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Found 20 papers, 8 papers with code
Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion Models
Recent advances in diffusion models have demonstrated remarkable capabilities in video generation.
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos
Specifically, we perform one-step denoising to convert initial noises into a clip and subsequently evaluate its long-term value, leveraging a reward model anchored by previously generated content.
Temporal Regularization Makes Your Video Generator Stronger
Temporal quality is a critical aspect of video generation, as it ensures consistent motion and realistic dynamics across frames.
VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention
Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines.
LightGen: Efficient Image Generation through Knowledge Distillation and Direct Preference Optimization
Recent advances in text-to-image generation have primarily relied on extensive datasets and parameter-heavy architectures.
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses.
VideoGen-of-Thought: A Collaborative Framework for Multi-Shot Video Generation
Cross-Shot Consistency: We ensure temporal and identity consistency by leveraging identity-preserving (IP) embeddings across shots, which are automatically created from the narrative.
GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation
The `out-of-the-box' insight of GoodSAM++ is to introduce a teacher assistant (TA) to provide semantic information for SAM, integrated with SAM to obtain reliable pseudo semantic maps to bridge both domain and capacity gaps.
Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
To this end, we manually construct a benchmark with 12 categories and design evaluation metrics that assess the degree of error in MLLM responses even when the visual content is seemingly understood.
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging
EyeFound provides a generalizable solution to improve model performance and lessen the annotation burden on experts, facilitating widespread clinical AI applications for retinal imaging.
Efficient Multimodal Large Language Models: A Survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering, visual understanding and reasoning.
Evaluating large language models in medical applications: a survey
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine.
Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality
Our system enables users to effortlessly locate and zoom in on the objects of interest in VR.
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment
Specifically, to address the first challenge, we propose a pseudo-label correction strategy that utilizes a Beta Mixture Model to predict the probability of mis-clustering based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning.
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
To this end, we propose a novel framework, called GoodSAM, that introduces a teacher assistant (TA) to provide semantic information, integrated with SAM to generate ensemble logits to achieve knowledge transfer.
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
To alleviate artifacts and improve quality of synthetic images, we fine-tune Vision-Language Model (VLM) as artifact classifier to automatically identify and classify a wide range of artifacts and provide supervision for further optimizing generative models.
Efficient Multimodal Learning from Data-centric Perspective
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks.
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation.
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively.
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign) of the intensity changes.
