Search Results for author:
Found 33 papers, 16 papers with code
Holographic MIMO Multi-Cell Communications
Then, for the design of HMIMO metamaterial elements under binary tuning constraints, we first propose a low-complexity suboptimal algorithm with closed-form solutions by exploiting the hidden convexity (HC) in the quadratic problem and then further propose an accelerated sphere decoding (SD)-based algorithm which yields global optimal solution in the iteration.
Beyond Single-Value Metrics: Evaluating and Enhancing LLM Unlearning with Cognitive Diagnosis
Due to the widespread use of LLMs and the rising critical ethical and safety concerns, LLM unlearning methods have been developed to remove harmful knowledge and undesirable capabilities.
Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation
When feature selection techniques are applied in causal inference, the crucial criterion is to select variables that, when used for matching, can achieve an unbiased and robust estimation of causal quantities.
SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering
Audio-Visual Question Answering (AVQA) is a challenging task that involves answering questions based on both auditory and visual information in videos.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+2
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process.
Robust Utility-Preserving Text Anonymization Based on Large Language Models
Text anonymization is crucial for sharing sensitive data while maintaining privacy.
SymPoint Revolutionized: Boosting Panoptic Symbol Spotting with Layer Feature Enhancement
In particular, we first propose a Layer Feature-Enhanced module (LFE) to encode the graphical layer information into the primitive feature, which significantly boosts the performance.
Compress3D: a Compressed Latent Space for 3D Generation from a Single Image
In this paper, we present a triplane autoencoder, which encodes 3D models into a compact triplane latent space to effectively compress both the 3D geometry and texture information.
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark
The paper introduces SceMQA, a novel benchmark for scientific multimodal question answering at the college entrance level.
Symbol as Points: Panoptic Symbol Spotting via Point-based Representation
Finally, we propose a KNN interpolation mechanism for the mask attention module of the spotting head to better handle primitive mask downsampling, which is primitive-level in contrast to pixel-level for the image.
GPAvatar: Generalizable and Precise Head Avatar from Image(s)
Head avatar reconstruction, crucial for applications in virtual reality, online meetings, gaming, and film industries, has garnered substantial attention within the computer vision community.
A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization Inversion for Zero-Shot Video Editing
This paper presents a video inversion approach for zero-shot video editing, which models the input video with low-rank representation during the inversion process.
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies.
TOSS:High-quality Text-guided Novel View Synthesis from a Single Image
In this paper, we present TOSS, which introduces text to the task of novel view synthesis (NVS) from just a single RGB image.
Consistent123: Improve Consistency for One Image to 3D Object Synthesis
Large image diffusion models enable novel view synthesis with high quality and excellent zero-shot capability.
Scalable Video Object Segmentation with Simplified Framework
The current popular methods for video object segmentation (VOS) implement feature matching through several hand-crafted modules that separately perform feature extraction and matching.
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation
These models also suffer from posterior collapse, i. e., the decoder tends to ignore latent variables and directly access information captured in the encoder through the cross-attention mechanism.
DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks
However, we find that this simple baseline heavily relies on spatial cues while ignoring temporal relations for frame reconstruction, thus leading to sub-optimal temporal matching representations for VOT and VOS.
Ranked #1 on
Visual Object Tracking
on TrackingNet
(AUC metric)
SimVTP: Simple Video Text Pre-training with Masked Autoencoders
This paper presents SimVTP: a Simple Video-Text Pretraining framework via masked autoencoders.
Ranked #25 on
Moment Retrieval
on Charades-STA
Latent Video Diffusion Models for High-Fidelity Long Video Generation
Diffusion models have shown remarkable results recently but require significant computational resources.
Ranked #2 on
Video Generation
on Taichi
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS).
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
LocVTP: Video-Text Pre-training for Temporal Localization
To further enhance the temporal reasoning ability of the learned feature, we propose a context projection head and a temporal aware contrastive loss to perceive the contextual relationships.
Unsupervised Pre-training for Temporal Action Localization Tasks
These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization.
Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding
In this paper, we propose a novel LS method to modify a given text by pivoting it between two different languages and embed secret data by applying a GLS-like information encoding strategy.
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting.
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
To alleviate such bias, we propose \textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others.
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning.
Ranked #8 on
Self-Supervised Action Recognition Linear
on UCF101
ROAM: Recurrently Optimizing Tracking Model
In this paper, we design a tracking model consisting of response generation and bounding box regression, where the first component produces a heat map to indicate the presence of the object at different positions and the second part regresses the relative bounding box shifts to anchors mounted on sliding-window locations.
Visual Tracking via Dynamic Memory Networks
The reading and writing process of the external memory is controlled by an LSTM network with the search feature map as input.
Generative adversarial network based on chaotic time series
Here we utilize chaotic time series generated experimentally by semiconductor lasers for the latent variables of GAN whereby the inherent nature of chaos can be reflected or transformed into the generated output data.
Learning Dynamic Memory Networks for Object Tracking
In this paper, we propose a dynamic memory network to adapt the template to the target's appearance variations during tracking.
Recurrent Filter Learning for Visual Tracking
Recently using convolutional neural networks (CNNs) has gained popularity in visual tracking, due to its robust feature representation of images.
