Research

WE PRESENT MAGIC123, A TWO-STAGE COARSE-TO-FINE APPROACH FOR HIGH-QUALITY, TEXTURED 3D MESHES GENERATION FROM A SINGLE UNPOSED IMAGE IN THE WILD USING BOTH2D AND 3D PRIORS.

IN ADDITION, WE FIND VALL-E COULD PRESERVE THE SPEAKER'S EMOTION AND ACOUSTIC ENVIRONMENT OF THE ACOUSTIC PROMPT IN SYNTHESIS.

SINGLE IMAGE 3D RECONSTRUCTION IS AN IMPORTANT BUT CHALLENGING TASK THAT REQUIRES EXTENSIVE KNOWLEDGE OF OUR NATURAL WORLD.

RECENTLY, THE DETR FRAMEWORK HAS EMERGED AS THE DOMINANT APPROACH FOR HUMAN--OBJECT INTERACTION (HOI) RESEARCH.

RECENT ADVANCEMENTS IN NEURAL VOCODING ARE PREDOMINANTLY DRIVEN BY GENERATIVE ADVERSARIAL NETWORKS (GANS) OPERATING IN THE TIME-DOMAIN.

MOST METHODS IN THIS DIRECTION DEVELOP TASKSPECIFIC MODELS THAT ARE TRAINED WITH TYPE-SPECIFIC LABELS, SUCH AS MOMENT RETRIEVAL (TIME INTERVAL) AND HIGHLIGHT DETECTION (WORTHINESS CURVE), WHICH LIMITS THEIR ABILITIES TO GENERALIZE TO VARIOUS VTG TASKS AND LABELS.

WE DEVELOP A PROCEDURE FOR INT8 MATRIX MULTIPLICATION FOR FEED-FORWARD AND ATTENTION PROJECTION LAYERS IN TRANSFORMERS, WHICH CUT THE MEMORY NEEDED FOR INFERENCE BY HALF WHILE RETAINING FULL PRECISION PERFORMANCE.

ADDING TRAINABLE MEMORY TO SELECTIVELY STORE LOCAL AS WELL AS GLOBAL REPRESENTATIONS OF A SEQUENCE IS A PROMISING DIRECTION TO IMPROVE THE TRANSFORMER MODEL.

OUR SSLREC PLATFORM COVERS A COMPREHENSIVE SET OF STATE-OF-THE-ART SSL-ENHANCED RECOMMENDATION MODELS ACROSS DIFFERENT SCENARIOS, ENABLING RESEARCHERS TO EVALUATE THESE CUTTING-EDGE MODELS AND DRIVE FURTHER INNOVATION IN THE FIELD.

MULTI-AGENT REINFORCEMENT LEARNING (MARL) HAS BEEN SHOWN EFFECTIVE FOR COOPERATIVE GAMES IN RECENT YEARS.

IN THE PROCESS, WE SPEED UP IMAGE GENERATION BY UP TO 2X AND REDUCE MEMORY CONSUMPTION BY UP TO 5. 6X.

WE INTRODUCE GPT-NEOX-20B, A 20 BILLION PARAMETER AUTOREGRESSIVE LANGUAGE MODEL TRAINED ON THE PILE, WHOSE WEIGHTS WILL BE MADE FREELY AND OPENLY AVAILABLE TO THE PUBLIC THROUGH A PERMISSIVE LICENSE.

TO EFFECTIVELY FUSE LANGUAGE AND VISION MODALITIES, WE CONCEPTUALLY DIVIDE A CLOSED-SET DETECTOR INTO THREE PHASES AND PROPOSE A TIGHT FUSION SOLUTION, WHICH INCLUDES A FEATURE ENHANCER, A LANGUAGE-GUIDED QUERY SELECTION, AND A CROSS-MODALITY DER FOR CROSS-MODALITY FUSION.

IN THIS NOTE, WE EXPLORE INFERENCE-TIME ALIGNMENT THROUGH IN-CONTEXT LEARNING.

ON THE ONE HAND, EACH QUERY IS GENERATED BASED ON 2D LANE-AWARE FEATURES AND ADOPTS A HYBRID EMBEDDING TO ENHANCE THE LANE INFORMATION.

WE PRESENT C-EVAL, THE FIRST COMPREHENSIVE CHINESE EVALUATION SUITE DESIGNED TO ASSESS ADVANCED KNOWLEDGE AND REASONING ABILITIES OF FOUNDATION MODELS IN A CHINESE CONTEXT.

THE PROPOSED FC-CLIP, BENEFITS FROM THE FOLLOWING OBSERVATIONS: THE FROZEN CLIP BACKBONE MAINTAINS THE ABILITY OF OPEN-VOCABULARY CLASSIFICATION AND CAN ALSO SERVE AS A STRONG MASK GENERATOR, AND THE CONVOLUTIONAL CLIP GENERALIZES WELL TO A LARGER INPUT RESOLUTION THAN THE ONE USED DURING CONTRASTIVE IMAGE-TEXT PRETRAINING.

THE FIVE-DOLLAR MODEL IS A LIGHTWEIGHT TEXT-TO-IMAGE GENERATIVE ARCHITECTURE THAT GENERATES LOW DIMENSIONAL IMAGES FROM AN END TEXT PROMPT.

TO ADDRESS THE CHALLENGES OF ACHIEVING AUTONOMOUS COOPERATION, WE PROPOSE A NOVEL COMMUNICATIVE AGENT FRAMEWORK NAMED ROLE-PLAYING.

WE PRESENT SADTALKER, WHICH GENERATES 3D MOTION COEFFICIENTS (HEAD POSE, EXPRESSION) OF THE 3DMM FROM AUDIO AND IMPLICITLY MODULATES A NOVEL 3D-AWARE FACE RENDER FOR TALKING HEAD GENERATION.