Research

OKAPI INTRODUCES INSTRUCTION AND RESPONSE-RANKED DATA IN 26 DIVERSE LANGUAGES TO FACILITATE THE EXPERIMENTS AND DEVELOPMENT OF FUTURE MULTILINGUAL LLM RESEARCH.

MULTIMODAL LARGE LANGUAGE MODEL (MLLM) RECENTLY HAS BEEN A NEW RISING RESEARCH HOTSPOT, WHICH USES POWERFUL LARGE LANGUAGE MODELS (LLMS) AS A BRAIN TO PERFORM MULTIMODAL TASKS.

WE PRESENT SDXL, A LATENT DIFFUSION MODEL FOR TEXT-TO-IMAGE SYNTHESIS.

THIS TUTORIAL NOTE SUMMARIZES THE PRESENTATION ON ``LARGE MULTIMODAL MODELS: TOWARDS BUILDING AND SURPASSING MULTIMODAL GPT-4'', A PART OF CVPR 2023 TUTORIAL ON ``RECENT ADVANCES IN VISION FOUNDATION MODELS''.

IN THIS , WE INTRODUCE SEMANTIC-SAM, A UNIVERSAL IMAGE SEGMENTATION MODEL TO ENABLE SEGMENT AND RECOGNIZE ANYTHING AT ANY DESIRED GRANULARITY.

IN RESPONSE TO THESE CHALLENGES, WE PROPOSE MMBENCH, A NOVEL MULTI-MODALITY BENCHMARK.

INSPIRED BY THE PROMPT LEARNING IN NATURAL LANGUAGE PROCESSING (NLP), WHICH HAS PRESENTED SIGNIFICANT EFFECTIVENESS IN LEVERAGING PRIOR KNOWLEDGE FOR VARIOUS NLP TASKS, WE STUDY THE PROMPTING TOPIC FOR GRAPHS WITH THE MOTIVATION OF FILLING THE GAP BETWEEN PRE-TRAINED MODELS AND VARIOUS GRAPH TASKS.

WE PRESENT THE ALL-SEEING (AS) PROJECT: A LARGE-SCALE DATA AND MODEL FOR RECOGNIZING AND UNDERSTANDING EVERYTHING IN THE OPEN WORLD.

WE PRESENT NNVISR - AN OPEN-SOURCE FILTER PLUGIN FOR THE VAPOURSYNTH VIDEO PROCESSING FRAMEWORK, WHICH FACILITATES THE APPLICATION OF NEURAL NETWORKS FOR VARIOUS KINDS OF VIDEO ENHANCING TASKS, INCLUDING DENOISING, SUPER RESOLUTION, INTERPOLATION, AND SPATIO-TEMPORAL SUPER-RESOLUTION.

IN THIS WORK, WE AIM TO BRIDGE THIS GAP BY CONDUCTING A RETROSPECTIVE ANALYSIS OF RECENT WORKS IN OFFLINE RL AND PROPOSE REBRAC, A MINIMALISTIC ALGORITHM THAT INTEGRATES SUCH DESIGN ELEMENTS BUILT ON TOP OF THE TD3+BC METHOD.

ACCURATE SAMPLING IS IMPORTANT TO PROVIDE A PRECISE COUPLING OF GEOMETRY AND RADIANCE; AND (III) IT ALLOWS EFFICIENT UNSUPERVISED DISENTANGLEMENT OF SHAPE AND APPEARANCE IN VOLUME RENDERING.

LARGE LANGUAGE MODELS (LLMS) HAVE SEEN AN IMPRESSIVE WAVE OF ADVANCES RECENTLY, WITH MODELS NOW EXCELLING IN A VARIETY OF TASKS, SUCH AS MATHEMATICAL REASONING AND PROGRAM SYNTHESIS.

WE INTRODUCE LLAMA, A COLLECTION OF FOUNDATION LANGUAGE MODELS RANGING FROM 7B TO 65B PARAMETERS.

THIS REPORT PRESENTS THE TECHNICAL DETAILS OF OUR SUBMISSION ON THE EGO4D AUDIO-VISUAL (AV) AUTOMATIC SPEECH RECOGNITION CHALLENGE 2023 FROM THE OXFORDVGG TEAM.

LARGE LANGUAGE MODEL (LLM) BASED GENERATIVE AI SYSTEMS HAVE SEEN SIGNIFICANT PROGRESS IN RECENT YEARS.

WE TACKLE THE TASK OF CONDITIONAL MUSIC GENERATION.

MULTI-QUERY ATTENTION (MQA), WHICH ONLY USES A SINGLE KEY-VALUE HEAD, DRASTICALLY SPEEDS UP DER INFERENCE.

MOST VISUAL RECOGNITION STUDIES RELY HEAVILY ON CROWD-LABELLED DATA IN DEEP NEURAL NETWORKS (DNNS) TRAINING, AND THEY USUALLY TRAIN A DNN FOR EACH SINGLE VISUAL RECOGNITION TASK, LEADING TO A LABORIOUS AND TIME-CONSUMING VISUAL RECOGNITION PARADIGM.

ADDING THESE DATA IN A LIGHTWEIGHT FINETUNING STEP CAN SIGNIFICANTLY REDUCE SYCOPHANTIC BEHAVIOR ON HELD-OUT PROMPTS.

CALIBRATION-BASED METHODS HAVE DOMINATED RAW IMAGE DENOISING UNDER EXTREMELY LOW-LIGHT ENVIRONMENTS.