Advanced Deep Learning

AS LARGE LANGUAGE MODELS IMPROVE, THERE IS INCREASING INTEREST IN TECHNIQUES THAT LEVERAGE THESE MODELS' CAPABILITIES TO REFINE THEIR OWN OUTPUTS.

TO THIS END, WE PROPOSE A BANK OF 3D-AWARE HIERARCHICAL FEATURES, INCLUDING GLOBAL, POINT-LEVEL, AND PIXEL-ALIGNED FEATURES, TO FACILITATE INFORMATIVE ENCODING.

BASED ON THIS OBSERVATION, WE PROPOSE A DATA SELECTOR BASED ON INSTAG TO SELECT 6K DIVERSE AND COMPLEX SAMPLES FROM OPEN-SOURCE DATASETS AND FINE-TUNE MODELS ON INSTAG-SELECTED DATA.

OUR APPROACH, NAMELY DIFFUSION-BASED CONDITIONAL INPAINTING FOR VIRTUAL TRY-ON (DCI-VTON), EFFECTIVELY UTILIZES THE POWER OF THE DIFFUSION MODEL, AND THE INCORPORATION OF THE WARPING MODULE HELPS TO PRODUCE HIGH-QUALITY AND REALISTIC VIRTUAL TRY-ON RESULTS.

RECENT ADVANCES IN LARGE LANGUAGE MODELS (LLMS) HAVE ACHIEVED REMARKABLE BREAKTHROUGHS IN UNDERSTANDING AND RESPONDING TO USER INTENTS.

IN THIS , WE DRAW INSPIRATION FROM ALBERTO ELFES' PIONEERING WORK IN 1989, WHERE HE INTRODUCED THE CONCEPT OF THE OCCUPANCY GRID AS WORLD MODELS FOR ROBOTS.

LARGE LANGUAGE MODELS (LLMS) DEMONSTRATE REMARKABLE ABILITY TO COMPREHEND, REASON, AND GENERATE FOLLOWING NATURE LANGUAGE INSTRUCTIONS.

DATABASE ADMINISTRATORS (DBAS) PLAY A CRUCIAL ROLE IN MANAGING, MAINTAINING AND OPTIMIZING A DATABASE SYSTEM TO ENSURE DATA AVAILABILITY, PERFORMANCE, AND RELIABILITY.

IN THIS WORK, WE DEVELOP A UNIVERSAL SOLUTION TO VPR -- A TECHNIQUE THAT WORKS ACROSS A BROAD RANGE OF STRUCTURED AND UNSTRUCTURED ENVIRONMENTS (URBAN, OUTDOORS, INDOORS, AERIAL, UNDERWATER, AND SUBTERRANEAN ENVIRONMENTS) WITHOUT ANY RE-TRAINING OR FINE-TUNING.

IN THIS WORK, WE DEVELOP AND RELEASE LLAMA 2, A COLLECTION OF PRETRAINED AND FINE-TUNED LARGE LANGUAGE MODELS (LLMS) RANGING IN SCALE FROM 7 BILLION TO 70 BILLION PARAMETERS.

SYNTHETIC IMAGE DATASETS OFFER UNMATCHED ADVANTAGES FOR DESIGNING AND EVALUATING DEEP NEURAL NETWORKS: THEY MAKE IT POSSIBLE TO (I) RENDER AS MANY DATA SAMPLES AS NEEDED, (II) PRECISELY CONTROL EACH SCENE AND YIELD GRANULAR GROUND TRUTH LABELS (AND CAPTIONS), (III) PRECISELY CONTROL DISTRIBUTION SHIFTS BETWEEN TRAINING AND TESTING TO ISOLATE VARIABLES OF INTEREST FOR SOUND EXPERIMENTATION.

THERE HAS BEEN AN INCREASING RESEARCH INTEREST IN DEVELOPING SPECIALIZED DIALOGUE SYSTEMS THAT CAN OFFER MENTAL HEALTH SUPPORT.

DIFFERENT FROM THE PREVIOUS SELF-KNOWLEDGE DISTILLATION, THIS STAGE FINETUNES THE STUDENT'S HEAD WITH ONLY 20% TRAINING TIME AS A PLUG-AND-PLAY TRAINING STRATEGY.

WE, FOR THE FIRST TIME, PROPOSE AN IMAGE RETRIEVAL PARADIGM LEVERAGING GLOBAL FEATURE ONLY TO ENABLE ACCURATE AND LIGHTWEIGHT IMAGE RETRIEVAL FOR BOTH COARSE RETRIEVAL AND RERANKING, THUS THE NAME - SUPERGLOBAL.

THIS EVOLUTION REQUIRES A COMBINATION OF BOTH TRADITIONAL METHODS (SUCH AS TERM-BASED SPARSE RETRIEVAL METHODS WITH RAPID RESPONSE) AND MODERN NEURAL ARCHITECTURES (SUCH AS LANGUAGE MODELS WITH POWERFUL LANGUAGE UNDERSTANDING CAPACITY).

JOINTLY PROCESSING INFORMATION FROM MULTIPLE SENSORS IS CRUCIAL TO ACHIEVING ACCURATE AND ROBUST PERCEPTION FOR RELIABLE AUTONOMOUS DRIVING SYSTEMS.

WE PRESENT THE CONTENT DEFORMATION FIELD F AS A NEW TYPE OF VIDEO REPRESENTATION, WHICH CONSISTS OF A CANONICAL CONTENT FIELD AGGREGATING THE STATIC CONTENTS IN THE ENTIRE VIDEO AND A TEMPORAL DEFORMATION FIELD RECORDING THE TRANSFORMATIONS FROM THE CANONICAL IMAGE (I. E., RENDERED FROM THE CANONICAL CONTENT FIELD) TO EACH INDIVIDUAL FRAME ALONG THE TIME AXIS. GIVEN A TARGET VIDEO, THESE TWO FIELDS ARE JOINTLY OPTIMIZED TO RECONSTRUCT IT THROUGH A CAREFULLY TAILORED RENDERING PIPELINE. WE ADVISEDLY INTRODUCE SOME REGULARIZATIONS INTO THE OPTIMIZATION PROCESS, URGING THE CANONICAL CONTENT FIELD TO INHERIT SEMANTICS (E. G., THE OBJECT SHAPE) FROM THE VIDEO. WITH SUCH A DESIGN, F NATURALLY SUPPORTS LIFTING IMAGE ALGORITHMS FOR VIDEO PROCESSING, IN THE SENSE THAT ONE CAN APPLY AN IMAGE ALGORITHM TO THE CANONICAL IMAGE AND EFFORTLESSLY PROPAGATE THE OUTCOMES TO THE ENTIRE VIDEO WITH THE AID OF THE TEMPORAL DEFORMATION FIELD. WE EXPERIMENTALLY SHOW THAT F IS ABLE TO LIFT IMAGE-TO-IMAGE TRANSLATION TO VIDEO-TO-VIDEO TRANSLATION AND LIFT KEYPOINT DETECTION TO KEYPOINT TRACKING WITHOUT ANY TRAINING. MORE IMPORTANTLY, THANKS TO OUR LIFTING STRATEGY THAT DEPLOYS THE ALGORITHMS ON ONLY ONE IMAGE, WE ACHIEVE SUPERIOR CROSS-FRAME CONSISTENCY IN PROCESSED VIDEOS COMPARED TO EXISTING VIDEO-TO-VIDEO TRANSLATION APPROACHES, AND EVEN MANAGE TO TRACK NON-RIGID OBJECTS LIKE WATER AND SMOG. PROJECT PAGE CAN BE FOUND AT IMAGE-TO-IMAGE TRANSLATION KEYPOINT DETECTION +1

IN THIS WORK, WE PROPOSE A NOVEL TRAINING MECHANISM TERMED SEGPROMPT THAT USES CATEGORY INFORMATION TO IMPROVE THE MODEL'S CLASS-AGNOSTIC SEGMENTATION ABILITY FOR BOTH KNOWN AND UNKNOWN CATEGORIES.

MESH IS EXTRACTED FROM THE SIGNED DISTANCE FUNCTION (SDF) NETWORK FOR THE SURFACE, AND COLOR FOR EACH SURFACE VERTEX IS DRAWN FROM THE GLOBAL COLOR NETWORK.

WE INTRODUCE MUAVIC, A MULTILINGUAL AUDIO-VISUAL CORPUS FOR ROBUST SPEECH RECOGNITION AND ROBUST SPEECH-TO-TEXT TRANSLATION PROVIDING 1200 S OF AUDIO-VISUAL SPEECH IN 9 LANGUAGES.