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Found 29 papers, 10 papers with code
Dual Attribute-Spatial Relation Alignment for 3D Visual Grounding
3D visual grounding is an emerging research area dedicated to making connections between the 3D physical world and natural language, which is crucial for achieving embodied intelligence.
GaussianPro: 3D Gaussian Splatting with Progressive Propagation
The advent of 3D Gaussian Splatting (3DGS) has recently brought about a revolution in the field of neural rendering, facilitating high-quality renderings at real-time speed.
Slot-VLM: SlowFast Slots for Video-Language Modeling
A pivotal challenge is the development of an efficient method to encapsulate video content into a set of representative tokens to align with LLMs.
Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing
In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow.
Hierarchical Intra-modal Correlation Learning for Label-free 3D Semantic Segmentation
Recent methods for label-free 3D semantic segmentation aim to assist 3D model training by leveraging the open-world recognition ability of pre-trained vision language models.
Cross-Dimension Affinity Distillation for 3D EM Neuron Segmentation
Recent advancements utilize 3D CNNs to predict a 3D affinity map with improved accuracy but suffer from two challenges: high computational cost and limited input size especially for practical deployment for large-scale EM volumes.
Retrieval-based Video Language Model for Efficient Long Video Question Answering
To address these issues, we introduce a simple yet effective retrieval-based video language model (R-VLM) for efficient and interpretable long video QA.
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities.
DPF-Net: Combining Explicit Shape Priors in Deformable Primitive Field for Unsupervised Structural Reconstruction of 3D Objects
Unsupervised methods for reconstructing structures face significant challenges in capturing the geometric details with consistent structures among diverse shapes of the same category.
Shape Guided Gradient Voting for Domain Generalization
In this work, with the assumption that the gradients of a specific domain samples under the classification task could also reflect the property of the domain, we propose a Shape Guided Gradient Voting (SGGV) method for domain generalization.
Region-Enhanced Feature Learning for Scene Semantic Segmentation
We design a region-based feature enhancement (RFE) module, which consists of a Semantic-Spatial Region Extraction stage and a Region Dependency Modeling stage.
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
Under the Lagrangian view, we parameterize the scene motion by tracking the trajectory of particles on objects.
Learning Cross-Representation Affinity Consistency for Sparsely Supervised Biomedical Instance Segmentation
Sparse instance-level supervision has recently been explored to address insufficient annotation in biomedical instance segmentation, which is easier to annotate crowded instances and better preserves instance completeness for 3D volumetric datasets compared to common semi-supervision. In this paper, we propose a sparsely supervised biomedical instance segmentation framework via cross-representation affinity consistency regularization.
Paint by Example: Exemplar-based Image Editing with Diffusion Models
Language-guided image editing has achieved great success recently.
Semantics-Preserving Sketch Embedding for Face Generation
With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images.
Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation
Sketching is an intuitive and effective way for content creation.
Exploiting Correspondences with All-pairs Correlations for Multi-view Depth Estimation
However, multi-view depth estimation is fundamentally a correspondence-based optimization problem, but previous learning-based methods mainly rely on predefined depth hypotheses to build correspondence as the cost volume and implicitly regularize it to fit depth prediction, deviating from the essence of iterative optimization based on stereo correspondence.
Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design
In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach.
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability.
Contrastive Learning for Mitochondria Segmentation
However, due to the image degradation during the imaging process, the large variety of mitochondrial structures, as well as the presence of noise, artifacts and other sub-cellular structures, mitochondria segmentation is very challenging.
Multi-Modulation Network for Audio-Visual Event Localization
We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance to modulate the related auditory, visual, and fused features.
Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds
In this paper, we propose an unsupervised shape abstraction method to map a point cloud into a compact cuboid representation.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity.
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
S2R-DepthNet consists of: a) a Structure Extraction (STE) module which extracts a domaininvariant structural representation from an image by disentangling the image into domain-invariant structure and domain-specific style components, b) a Depth-specific Attention (DSA) module, which learns task-specific knowledge to suppress depth-irrelevant structures for better depth estimation and generalization, and c) a depth prediction module (DP) to predict depth from the depth-specific representation.
Compositionally Generalizable 3D Structure Prediction
However, there remains a much more difficult and under-explored issue on how to generalize the learned skills over unseen object categories that have very different shape geometry distributions.
DeepFacePencil: Creating Face Images from Freehand Sketches
In this paper, we explore the task of generating photo-realistic face images from hand-drawn sketches.
LinesToFacePhoto: Face Photo Generation from Lines with Conditional Self-Attention Generative Adversarial Network
The large-scale discriminator enforces the completeness of global structures and the small-scale discriminator encourages fine details, thereby enhancing the realism of generated face images.
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details.
Ranked #58 on
Monocular Depth Estimation
on NYU-Depth V2
(RMSE metric)
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation.
