Advanced Deep Learning

UNLIKE TRADITIONAL MULTI-VIEW STEREO APPROACHES, THE NEURAL IMPLICIT SURFACE-BASED METHODS LEVERAGE NEURAL NETWORKS TO REPRESENT 3D SCENES AS SIGNED DISTANCE FUNCTIONS (SDFS).

FINE-TUNING LANGUAGE MODELS (LMS) HAS YIELDED SUCCESS ON DIVERSE DOWNSTREAM TASKS, BUT AS LMS GROW IN SIZE, BACKPROPAGATION REQUIRES A PROHIBITIVELY LARGE AMOUNT OF MEMORY.

IN OUR EXPERIMENTS, WE TREAT DISCRETE ACOUSTIC S AS TEXTUAL DATA AND TRAIN A MASKED LANGUAGE MODEL USING A CLOZE-LIKE METHODOLOGY, ULTIMATELY DERIVING HIGH-QUALITY AUDIO REPRESENTATIONS.

IN THIS , WE EXPLORE THE POSSIBILITY OF BOOSTING DEEP 3D POINT CLOUD ENRS BY TRANSFERRING VISUAL KNOWLEDGE EXTRACTED FROM DEEP 2D IMAGE ENRS UNDER A STANDARD TEACHER-STUDENT DISTILLATION WORKFLOW.

APPLICATIONS BUILT ON TOP OF LARGE LANGUAGE MODELS (LLMS) SUCH AS GPT-4 REPRESENT A REVOLUTION IN AI DUE TO THEIR HUMAN-LEVEL CAPABILITIES IN NATURAL LANGUAGE PROCESSING.

TEXT EVALUATION HAS HISTORICALLY POSED SIGNIFICANT CHALLENGES, OFTEN DEMANDING SUBSTANTIAL LABOR AND TIME COST.

TRACKING 3D OBJECTS ACCURATELY AND CONSISTENTLY IS CRUCIAL FOR AUTONOMOUS VEHICLES, ENABLING MORE RELIABLE DOWNSTREAM TASKS SUCH AS TRAJECTORY PREDICTION AND MOTION PLANNING.

WE OBSERVE THAT THE INEFFICIENCY IS DUE TO SUBOPTIMAL WORK PARTITIONING BETWEEN DIFFERENT THREAD BLOCKS AND WARPS ON THE GPU, CAUSING EITHER LOW-OCCUPANCY OR UNNECESSARY SHARED MEMORY READS/WRITES.

THE MISUSE OF LARGE LANGUAGE MODELS (LLMS) HAS GARNERED SIGNIFICANT ATTENTION FROM THE GENERAL PUBLIC AND LLM VENDORS.

MS3D++ PROVIDES A STRAIGHTFORWARD APPROACH TO DOMAIN ADAPTATION BY GENERATING HIGH-QUALITY PSEUDO-LABELS, ENABLING THE ADAPTATION OF 3D DETECTORS TO A DIVERSE RANGE OF LIDAR TYPES, REGARDLESS OF THEIR DENSITY.

LCENSEMBLE IS A HIGH-PERFORMING, SCALABLE AND USER-FRIENDLY PYTHON PACKAGE FOR THE GENERAL TASKS OF CLASSIFICATION AND REGRESSION.

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.

WE PRESENT ARTRACK, AN AUTOREGRESSIVE FRAMEWORK FOR VISUAL OBJECT TRACKING.

HQ-SAM IS ONLY TRAINED ON THE INTRODUCED DETASET OF 44K MASKS, WHICH TAKES ONLY 4 S ON 8 GPUS.

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.