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Found 24 papers, 8 papers with code
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
Ranked #1 on
Zero-Shot Video Question Answer
on TGIF-QA
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+4
MagicVideo-V2: Multi-Stage High-Aesthetic Video Generation
The growing demand for high-fidelity video generation from textual descriptions has catalyzed significant research in this field.
ChatAnything: Facetime Chat with LLM-Enhanced Personas
For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically.
Towards Garment Sewing Pattern Reconstruction from a Single Image
In this work, we explore the challenging problem of recovering garment sewing patterns from daily photos for augmenting these applications.
Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models
We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models.
BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs
Our experiments show that BuboGPT achieves impressive multi-modality understanding and visual grounding abilities during the interaction with human.
DATE: Domain Adaptive Product Seeker for E-commerce
Then, we present two cooperative seekers to simultaneously search the image for PR and localize the product for PG.
Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale Upsampling
Recently, several methods have explored the potential of multi-contrast magnetic resonance imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR methods.
Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models
These imply that the gap corresponds to the lost information of the image, and we can reconstruct the image by filling the gap.
Pseudo Numerical Methods for Diffusion Models on Manifolds
Under such a perspective, we propose pseudo numerical methods for diffusion models (PNDMs).
Ranked #11 on
Image Generation
on CelebA 64x64
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples
In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance.
ST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models
Score-based generative models involve sequentially corrupting the data distribution with noise and then learns to recover the data distribution based on score matching.
SimulLR: Simultaneous Lip Reading Transducer with Attention-Guided Adaptive Memory
Lip reading, aiming to recognize spoken sentences according to the given video of lip movements without relying on the audio stream, has attracted great interest due to its application in many scenarios.
Cascaded Prediction Network via Segment Tree for Temporal Video Grounding
Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video.
Learning to Rehearse in Long Sequence Memorization
Further, we design a history sampler to select informative fragments for rehearsal training, making the memory focus on the crucial information.
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML
Model Agnostic Meta-Learning~(MAML)~(\cite{finn2017model}) is one of the most well-known gradient-based meta learning algorithms, that learns the meta-initialization through the inner and outer optimization loop.
Continual Memory: Can We Reason After Long-Term Memorization?
Existing reasoning tasks often follow the setting of "reasoning while experiencing", which has an important assumption that the raw contents can be always accessed while reasoning.
Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding
Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.
Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos
Thus, these methods fail to distinguish the target moment from plausible negative moments.
Weakly-Supervised Video Moment Retrieval via Semantic Completion Network
Video moment retrieval is to search the moment that is most relevant to the given natural language query.
Localizing Unseen Activities in Video via Image Query
Thus, we consider a new task to localize unseen activities in videos via image queries, named Image-Based Activity Localization.
Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks
Concretely, we first develop a hierarchical convolutional self-attention encoder to efficiently model long-form video contents, which builds the hierarchical structure for video sequences and captures question-aware long-range dependencies from video context.
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos
Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query.
