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Found 24 papers, 10 papers with code
CameraCtrl II: Dynamic Scene Exploration via Camera-controlled Video Diffusion Models
This paper introduces CameraCtrl II, a framework that enables large-scale dynamic scene exploration through a camera-controlled video diffusion model.
SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration
Video restoration poses non-trivial challenges in maintaining fidelity while recovering temporally consistent details from unknown degradations in the wild.
Learning from Concealed Labels
Annotating data for sensitive labels (e. g., disease, smoking) poses a potential threats to individual privacy in many real-world scenarios.
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications.
CoA: Chain-of-Action for Generative Semantic Labels
These VLMs leverage a predefined set of categories to construct text prompts for zero-shot reasoning.
Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering
Previous attempts to regularize 3D Gaussian normals often degrade rendering quality due to the fundamental disconnect between normal vectors and the rendering pipeline in 3DGS-based methods.
Diffusion Models Need Visual Priors for Image Generation
Conventional class-guided diffusion models generally succeed in generating images with correct semantic content, but often struggle with texture details.
Ranked #28 on
Image Generation
on ImageNet 256x256
Learning from True-False Labels via Multi-modal Prompt Retrieving
Weakly supervised learning has recently achieved considerable success in reducing annotation costs and label noise.
Learning from Reduced Labels for Long-Tailed Data
Long-tailed data is prevalent in real-world classification tasks and heavily relies on supervised information, which makes the annotation process exceptionally labor-intensive and time-consuming.
Determined Multi-Label Learning via Similarity-Based Prompt
In this novel labeling setting, each training instance is associated with a \textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label.
Large Model driven Radiology Report Generation with Clinical Quality Reinforcement Learning
Finally, to better reflect the clinical significant and insignificant errors that radiologists would normally assign in the report, we introduce a novel clinical quality reinforcement learning strategy.
OV-PARTS: Towards Open-Vocabulary Part Segmentation
Secondly, part segmentation introduces an open granularity challenge due to the diverse and often ambiguous definitions of parts in the open world.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
Understanding Masked Autoencoders From a Local Contrastive Perspective
Masked AutoEncoder (MAE) has revolutionized the field of self-supervised learning with its simple yet effective masking and reconstruction strategies.
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions.
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method.
In Defense of Clip-based Video Relation Detection
While recent video-based methods utilizing video tubelets have shown promising results, we argue that the effective modeling of spatial and temporal context plays a more significant role than the choice between clip tubelets and video tubelets.
Learning from Stochastic Labels
Annotating multi-class instances is a crucial task in the field of machine learning.
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans
The combination of PET-based metabolic and CT-based anatomic information can contribute to better tumor segmentation results.
Class-Imbalanced Complementary-Label Learning via Weighted Loss
In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions.
Counting with Adaptive Auxiliary Learning
This paper proposes an adaptive auxiliary task learning based approach for object counting problems.
Rethinking the Two-Stage Framework for Grounded Situation Recognition
Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage.
Ranked #4 on
Situation Recognition
on imSitu
Vision Transformer with Progressive Sampling
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation
Second, since learning too many context-specific classification subspaces can suffer from data sparsity issues, we propose a hierarchical semantic aggregation(HSA) module to reduces the number of subspaces by introducing higher order structural information.
XJTLUIndoorLoc: A New Fingerprinting Database for Indoor Localization and Trajectory Estimation Based on Wi-Fi RSS and Geomagnetic Field
In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China.
