Search Results for author:
Found 25 papers, 16 papers with code
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution
The M\"obius transformation is typically employed to further provide the opportunity for movement and zoom on ODIs, but applying it to the image level often results in blurry effect and aliasing problem.
HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video
Our method enables pausing the video at any frame and rendering all scene details (dynamic humans, objects, and backgrounds) from arbitrary viewpoints.
MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing
Despite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results.
Ranked #11 on
Text-based Image Editing
on PIE-Bench
VMesh: Hybrid Volume-Mesh Representation for Efficient View Synthesis
With the emergence of neural radiance fields (NeRFs), view synthesis quality has reached an unprecedented level.
Accelerating Vision-Language Pretraining with Free Language Modeling
FLM successfully frees the prediction rate from the tie-up with the corruption rate while allowing the corruption spans to be customized for each token to be predicted.
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly.
RILS: Masked Visual Reconstruction in Language Semantic Space
Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training.
ViLEM: Visual-Language Error Modeling for Image-Text Retrieval
ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information.
Ranked #45 on
Visual Reasoning
on Winoground
Order-Prompted Tag Sequence Generation for Video Tagging
This paper proposes a novel generative model, Order-Prompted Tag Sequence Generation (OP-TSG), according to the above characteristics.
Dream3D: Zero-Shot Text-to-3D Synthesis Using 3D Shape Prior and Text-to-Image Diffusion Models
Specifically, we first generate a high-quality 3D shape from the input text in the text-to-shape stage as a 3D shape prior.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
To replicate the success of text-to-image (T2I) generation, recent works employ large-scale video datasets to train a text-to-video (T2V) generator.
Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis
Vector-Quantized (VQ-based) generative models usually consist of two basic components, i. e., VQ tokenizers and generative transformers.
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
CAT-ART boosts the recommendation performance in any target domain through the combined use of the learned global user representation and knowledge transferred from other domains, in addition to the original user embedding in the target domain.
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.
DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes
In this paper, we present DeVRF, a novel representation to accelerate learning dynamic radiance fields.
Masked Image Modeling with Denoising Contrast
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up.
MILES: Visual BERT Pre-training with Injected Language Semantics for Video-text Retrieval
Dominant pre-training work for video-text retrieval mainly adopt the "dual-encoder" architectures to enable efficient retrieval, where two separate encoders are used to contrast global video and text representations, but ignore detailed local semantics.
Ranked #7 on
Zero-Shot Video Retrieval
on MSVD
UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection
Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era.
Ranked #3 on
Highlight Detection
on YouTube Highlights
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
All in One: Exploring Unified Video-Language Pre-training
In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.
Ranked #6 on
TGIF-Transition
on TGIF-QA
(using extra training data)
Bridging Video-text Retrieval with Multiple Choice Questions
As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e. g., action recognition with linear evaluation.
Ranked #8 on
Zero-Shot Video Retrieval
on MSVD
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild
Current researches mainly focus on only English characters and digits, while few work studies Chinese characters due to the lack of public large-scale and high-quality Chinese datasets, which limits the practical application scenarios of text segmentation.
Object-aware Video-language Pre-training for Retrieval
In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.
Ranked #20 on
Zero-Shot Video Retrieval
on DiDeMo
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.
Optimization and Control Applications
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Thus, in this paper, we exploit adaptive dispatching intervals to boost the platform's profit under a guarantee of the maximum passenger waiting time.
