Search Results for author:
Found 51 papers, 22 papers with code
Improving Audio-Visual Segmentation with Bidirectional Generation
In this paper, inspired by the human ability to mentally simulate the sound of an object and its visual appearance, we introduce a bidirectional generation framework.
Image-based Geolocalization by Ground-to-2.5D Map Matching
We study the image-based geolocalization problem that aims to locate ground-view query images on cartographic maps.
All-pairs Consistency Learning for Weakly Supervised Semantic Segmentation
Given a pair of augmented views, our approach regularizes the activation intensities between a pair of augmented views, while also ensuring that the affinity across regions within each view remains consistent.
Object Localization
Weakly supervised Semantic Segmentation
+1
Contrastive Conditional Latent Diffusion for Audio-visual Segmentation
We propose a latent diffusion model with contrastive learning for audio-visual segmentation (AVS) to extensively explore the contribution of audio.
Scaling TransNormer to 175 Billion Parameters
TransNormerLLM evolves from the previous linear attention architecture TransNormer by making advanced modifications that include positional embedding, linear attention acceleration, gating mechanism, tensor normalization, inference acceleration and stabilization.
Exploring Transformer Extrapolation
While these methods perform well on a variety of corpora, the conditions for length extrapolation have yet to be investigated.
Linearized Relative Positional Encoding
Meanwhile, it emphasizes a general paradigm for designing broadly more relative positional encoding methods that are applicable to linear transformers.
Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning
In this case, salient objects are typically non-camouflaged, and camouflaged objects are usually not salient.
Toeplitz Neural Network for Sequence Modeling
Sequence modeling has important applications in natural language processing and computer vision.
An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems
The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks.
Ranked #1 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
(using extra training data)
Fine-grained Audible Video Description
We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD).
Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning
Based on this observation, we propose a new learning strategy named False Negative Aware Contrastive (FNAC) to mitigate the problem of misleading the training with such false negative samples.
Improving Audio-Visual Video Parsing with Pseudo Visual Labels
We perform extensive experiments on the LLP dataset and demonstrate that our method can generate high-quality segment-level pseudo labels with the help of our newly proposed loss and the label denoising strategy.
Audio-Visual Segmentation with Semantics
To deal with these problems, we propose a new baseline method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
The Devil in Linear Transformer
In this paper, we examine existing kernel-based linear transformers and identify two key issues that lead to such performance gaps: 1) unbounded gradients in the attention computation adversely impact the convergence of linear transformer models; 2) attention dilution which trivially distributes attention scores over long sequences while neglecting neighbouring structures.
Linear Video Transformer with Feature Fixation
Therefore, we propose a feature fixation module to reweight the feature importance of the query and key before computing linear attention.
Neural Architecture Search on Efficient Transformers and Beyond
To address this issue, we propose a new framework to find optimal architectures for efficient Transformers with the neural architecture search (NAS) technique.
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification
Fine-tuning is widely applied in image classification tasks as a transfer learning approach.
Deep Laparoscopic Stereo Matching with Transformers
The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection.
Audio-Visual Segmentation
To deal with the AVS problem, we propose a novel method that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process.
Vicinity Vision Transformer
Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity.
Deep Non-rigid Structure-from-Motion: A Sequence-to-Sequence Translation Perspective
In this paper, we propose to model deep NRSfM from a sequence-to-sequence translation perspective, where the input 2D frame sequence is taken as a whole to reconstruct the deforming 3D non-rigid shape sequence.
Implicit Motion Handling for Video Camouflaged Object Detection
We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames.
cosFormer: Rethinking Softmax in Attention
As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length.
Transcribing Natural Languages for The Deaf via Neural Editing Programs
This work studies the task of glossification, of which the aim is to em transcribe natural spoken language sentences for the Deaf (hard-of-hearing) community to ordered sign language glosses.
GETAM: Gradient-weighted Element-wise Transformer Attention Map for Weakly-supervised Semantic segmentation
To bridge their gap, a Class Activation Map (CAM) is usually generated to provide pixel level pseudo labels.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation
The task of semi-supervised video object segmentation (VOS) has been greatly advanced and state-of-the-art performance has been made by dense matching-based methods.
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model
Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty.
IDENTIFYING CONCEALED OBJECTS FROM VIDEOS
The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames.
RGB-D Saliency Detection via Cascaded Mutual Information Minimization
In this paper, we introduce a novel multi-stage cascaded learning framework via mutual information minimization to "explicitly" model the multi-modal information between RGB image and depth data.
Memory-Free Generative Replay For Class-Incremental Learning
With the absence of old task images, they often assume that old knowledge is well preserved if the classifier produces similar output on new images.
Exploring Depth Contribution for Camouflaged Object Detection
In this paper, we study the depth contribution for camouflaged object detection, where the depth maps are generated with existing monocular depth estimation (MDE) methods.
Invertible Attention
Attention has been proved to be an efficient mechanism to capture long-range dependencies.
Blind Motion Deblurring Super-Resolution: When Dynamic Spatio-Temporal Learning Meets Static Image Understanding
Single-image super-resolution (SR) and multi-frame SR are two ways to super resolve low-resolution images.
Positive Sample Propagation along the Audio-Visual Event Line
To encourage the network to extract high correlated features for positive samples, a new audio-visual pair similarity loss is proposed.
Deep Two-View Structure-from-Motion Revisited
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM.
Ranked #13 on
Monocular Depth Estimation
on KITTI Eigen split
ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring
Video deblurring models exploit consecutive frames to remove blurs from camera shakes and object motions.
Depth Completion using Piecewise Planar Model
More specifically, we represent the desired depth map as a collection of 3D planar and the reconstruction problem is formulated as the optimization of planar parameters.
Efficient Depth Completion Using Learned Bases
The given sparse depth points are served as a data term to constrain the weighting process.
Displacement-Invariant Cost Computation for Efficient Stereo Matching
A common way to speed up the computation is to downsample the feature volume, but this loses high-frequency details.
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation
Learning matching costs has been shown to be critical to the success of the state-of-the-art deep stereo matching methods, in which 3D convolutions are applied on a 4D feature volume to learn a 3D cost volume.
Hierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation.
Ranked #2 on
Stereo Disparity Estimation
on Scene Flow
Deblurring by Realistic Blurring
To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
Ranked #14 on
Deblurring
on HIDE (trained on GOPRO)
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes
In this paper, we propose Deep Epipolar Flow, an unsupervised optical flow method which incorporates global geometric constraints into network learning.
Noise-Aware Unsupervised Deep Lidar-Stereo Fusion
In this paper, we present LidarStereoNet, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps.
Stereo Computation for a Single Mixture Image
This paper proposes an original problem of \emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before.
3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes
This paper is concerned with the problem of how to better exploit 3D geometric information for dense semantic image labeling.
Open-World Stereo Video Matching with Deep RNN
Deep Learning based stereo matching methods have shown great successes and achieved top scores across different benchmarks.
Adversarial Spatio-Temporal Learning for Video Deblurring
To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training.
Self-Supervised Learning for Stereo Matching with Self-Improving Ability
Exiting deep-learning based dense stereo matching methods often rely on ground-truth disparity maps as the training signals, which are however not always available in many situations.
Robust Multi-body Feature Tracker: A Segmentation-free Approach
Feature tracking is a fundamental problem in computer vision, with applications in many computer vision tasks, such as visual SLAM and action recognition.
