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Found 59 papers, 21 papers with code
Guided Semantic Flow
Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images.
EBDM: Exemplar-guided Image Translation with Brownian-bridge Diffusion Models
Our method formulates the task as a stochastic Brownian bridge process, a diffusion process with a fixed initial point as structure control and translates into the corresponding photo-realistic image while being conditioned solely on the given exemplar image.
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Extensive experiments demonstrate that our LPLD loss leads to effective adaptation by reducing false negatives and facilitating the use of domain-invariant knowledge from the source model.
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets.
Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation
To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models (DMs) by employing the multi-modal features in the DM into the latent space of the pre-trained GANs.
Bridging Vision and Language Spaces with Assignment Prediction
This paper introduces VLAP, a novel approach that bridges pretrained vision models and large language models (LLMs) to make frozen LLMs understand the visual world.
Improving Visual Recognition with Hyperbolical Visual Hierarchy Mapping
Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details.
Layer-wise Auto-Weighting for Non-Stationary Test-Time Adaptation
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment.
Semantic-aware Network for Aerial-to-Ground Image Synthesis
Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image.
Knowing Where to Focus: Event-aware Transformer for Video Grounding
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries.
Hierarchical Visual Primitive Experts for Compositional Zero-Shot Learning
Previous works for CZSL often suffer from grasping the contextuality between attribute and object, as well as the discriminability of visual features, and the long-tailed distribution of real-world compositional data.
PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification
Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored.
Probabilistic Prompt Learning for Dense Prediction
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models.
Dual-path Adaptation from Image to Video Transformers
In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters.
Ranked #1 on
Action Classification
on Diving-48
TemporalMaxer: Maximize Temporal Context with only Max Pooling for Temporal Action Localization
To this end, we introduce TemporalMaxer, which minimizes long-term temporal context modeling while maximizing information from the extracted video clip features with a basic, parameter-free, and local region operating max-pooling block.
Ranked #1 on
Temporal Action Localization
on MUSES
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning
Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies.
Unsupervised Deep Asymmetric Stereo Matching With Spatially-Adaptive Self-Similarity
In this paper, we present a novel spatially-adaptive self-similarity (SASS) for unsupervised asymmetric stereo matching.
SimOn: A Simple Framework for Online Temporal Action Localization
In addition, the evaluation for Online Detection of Action Start (ODAS) demonstrates the effectiveness and robustness of our method in the online setting.
Language-free Training for Zero-shot Video Grounding
Given an untrimmed video and a language query depicting a specific temporal moment in the video, video grounding aims to localize the time interval by understanding the text and video simultaneously.
PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation
To mitigate this issue, we propose to incorporate an auxiliary point-selective network into a meta-learning framework, called PointFix, to provide a robust initialization of stereo models for online stereo adaptation.
Probabilistic Representations for Video Contrastive Learning
This paper presents Probabilistic Video Contrastive Learning, a self-supervised representation learning method that bridges contrastive learning with probabilistic representation.
Pin the Memory: Learning to Generalize Semantic Segmentation
The rise of deep neural networks has led to several breakthroughs for semantic segmentation.
Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection
By learning sampling offset to the grid of standard convolution, the network can robustly extract the features from targets with shape variations for SAR ship detection.
Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution
We show that the proposed method produces visually diverse and plausible results in multiple domains compared to the state-of-the-art methods.
Memory-guided Image De-raining Using Time-Lapse Data
To address this problem, we propose a novel network architecture based on a memory network that explicitly helps to capture long-term rain streak information in the time-lapse data.
KNN Local Attention for Image Restoration
In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i. e., inductive bias of locality and linear complexity to input resolution.
Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples.
Wide and Narrow: Video Prediction from Context and Motion
Video prediction, forecasting the future frames from a sequence of input frames, is a challenging task since the view changes are influenced by various factors, such as the global context surrounding the scene and local motion dynamics.
DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset.
Self-Supervised Structured Representations for Deep Reinforcement Learning
The proposed method imposes similarity constraints on the three latent volumes; warped query representations by estimated flows, predicted target representations from the transition model, and target representations of future state.
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning
Given the latent representations as skills, a skill-based policy network is trained to generate similar trajectories to the learned decoder of the trajectory VAE.
Self-balanced Learning For Domain Generalization
Domain generalization aims to learn a prediction model on multi-domain source data such that the model can generalize to a target domain with unknown statistics.
Learning Canonical 3D Object Representation for Fine-Grained Recognition
By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification.
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
The ability to perform causal and counterfactual reasoning are central properties of human intelligence.
Mining Better Samples for Contrastive Learning of Temporal Correspondence
We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video.
Prototype-Guided Saliency Feature Learning for Person Search
Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system.
CATs: Cost Aggregation Transformers for Visual Correspondence
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
Ranked #5 on
Semantic correspondence
on PF-WILLOW
Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answering
As a result, our method can learn the question conditioned visual representations attributed to appearance and motion that show powerful capability for video question answering.
Memory-guided Unsupervised Image-to-image Translation
We present a novel unsupervised framework for instance-level image-to-image translation.
Looking into Your Speech: Learning Cross-modal Affinity for Audio-visual Speech Separation
In this paper, we address the problem of separating individual speech signals from videos using audio-visual neural processing.
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images.
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner.
Adaptive confidence thresholding for monocular depth estimation
To cope with the prediction error of the confidence map itself, we also leverage the threshold network that learns the threshold dynamically conditioned on the pseudo depth maps.
SumGraph: Video Summarization via Recursive Graph Modeling
The goal of video summarization is to select keyframes that are visually diverse and can represent a whole story of an input video.
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation
To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space.
Joint Learning of Semantic Alignment and Object Landmark Detection
Based on the key insight that the two tasks can mutually provide supervisions to each other, our networks accomplish this through a joint loss function that alternatively imposes a consistency constraint between the two tasks, thereby boosting the performance and addressing the lack of training data in a principled manner.
Context-Aware Emotion Recognition Networks
We present deep networks for context-aware emotion recognition, called CAER-Net, that exploit not only human facial expression but also context information in a joint and boosting manner.
Ranked #1 on
Emotion Recognition in Context
on CAER-Dynamic
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs.
Semantic Attribute Matching Networks
SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences.
Recurrent Transformer Networks for Semantic Correspondence
Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations.
PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence
To the best of our knowledge, it is the first work that attempts to estimate dense affine transformation fields in a coarse-to-fine manner within deep networks.
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
In this way, our approach draws solutions from the continuous space of affine transformations in a manner that can be computed efficiently through constant-time edge-aware filtering and a proposed affine-varying CNN-based descriptor.
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
The sampling patterns of local structure and the self-similarity measure are jointly learned within the proposed network in an end-to-end and multi-scale manner.
Deeply Aggregated Alternating Minimization for Image Restoration
The proposed framework enables the convolutional neural networks (CNNs) to operate as a prior or regularizer in the AM algorithm.
DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
In this paper, we propose a novel dense descriptor, called dense adaptive self-correlation (DASC), to estimate multi-modal and multi-spectral dense correspondences.
Efficient Splitting-based Method for Global Image Smoothing
In this paper, we introduce a highly efficient splitting-based method for global EPS that minimizes the objective function of ${l_2}$ data and prior terms (possibly non-smooth and non-convex) in linear time.
Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence
We present a novel descriptor, called deep self-convolutional activations (DeSCA), designed for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions.
Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields
We present a method for jointly predicting a depth map and intrinsic images from single-image input.
DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence
To further improve the matching quality and runtime efficiency, we propose a patch-wise receptive field pooling, in which a sampling pattern is optimized with a discriminative learning.
