM and NN by incorporating sample attention module, class prototypes, and memory block to NN.
A Survey of Large Language Models Attribution
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
Temporal Knowledge Question Answering via Abstract Reasoning Induction
In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties.
Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning
In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data.
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model in real-world applications.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
We introduce UniTouch, a unified tactile model for vision-based touch sensors connected to multiple modalities, including vision, language, and sound.
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark
The dataset includes high-quality and densely captured room impulse response data paired with multi-view images, and precise 6DoF pose tracking data for sound emitters and listeners in the rooms.
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
In addition, edge variations in %potential feature channels of the reconstruction error map also affect details matching, we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation.
Ranked #1 on
Stereo Depth Estimation
on KITTI 2015
