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Found 18 papers, 4 papers with code
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem.
Structure-Aware Human Body Reshaping with Adaptive Affinity-Graph Network
Particularly, an SRM filter is utilized to extract high-frequency details, which are combined with spatial features as input to the BSD.
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning
Aligning these distributions between corresponding regions from different tasks imparts higher flexibility and capacity to capture intra-region structures, accommodating a broader range of tasks.
Open-Vocabulary Calibration for Vision-Language Models
Vision-language models (VLMs) have emerged as formidable tools, showing their strong capability in handling various open-vocabulary tasks in image recognition, text-driven visual content generation, and visual chatbots, to name a few.
ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model for Low-Light Image Enhancement
Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models.
Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Multi-modal 3D scene understanding has gained considerable attention due to its wide applications in many areas, such as autonomous driving and human-computer interaction.
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Faster Video Moment Retrieval with Point-Level Supervision
Existing VMR methods suffer from two defects: (1) massive expensive temporal annotations are required to obtain satisfying performance; (2) complicated cross-modal interaction modules are deployed, which lead to high computational cost and low efficiency for the retrieval process.
Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement
To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model.
Ranked #3 on
Low-Light Image Enhancement
on LOLv2
ScanERU: Interactive 3D Visual Grounding based on Embodied Reference Understanding
Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances. To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visual grounding.
Thunder: Thumbnail based Fast Lightweight Image Denoising Network
To achieve promising results on removing noise from real-world images, most of existing denoising networks are formulated with complex network structure, making them impractical for deployment.
Learning content and context with language bias for Visual Question Answering
Many works concentrate on how to reduce language bias which makes models answer questions ignoring visual content and language context.
PPGN: Phrase-Guided Proposal Generation Network For Referring Expression Comprehension
Reference expression comprehension (REC) aims to find the location that the phrase refer to in a given image.
Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning
In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD.
Modeling the Control of COVID-19: Impact of Policy Interventions and Meteorological Factors
Home quarantine is the most important one to prevent the spread of COVID-19.
ERL-Net: Entangled Representation Learning for Single Image De-Raining
In this paper, we hypothesize that there exists an inherent mapping between the low-quality embedding to a latent optimal one, with which the generator (decoder) can produce much better results.
