Search Results for author:
Found 23 papers, 10 papers with code
InfiniDreamer: Arbitrarily Long Human Motion Generation via Segment Score Distillation
InfiniDreamer addresses the limitations of current motion generation methods, which are typically restricted to short sequences due to the lack of long motion training data.
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks
A Uni-Controlled Image Generation Module is then developed to create high-quality synthetic images that are controllable and based on the generated layouts.
Imagine and Seek: Improving Composed Image Retrieval with an Imagined Proxy
In this paper, we introduce Imagined Proxy for CIR (IP-CIR), a training-free method that creates a proxy image aligned with the query image and text description, enhancing query representation in the retrieval process.
Ranked #1 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRR
Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models
We use GPT4V to bridge the gap between the reference image and the text input for the T2I model, allowing T2I models to understand image content.
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion
Despite growing interest in T2M, few methods focus on safeguarding these models against adversarial attacks, with existing work on text-to-image models proving insufficient for the unique motion domain.
VividDreamer: Invariant Score Distillation For Hyper-Realistic Text-to-3D Generation
In this paper, SDS is decoupled into a weighted sum of two components: the reconstruction term and the classifier-free guidance term.
MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis
Lastly, we introduced the Consistent-MIG algorithm to enhance the iterative MIG ability of MIGC and MIGC++.
Clustering for Protein Representation Learning
We select the highest-scoring clusters and use their medoid nodes for the next iteration of clustering, until we obtain a hierarchical and informative representation of the protein.
Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity
Reconstructing the viewed images from human brain activity bridges human and computer vision through the Brain-Computer Interface.
Knowledge-Enhanced Dual-stream Zero-shot Composed Image Retrieval
The pseudo-word tokens generated in this stream are explicitly aligned with fine-grained semantics in the text embedding space.
LSK3DNet: Towards Effective and Efficient 3D Perception with Large Sparse Kernels
Consequently, it is essential to develop LiDAR perception methods that are both efficient and effective.
HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting
Extensive experiments demonstrate the efficacy of HeadStudio in generating animatable avatars from textual prompts, exhibiting appealing appearances.
MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis
Lastly, we aggregate all the shaded instances to provide the necessary information for accurately generating multiple instances in stable diffusion (SD).
Ranked #1 on
Conditional Text-to-Image Synthesis
on COCO-MIG
CapHuman: Capture Your Moments in Parallel Universes
Moreover, we introduce the 3D facial prior to equip our model with control over the human head in a flexible and 3D-consistent manner.
VISTA-LLAMA: Reducing Hallucination in Video Language Models via Equal Distance to Visual Tokens
This amplifies the effect of visual tokens on text generation especially when the relative distance is longer between visual and text tokens.
Vista-LLaMA: Reliable Video Narrator via Equal Distance to Visual Tokens
This amplifies the effect of visual tokens on text generation, especially when the relative distance is longer between visual and text tokens.
Ranked #2 on
Question Answering
on NExT-QA (Open-ended VideoQA)
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Towards this goal, we propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature Adapting and Blending, which transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks.
Ranked #1 on
Visual Question Answering (VQA)
on MSVD-QA
(using extra training data)
Temporal Perceiving Video-Language Pre-training
Specifically, text-video localization consists of moment retrieval, which predicts start and end boundaries in videos given the text description, and text localization which matches the subset of texts with the video features.
Unified Transformer Tracker for Object Tracking
Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking.
Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries
Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video.
Ranked #10 on
Referring Expression Segmentation
on J-HMDB
SF-Net: Single-Frame Supervision for Temporal Action Localization
To obtain the single-frame supervision, the annotators are asked to identify only a single frame within the temporal window of an action.
Ranked #5 on
Weakly Supervised Action Localization
on BEOID
Self-Paced Co-training
During co-training process, labels of unlabeled instances in the training pool are very likely to be false especially in the initial training rounds, while the standard co-training algorithm utilizes a “draw without replacement” manner and does not remove these false labeled instances from training.
Few-Example Object Detection with Model Communication
Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
Ranked #1 on
Weakly Supervised Object Detection
on MS COCO
