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Found 18 papers, 10 papers with code
ReactDiff: Latent Diffusion for Facial Reaction Generation
Given the audio-visual clip of the speaker, facial reaction generation aims to predict the listener's facial reactions.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Med-LEGO: Editing and Adapting toward Generalist Medical Image Diagnosis
The adoption of visual foundation models has become a common practice in computer-aided diagnosis (CAD).
MITracker: Multi-View Integration for Visual Object Tracking
Multi-view object tracking (MVOT) offers promising solutions to challenges such as occlusion and target loss, which are common in traditional single-view tracking.
OpenOmni: Large Language Models Pivot Zero-shot Omnimodal Alignment across Language with Real-time Self-Aware Emotional Speech Synthesis
In the alignment phase, a pre-trained speech model is further trained on text-image tasks to generalize from vision to speech in a (near) zero-shot manner, outperforming models trained on tri-modal datasets.
Mitigating Hallucination for Large Vision Language Model by Inter-Modality Correlation Calibration Decoding
In this method, we design a Cross-Modal Value-Enhanced Decoding(CMVED) module to alleviate hallucination by a novel contrastive decoding mechanism.
Benchmarking Large Language Models for Image Classification of Marine Mammals
Further progress has been made in multimodal LLMs, with many datasets created to evaluate LLMs with vision abilities.
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation
To bridge this gap, we propose a data augmentation approach and introduce PersonaMathQA, a dataset derived from MATH and GSM8K, on which we train the PersonaMath models.
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
Specifically, Ruler equips LLMs with the ability to generate responses of a specified length based on length constraints within the instructions.
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs
We applied the HCP strategy in experiments with six Repo-Code LLMs, and the results demonstrate that our proposed method can significantly enhance completion accuracy while substantially reducing the length of input.
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks.
Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection
Specifically, we devise three modules: Background Category-specific Prompt, Background Object Discovery, and Inference Probability Rectification, to empower the detector to discover, represent, and leverage implicit object knowledge explored from background proposals.
Instruction-Guided Visual Masking
To achieve more accurate and nuanced multimodal instruction following, we introduce Instruction-guided Visual Masking (IVM), a new versatile visual grounding model that is compatible with diverse multimodal models, such as LMM and robot model.
Ranked #1 on
Visual Question Answering
on V*bench
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection
In this paper, we propose a novel face protection approach, dubbed DiffAM, which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images.
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.
Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.
Single-pixel imaging based on deep learning
Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors.
Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection
Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries.
