PYTHON

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.

FROM THE APPLICATION POINT OF VIEW, A USER NEEDS TO GENERATE THE MASKS FOR THE OBJECTS THEY WOULD LIKE TO REMOVE WHICH CAN BE TIME-CONSUMING AND PRONE TO ERRORS.

WE PRESENT A FAMILY OF FINE-TUNED AND MERGED LARGE LANGUAGE MODELS (LLMS) THAT ACHIEVES THE STRONGEST PERFORMANCE AND CURRENTLY STANDS AT FIRST PLACE IN HUGGINGFACE'S OPEN LLM LEADERBOARD AS OF THE RELEASE DATE OF THIS WORK.

TO ADDRESS THIS ISSUE, WE PROPOSE A GENERIC AND LIGHTWEIGHT CONTROLLABLE KNOWLEDGE RE-INJECTION MODULE, WHICH UTILIZES THE SOPHISTICATED REASONING ABILITY OF LLMS TO CONTROL THE VPG TO CONDITIONALLY EXTRACT INSTRUCTION-SPECIFIC VISUAL INFORMATION AND RE-INJECT IT INTO THE LLM.

THE TOOL PERFORMS SIGNIFICANTLY BETTER THAN OTHER OPEN-SOURCE SOLUTIONS IN THIS EVALUATION AND IN EXTERNAL BENCHMARKS.

3D VISION-LANGUAGE GROUNDING (3D-VL) IS AN EMERGING FIELD THAT AIMS TO CONNECT THE 3D PHYSICAL WORLD WITH NATURAL LANGUAGE, WHICH IS CRUCIAL FOR ACHIEVING EMBODIED INTELLIGENCE.

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.

RECENTLY, REMARKABLE PROGRESS HAS BEEN MADE IN AUTOMATED TASK-SOLVING THROUGH THE USE OF MULTI-AGENT DRIVEN BY LARGE LANGUAGE MODELS (LLMS).

RADIANCE FIELD METHODS HAVE RECENTLY REVOLUTIONIZED NOVEL-VIEW SYNTHESIS OF SCENES CAPTURED WITH MULTIPLE PHOTOS OR VIDEOS.

LARGE PRE-TRAINED GENERATION MODELS, SUCH AS OPENAI X, CAN GENERATE SYNTAX- AND FUNCTION-CORRECT , MAKING THE CODING OF PROGRAMMERS MORE PRODUCTIVE AND OUR PURSUIT OF ARTIFICIAL GENERAL INTELLIGENCE CLOSER.

TO SHOWCASE THE POWER OF THE PROPOSED APPROACH, WE GENERATE DATASETS WITH RICH DENSE PIXEL-WISE LABELS FOR A WIDE RANGE OF DOWNSTREAM TASKS, INCLUDING SEMANTIC SEGMENTATION, INSTANCE SEGMENTATION, AND DEPTH ESTIMATION.

WE PROPOSE A NOVEL FRAMEWORK CIPHERCHAT TO SYSTEMATICALLY EXAMINE THE GENERALIZABILITY OF SAFETY ALIGNMENT TO NON-NATURAL LANGUAGES -- CIPHERS.

WE FIRST PRESENT TOOLBENCH, AN INSTRUCTION-TUNING DATASET FOR TOOL USE, WHICH IS CREATED AUTOMATICALLY USING CHATGPT.

MOREOVER, OUR MODEL EVEN OUTPERFORMS THE LARGEST CLOSED LLMS, ANTHROPIC'S CLAUDE AND GOOGLE'S BARD, ON HUMANEVAL AND HUMANEVAL+.

HOWEVER, RLHF IS A COMPLEX AND OFTEN UNSTABLE PROCEDURE, FIRST FITTING A REWARD MODEL THAT REFLECTS THE HUMAN PREFERENCES, AND THEN FINE-TUNING THE LARGE UNSUPERVISED LM USING REINFORCEMENT LEARNING TO MAXIMIZE THIS ESTIMATED REWARD WITHOUT DRIFTING TOO FAR FROM THE ORIGINAL MODEL.

WE PRESENT A NEAR REAL-TIME METHOD FOR 6-DOF TRACKING OF AN UNKNOWN OBJECT FROM A MONOCULAR RGBD VIDEO SEQUENCE, WHILE SIMULTANEOUSLY PERFORMING NEURAL 3D RECONSTRUCTION OF THE OBJECT.