Research

OUR SYSTEM DISENTANGLES THIS OBJECTIVE INTO THREE SEQUENTIAL TASKS: (1) FACE VIDEO GENERATION WITH A CANONICAL EXPRESSION; (2) AUDIO-DRIVEN LIP-SYNC; AND (3) FACE ENHANCEMENT FOR IMPROVING PHOTO-REALISM.

EXISTING CLASSIC RES DATASETS AND METHODS COMMONLY SUPPORT SINGLE-TARGET EXPRESSIONS ONLY, I. E., ONE EXPRESSION REFERS TO ONE TARGET OBJECT.

IN ADDITION, THE OVERLAPPING SHARED STRUCTURE HELPS TO QUICKLY LEVERAGE ALL ACQUIRED KNOWLEDGE TO NEW TASKS, EMPOWERING A SINGLE NETWORK CAPABLE OF SUPPORTING MULTIPLE INCREMENTAL TASKS (WITHOUT THE SEPARATE SUB-NETWORK MASK FOR EACH TASK).

CHATBOT LANGUAGE MODELLING +1

BUILDING A MULTI-MODALITY MULTI-TASK NEURAL NETWORK TOWARD ACCURATE AND ROBUST PERFORMANCE IS A DE-FACTO STANDARD IN PERCEPTION TASK OF AUTONOMOUS DRIVING.

TOOLS SERVE AS PIVOTAL INTERFACES THAT ENABLE HUMANS TO UNDERSTAND AND RESHAPE THE WORLD.

IN THIS WORK, WE PROPOSE TO MODEL THE 3D PARAMETER AS A RANDOM VARIABLE INSTEAD OF A CONSTANT AS IN SDS AND PRESENT VARIATIONAL SCORE DISTILLATION (VSD), A PRINCIPLED PARTICLE-BASED VARIATIONAL FRAMEWORK TO EXPLAIN AND ADDRESS THE AFOREMENTIONED ISSUES IN TEXT-TO-3D GENERATION.

WE PROPOSE A UNIFIED PERMUTATION-EQUIVALENT MODELING APPROACH, IE, MODELING MAP ELEMENT AS A POINT SET WITH A GROUP OF EQUIVALENT PERMUTATIONS, WHICH ACCURATELY DESCRIBES THE SHAPE OF MAP ELEMENT AND STABILIZES THE LEARNING PROCESS.

BATGPT IS A LARGE-SCALE LANGUAGE MODEL DESIGNED AND TRAINED JOINTLY BY WUHAN UNIVERSITY AND SHANGHAI JIAO TONG UNIVERSITY.

THE AVAILABILITY OF LARGE, HIGH-QUALITY DATASETS HAS BEEN ONE OF THE MAIN DRIVERS OF RECENT PROGRESS IN QUESTION ANSWERING (QA).

SINCE THE INTRODUCTION OF THE TRANSFORMER MODEL BY VASWANI ET AL. (2017), A FUNDAMENTAL QUESTION HAS YET TO BE ANSWERED: HOW DOES A MODEL ACHIEVE EXTRAPOLATION AT INFERENCE TIME FOR SEQUENCES THAT ARE LONGER THAN IT SAW DURING TRAINING?

WITH THE ADVANCE OF TEXT-TO-IMAGE MODELS (E. G., STABLE DIFFUSION) AND CORRESPONDING PERSONALIZATION TECHNIQUES SUCH AS DREAMBOOTH AND LORA, EVERYONE CAN MANIFEST THEIR IMAGINATION INTO HIGH-QUALITY IMAGES AT AN AFFORDABLE COST.

BY CONTRAST, HUMANS CAN GENERALLY PERFORM A NEW LANGUAGE TASK FROM ONLY A FEW EXAMPLES OR FROM SIMPLE INSTRUCTIONS - SOMETHING WHICH CURRENT NLP SYSTEMS STILL LARGELY STRUGGLE TO DO.

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.

BASED ON THE ABOVE IDEA, WE PROPOSE A NOVEL TRANSFORMER MODEL, DUAL AGGREGATION TRANSFORMER (DAT), FOR IMAGE SR. OUR DAT AGGREGATES FEATURES ACROSS SPATIAL AND CHANNEL DIMENSIONS, IN THE INTER-BLOCK AND INTRA-BLOCK DUAL MANNER.