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Found 14 papers, 5 papers with code
DyST-XL: Dynamic Layout Planning and Content Control for Compositional Text-to-Video Generation
Compositional text-to-video generation, which requires synthesizing dynamic scenes with multiple interacting entities and precise spatial-temporal relationships, remains a critical challenge for diffusion-based models.
CMMCoT: Enhancing Complex Multi-Image Comprehension via Multi-Modal Chain-of-Thought and Memory Augmentation
The construction of interleaved multimodal multi-step reasoning chains, which utilize critical visual region tokens, extracted from intermediate reasoning steps, as supervisory signals.
RectifiedHR: Enable Efficient High-Resolution Image Generation via Energy Rectification
In this paper, we propose RectifiedHR, an straightforward and efficient solution for training-free high-resolution image generation.
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers
Customized generation has achieved significant progress in image synthesis, yet personalized video generation remains challenging due to temporal inconsistencies and quality degradation.
RestorerID: Towards Tuning-Free Face Restoration with ID Preservation
RestorerID is a diffusion model-based method that restores low-quality images with varying levels of degradation by using a single reference image.
Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst complex infrared background clutter.
Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition
In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre-training framework, to integrate both the spatial information and the sequential temporal dynamics into the feature embeddings.
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples.
OmniCLIP: Adapting CLIP for Video Recognition with Spatial-Temporal Omni-Scale Feature Learning
In this paper, we propose OmniCLIP, a framework that adapts CLIP for video recognition by focusing on learning comprehensive features encompassing spatial, temporal, and dynamic spatial-temporal scales, which we refer to as omni-scale features.
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis.
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners
Prompt tuning, which involves training a small set of parameters, effectively enhances the pre-trained Vision-Language Models (VLMs) to downstream tasks.
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
Diffusion models have exhibited substantial success in text-to-image generation.
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images.
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
