Search Results for author:
Found 25 papers, 14 papers with code
Optimized View and Geometry Distillation from Multi-view Diffuser
We leverage the rendered views from the optimized radiance field as the basis and develop a two-step specialization process of a 2D diffusion model, which is adept at conducting object-specific denoising and generating high-quality multi-view images.
Fine-grained Appearance Transfer with Diffusion Models
A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes.
Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields
Different from existing methods that consider cross-view and along-epipolar information independently, EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process.
Ranked #1 on
Generalizable Novel View Synthesis
on Shiny dataset
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation
Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships.
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Our workflow generates target-like images using the noisy predictions from the original target domain images.
Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization
In this paper, we conduct a thorough causal analysis to investigate the origins of biased activation.
Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning
Specifically, DLL decouples the predicate labels and adopts separate classifiers to learn actional and spatial patterns respectively.
Ranked #1 on
Video scene graph generation
on ImageNet-VidVRD
Knowledge-guided Causal Intervention for Weakly-supervised Object Localization
Previous weakly-supervised object localization (WSOL) methods aim to expand activation map discriminative areas to cover the whole objects, yet neglect two inherent challenges when relying solely on image-level labels.
Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo
To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds.
Ranked #6 on
Point Clouds
on Tanks and Temples
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them.
Local-Global Context Aware Transformer for Language-Guided Video Segmentation
We explore the task of language-guided video segmentation (LVS).
Ranked #7 on
Referring Expression Segmentation
on A2D Sentences
Referring Expression Segmentation
Referring Video Object Segmentation
+5
Active Learning for Point Cloud Semantic Segmentation via Spatial-Structural Diversity Reasoning
To deploy SSDR-AL in a more practical scenario, we design a noise-aware iterative labeling strategy to confront the "noisy annotation" problem introduced by the previous "dominant labeling" strategy in superpoints.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value.
Domain Adaptive Person Re-Identification
Person Re-Identification
Contrastive Video-Language Segmentation
We focus on the problem of segmenting a certain object referred by a natural language sentence in video content, at the core of formulating a pinpoint vision-language relation.
Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes
The hierarchical features help the model highlight the decision boundary and focus on hard pixels, and the structural information learned from base classes is treated as the prior knowledge for novel classes.
VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots
In this paper, we investigate the task of hallucinating an authentic high-resolution (HR) human face from multiple low-resolution (LR) video snapshots.
Improving Weakly-supervised Object Localization via Causal Intervention
The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels.
ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation
Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos.
Ranked #4 on
Referring Expression Segmentation
on J-HMDB
(Precision@0.9 metric)
Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data
To generalize the model towards a real scenario, we propose to fulfill several aspects: (1) Look: visually incorporate spatial structure from the single view to enhance the expressiveness of representation; (2) Cast: perceptually align the 2D image features to the 3D shape priors with cross-modal semantic contrastive mapping; (3) Mold: reconstruct stereo-shape of target by transforming embeddings into the desired manifold.
Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
We aim at the problem named One-Shot Unsupervised Domain Adaptation.
domain classification
One-shot Unsupervised Domain Adaptation
+2
Copy and Paste GAN: Face Hallucination from Shaded Thumbnails
This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination.
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex.
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning
To capitalize on the information from unlabeled nodes to boost the training for GCN, we propose a novel framework named Self-Ensembling GCN (SEGCN), which marries GCN with Mean Teacher - another powerful model in semi-supervised learning.
Ranked #4 on
Node Classification
on Cora: fixed 20 node per class
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
We consider the problem of unsupervised domain adaptation in semantic segmentation.
Ranked #8 on
Semantic Segmentation
on DADA-seg
Macro-Micro Adversarial Network for Human Parsing
To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).
Ranked #12 on
Semantic Segmentation
on LIP val
