Search Results for author:
Found 15 papers, 8 papers with code
Dynamic-I2V: Exploring Image-to-Video Generaion Models via Multimodal LLM
Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios.
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
A total of 211 participants have registered in the structure track.
Improved Visual-Spatial Reasoning via R1-Zero-Like Training
Increasing attention has been placed on improving the reasoning capacities of multi-modal large language models (MLLMs).
MergeVQ: A Unified Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization
Masked Image Modeling (MIM) with Vector Quantization (VQ) has achieved great success in both self-supervised pre-training and image generation.
H2VU-Benchmark: A Comprehensive Benchmark for Hierarchical Holistic Video Understanding
To tackle this challenge, we propose a hierarchical and holistic video understanding (H2VU) benchmark designed to evaluate both general video and online streaming video comprehension.
Layton: Latent Consistency Tokenizer for 1024-pixel Image Reconstruction and Generation by 256 Tokens
Experiments demonstrate Layton's superiority in high-fidelity reconstruction, with 10. 8 reconstruction Frechet Inception Distance on MSCOCO-2017 5K benchmark for 1024x1024 image reconstruction.
PainterNet: Adaptive Image Inpainting with Actual-Token Attention and Diverse Mask Control
Recently, diffusion models have exhibited superior performance in the area of image inpainting.
FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
Targeting addressing such an issue, we first assess the face quality of generations from popular pre-trained DMs with the aid of human annotators and then evaluate the alignment between existing metrics with human judgments.
TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
Distilling latent diffusion models (LDMs) into ones that are fast to sample from is attracting growing research interest.
Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification
In the task-specific evaluation setting, gallery sets are categorized according to specific ReID tasks.
LAPTOP-Diff: Layer Pruning and Normalized Distillation for Compressing Diffusion Models
To this end, we proposed the layer pruning and normalized distillation for compressing diffusion models (LAPTOP-Diff).
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation
In contrast to vanilla consistency distillation (CD) which distills the ordinary differential equation solvers-based sampling process of a pretrained teacher model into a student, SCott explores the possibility and validates the efficacy of integrating stochastic differential equation (SDE) solvers into CD to fully unleash the potential of the teacher.
PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation
In this paper, we are inspired to propose a simple plug-and-play language transfer method based on knowledge distillation.
Cross-lingual Text-to-Image Generation
Knowledge Distillation
+2
Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions.
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
Specifically, we propose a \textbf{HumanBench} based on existing datasets to comprehensively evaluate on the common ground the generalization abilities of different pretraining methods on 19 datasets from 6 diverse downstream tasks, including person ReID, pose estimation, human parsing, pedestrian attribute recognition, pedestrian detection, and crowd counting.
Ranked #1 on
Pedestrian Attribute Recognition
on PA-100K
(using extra training data)
