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Found 27 papers, 12 papers with code
New Dataset and Methods for Fine-Grained Compositional Referring Expression Comprehension via Specialist-MLLM Collaboration
To address fine-grained compositional REC, we propose novel methods based on a Specialist-MLLM collaboration framework, leveraging the complementary strengths of them: Specialist Models handle simpler tasks efficiently, while MLLMs are better suited for complex reasoning.
Disentangled Modeling of Preferences and Social Influence for Group Recommendation
The group recommendation (GR) aims to suggest items for a group of users in social networks.
Ultra-Low Complexity On-Orbit Compression for Remote Sensing Imagery via Block Modulated Imaging
Efficient compression of remote sensing imagery is a critical solution to alleviate these burdens on satellites.
Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance
To achieve our goal, we propose a novel subgoal guidance learning strategy.
Towards Real-time Video Compressive Sensing on Mobile Devices
The fast evolving mobile devices and existing high-performance video SCI reconstruction algorithms motivate us to develop mobile reconstruction methods for real-world applications.
Diffusion Models as Optimizers for Efficient Planning in Offline RL
To evaluate the effectiveness and efficiency of the Trajectory Diffuser, we conduct experiments on the D4RL benchmarks.
VEON: Vocabulary-Enhanced Occupancy Prediction
Hence, instead of building our model from scratch, we try to blend 2D foundation models, specifically a depth model MiDaS and a semantic model CLIP, to lift the semantics to 3D space, thus fulfilling 3D occupancy.
DMM: Disparity-guided Multispectral Mamba for Oriented Object Detection in Remote Sensing
Inspired by the efficiency and lower complexity of Mamba in long sequence tasks, we propose Disparity-guided Multispectral Mamba (DMM), a multispectral oriented object detection framework comprised of a Disparity-guided Cross-modal Fusion Mamba (DCFM) module, a Multi-scale Target-aware Attention (MTA) module, and a Target-Prior Aware (TPA) auxiliary task.
Q-SNNs: Quantized Spiking Neural Networks
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent information and process them in an asynchronous event-driven manner, offering an energy-efficient paradigm for the next generation of machine intelligence.
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 Fine-tuned CLIP
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.
JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement
By treating Retinex- and semantic-based priors as the condition, JoReS-Diff presents a unique perspective for establishing an diffusion model for LLIE and similar image enhancement tasks.
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 #5 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.
